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Ciente’s Picks of Underrated Fintech Marketing Campaigns

Author: Ciente Editorial Team

Ciente's Picks of Underrated Fintech Marketing Campaigns
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Ciente’s Picks of Underrated Fintech Marketing Campaigns

Ciente’s Picks of Underrated Fintech Marketing Campaigns

Fintech seems like it’s all technology. But it’s about imagination that transcends this limited perception. And here are fintech marketing campaigns that prove it.

At its core, fintech marketing is like any other industry-specific marketing. The tech complexity and nitty-gritty in fintech oscillate. But the shell framework remains the same.

What’s true for traditional financial institutions is true for fintech- reliability, trust, and credibility. These are essential requirements for even the earliest adopters. Because the to-and-fro of money isn’t mundane.

This automatically makes fintech marketing not about marketing a fintech, but about meeting your customers where they want you to. And transparency isn’t just a marketing trend.

Traditional financial institutions are going to catch up. Digital adoption is becoming imperative. Not a nice-to-have, but a must-have. Transparency or any other elements mentioned above can’t be used as value propositions by fintechs for the long term.

But fintech isn’t done. The revolution hasn’t ended. And that’s the sparkle.

However, this isn’t being leveraged correctly. There’s a dissonance because most customers belong to a non-financial background. They end up feeling disconnected from the brand’s vision. And overexplaining only makes them feel unintelligent. It’s challenging to connect with the audience because, honestly, no one gets up in the morning to feel enthusiastic about balancing their checkbooks. It’s tedious and not all that entertaining. But the demand’s there because it’s imperative to our living conditions.

How do you make your audience feel excited about something so acutely banal?

You build a truly innovative fintech marketing campaign.

You primarily focus on the different stages of your buyer’s journey, and design your fintech marketing campaign around that. similar to how a full-funnel marketing campaign aligns messaging across awareness, consideration, and decision stages.

The Three Growth Stages of an Enterprise Fintech Campaign

Enterprise fintech campaigns fail because a great idea gets deployed at the exact wrong stage of the buyer’s journey. Or worse, deployed like that, the journey doesn’t even exist.

In this space, there are three distinct levels of campaign maturity. Each has a highly specific job to do, and blurring the lines between them is exactly where your marketing budget quietly vanishes.

Level 1: Presence, i.e., Getting on the Radar Before the RFP Hits

In B2B fintech, the shortlist is largely written before a formal evaluation even starts. Decision-makers carry a mental map of the vendor landscape built from analyst briefings, industry chatter, and peer conversations.

If you aren’t on that map, no amount of hyper-targeted, bottom-of-funnel ads will save you.

Level 1 isn’t about generating leads; it’s about generating presence. something often overlooked in traditional lead generation campaigns that focus heavily on immediate conversions.

Nuvei nailed this.

They knew their target enterprise accounts were structurally immune to standard SDR cold-calling. Their Ryan Reynolds campaign wasn’t just a vanity play for brand awareness- it was a calculated strike to get onto the mental shortlists that cold outreach couldn’t penetrate. At this stage, you measure success through share of voice, brand recall, and the caliber of inbound conversations.

Level 2: Relevance, i.e., Winning Over a Skeptical Buying Committee

Once they know who you are, your campaign’s job pivots from presence to relevance. That is where most enterprise brands trip up. They’ll build presence with a bold, distinct campaign, but the second a buyer enters evaluation mode, they abruptly default to dry product sheets and ROI calculators.

That whiplash is jarring, and it tells the buyer you don’t actually understand their day-to-day reality.

Think: you have to speak to the people who feel historically ignored by financial marketing. In an enterprise, that’s your IT Director or the skeptical Operations lead. If you only pitch the CFO, you ignore the exact people who will quietly kill your deal.

Level 2 campaigns earn relevance by engaging the entire committee. An approach closely aligned with ABM strategies for fintech startups using AI and predictive analytics, where messaging is tailored to multiple stakeholders. That means running different messages across different channels simultaneously, solving specific headaches for specific stakeholders.

Level 3: Conviction, i.e., Unseating the “Good Enough” Incumbent

This is the stage most campaigns miss entirely.

By Level 3, the buyer already believes your product is superior. The barrier isn’t awareness or even preference- it’s the sheer dread of switching.

Enterprise buyers have learned to tolerate clunky legacy systems because the pain of change (migration nightmares, retraining costs, burning political capital) feels heavier than the promise of a better platform.

Here, you don’t need to sell harder; you need to de-risk the decision. something that can be strengthened by leveraging buyer intent data in ABM campaigns to understand readiness and hesitation signals. You need case studies from equally complex organizations, clear migration roadmaps that make the switch feel seamless, and peer validation that makes choosing you a safe, defensible bet.

Just like Monzo leverages consumer “love” to drive adoption, enterprise brands need reference customers, third-party audits, and implementation guarantees. It is the campaign content that turns a buyer who wants to switch into one who do.

It’s nothing new. There are brands out there that have mastered the art of fintech marketing campaigns. much like other successful SaaS marketing campaigns that broke through crowded markets.

And that’s precisely what we’re here to talk about. Fintech marketing campaigns that broke through the tradition with their bold moments. And made an impression on the industry.

Fintech Marketing Campaigns: Moments that Broke Through the Humdrum Routine of the Fintech World

Catching and engaging the attention of your ICPs is not a simple feat. which is why studying examples of great ABM campaigns can offer practical direction on what resonates with high-value accounts. Especially when it’s something so monotonous- you must be tactical enough not to rub them inappropriately. Or create more problems when you wanted to solve one.

What does that require? Positioning yourself as a credible and trustworthy fintech brand. something that strong B2B branding campaigns consistently reinforce across touchpoints. You can’t mix and match random marketing strategies and expect them to work. Fintech demands finesse and insight. It requires strategy and creativity along with the statistics and features.

We have five fintech marketing campaigns to inspire you. And to help you break through the insipidness of marketing such crucial tech-centric solutions.

1. Nuvei x Ryan Reynolds

Nuvei is a payments powerhouse based in Canada. It has an impenetrable audience base of large enterprise merchants- it’s very challenging to reach these accounts. But without a way to reach its potential customers, Nuvei knew it would lose its market positioning.

The company had to find a way out of this conundrum. And the solution was quite unorthodox.

Nuvei stepped into influencer marketing.

Yes, it seems ambitious for a fintech company. But the point is that it actually worked. Nuvei, a Canadian platform, would now have to choose an influencer who would be its poster child and propagate its brand story through the most impactful means.

It chose a Hollywood A-lister: Ryan Reynolds. Known for his humour and quirky demeanor. Actually, it was Reynolds who invested in his homebound brand. As part of the deal, he became part of Nuvei’s ads.

image 1

Source: Nuvei

These ads became the talk of the fintech world. Because of its unique intersection of a B2B brand like Nuvei and Reynolds’ B2C storytelling method. He had complete control over the ads and the content, which allowed him to display his humor.

The impact?

“Huge. Especially on brand awareness,” shares Alexandra Bucur, Nuvei’s Head of Content Marketing.

They could reach publications that were difficult to receive unless you paid. SDRs’ job became a easier because now they could use Reynolds as the opener. And they had millions of views, that’s impossible without paid ad channels.

It didn’t come down to leads and sales for Nuvei. But the awareness that it brought? That had long-term effects on Nuvei’s brand positioning.

Maybe it’s not always about the numbers. But about creating memorable impact.

Nuvei made an unconventional call by borrowing a rather B2C tactic. But their logic was flawless- they knew that the business was struggling to get SDRs through the door. Engaging hard-to-reach enterprise accounts was challenging. Nuvei knew it was facing an awareness pain.

Unlike other B2B brands, it didn’t try to create more whitepapers and thought leadership content. It chose a lever that solved the specific problem of awareness and visibility. Ryan Reynolds gave them that leverage.

2. Your Way In by Revolut

Revolut is as ambitious as it was years ago. It aimed to become the leading digital banking platform globally.

In 2022, Revolut teased a special marketing campaign to “reach” each UK consumer wherever they were at home, outdoors, and online. It sought to meet its potential customers wherever they were. And that was quite a strategy.

Source: YouTube

Your Way In was Revolut’s most significant brand awareness omnichannel campaign. And it made substantial splashes because the brand hadn’t published campaigns across such a scale.

The marketing strategy revolved around a unique message: financial inclusion. It spoke directly to the financial underdogs, not experts. For example, one of the clips illustrated a woman trading on her phone in a bathroom. And then the wall breaks and collides with a room full of traders.

Revolut challenged financial stereotypes with this campaign. The rooms that were previously too difficult to crack? Cryptocurrencies? Trading? Investing? The most rewarding opportunities were only accessible to some segments.

Revolut wanted to show its audience that it was possible to enter these “closed off worlds of money.”

As ordinary characters (or regular people) crash through these barriers, the digital banking services platform illustrates them bursting through walls of financial arenas and through challenging financial situations by leveraging the Revolut app.

This campaign worked because the time was right. As the cost of living surges, ordinary people want more channels to gain confidence, financial advantages, and financial freedom.

Revolut understood the timing. And it delivered the campaign, full of relevance. An approach that aligns with broader B2B fintech trends for 2026, shaping customer expectations.

The impact? It resonated because users could enter a world they hadn’t been in before.

Look at what made this fintech marketing campaign truly successful- it included those financially excluded. Omnichannel was merely the means; it wasn’t the real genius. Revolut spotlights a new chasm- with this campaign, it tried changing the mundanity of how the messaging truly feels for end users, i.e., it’s not truly meant for them.

This maps to the enterprise context. Your campaigns also need to target the non-CFO stakeholders they’re actively ignoring.

3. Monzo: “Money Has Never Felt Better.”

The anxiety of money management plagues us all. And Monzo wanted to be relatable.

Its “Money Has Never Felt Better” campaign was a humorous juxtaposition- published across OOH and a 60″ hero film. This campaign focused on two sides of the same coin: the good and the bad.

In a consecutive series of shots, the video illustrates what managing money generally feels like and how it feels with Monzo. It’s creative and built to highlight Monzo’s value proposition. The former is cold, stressful, unsatisfying, and even painful. Meanwhile, using Monzo feels warm, peaceful, and zen.

Source: YouTube

The imagery is commendable.

In one part of the film, money management feels like the middle of your workday, and you keep on banging your head on the keyboard. Meanwhile, with Monzo, it feels like learning Kung Fu to break through a wooden board with your head.

That’s hope. And the power of learning. And that is propagated through a bunch of juxtapositions. Anxiety with celebration. Screaming match with a loving moment. Cold with warmth. Failure with success. The list goes on and on.

The idea is simple. And it requires no further explanation. The campaign delivers Monzo’s message straightforwardly. reinforcing how UX design for fintech plays a key role in shaping emotional and functional user experiences. It’s placing Monzo under a bright light, but also showcasing that it cares about its users’ feelings.

“Leveraging us will dispel the discomfort that you feel in your daily lives.”

The campaign promotes Monzo’s solutions for customers. Not about itself and what it can do for them. Monzo’s VP of Marketing puts it quite simply- “

Across the country, money evokes a variety of feelings, usually stress, anxiety, and avoidance. However, our customers tell us that on Monzo, money feels different, so much so that they’re seven times more likely to use the word ‘love’ when describing us than any other bank.”

The Gross Error Fintech Marketing Campaigns are Making

You learn specific things from these three fintech marketing campaigns.

The messaging comprises a similar authoritative and informative tone across all channels- even if the customers at the end of the day are humans, i.e., those who feel and partake in critical thinking.

But fintech companies must grasp that not all buyers are CFOs. CFOs, Controllers, and Treasurers have financial literacy. But they’re just one segment of the buying committee. Most of them are motivated by financial business and operational needs. IT Directors, Ops, or even end users don’t entail in-depth financial knowledge.

Talking in terms that only make sense to your own brand can drive your potential customers away.

Most buying decisions is meant for problem-solving. It’s not about having the financial or technical acumen. Even fintech companies orchestrate solutions with non-financial users in mind. It’s not about who the buyer is, but the end users who will leverage the solutions down the line.

That’s why fintech requires storytelling. moving beyond tactics into crafting memorable marketing campaigns beyond mindshare that truly connect with audiences. Clarity in what they offer and merely presenting the same information isn’t enough. Your fintech campaigns rely on financial jargon neatly packed with ribbons- “zero fees” or “instant loans.” A pitfall often addressed in a solid fintech ad mastery guide. But these copies only end up feeling spammy.

There’s no real value- A tempting yet bare minimum offer.

Do buyers remember these statistics? They don’t need more reasons to make a purchase. They are scared of the investment that could turn meaningless. Your buyers aren’t simply confident in their decision. And that’s what you need to help them weather this dilemma.

They need reasons they shouldn’t hesitate. Leaning into the uncertain is scary. How do they know if these solutions will reap rewards? They don’t. But they need to move past the hesitation. And feel safe in the decision they’re making. And that’s a problem that the market is struggling with, not merely fintech.

Fintech Marketing Campaigns: What They Should Be

In a far-fetched scenario, your competitors have it all figured out. The top to bottom of fintech marketing. It’s all about presenting valuable information for them.

The same message circulates in the industry like a single meme. You laugh at it again and again until it loses its essence. That’s what happens with marketing messages. Your fintech company requires its own unique storytelling to penetrate the complexity and oversimplification.

Beyond the financial tidbits, you must humanize your brand. The brand-building front isn’t the maturity of your marketing operations aligning with market structures. It’s merely a single part. In fintech marketing, the emotional segment is always neglected, even though thought leadership for fintech often emphasizes human-centric storytelling as a differentiator. because, honestly, which business leader needs emotional gibberish?

That’s incorrect.

If you don’t instill storytelling across campaigns, you lose your customers. Product differentiation in fintech is at an all-time low. And you need a truly disruptive product to stand out. There’s only an incremental or marginal difference in solutions, such as Stripe v/s PayPal. Fintech’s true worth gets lost in all of the noise.

Your fintech marketing campaigns should revolve around empowerment- that’s what this digital transformation trickles down to.

Traditional financial institutions have neglected financial inclusion for the longest time. Especially in the domain of asset and wealth management. It’s time for fintech to change that. And that should reflect in their messaging.

The Core Pillars of a B2B Fintech Marketing Campaign

Simply mentioning a product doesn’t offer a clear notion of what buyers truly expect from marketing campaigns, especially in fintech. The solutions themselves are so complex- and with an increasing dissonance in marketing comms, it’s not easy for fintech solutions to build mindshare quickly and with impact.

The market is crowded- every fintech marketer realizes that. However, reports assert that new contenders pop up even with tight funding. These newcomers influence investor interests towards something new, changing the industry’s momentum.  

This is the market problem. Adding on to it is the buyer’s problem. Buyers are changing almost disproportionately with the market, so businesses must change how they market to them, riding the waves for their own sakes as well.

For this, we must question- to what extent is the tech, i.e., the solution, the focal point? If we must talk about marketing, we obviously spotlight the solution launches in perspective, but it doesn’t negate why some campaigns succeed, and some don’t. Because B2B fintech is an entirely different beast to tackle.

So, if we’re building some core pillars for B2B fintech marketing campaigns, the requirements change. What might those be?

  1. The buying reality
  2. Content that the buying committee resonates with
  3. A “good” campaign for an enterprise setting
  4. What has changed more- the buyer or the market?
  5. The priorities

And then there are the core pillars:

1. Institutional Trust

Enterprise fintech buyers don’t approach your brand by evaluating whether they like it. Even if they do, the question is whether they can defend the decision confidently.

The distinction is often framed around the campaign messaging. B2C fintech brands build trust through relatability- warm tone, funny ad, and a relevant moment. Meanwhile, enterprise fintech brands must earn trust through institutional credibility. You name it- from compliance posture to integration depth, it’s the tooling and infrastructure that’s at the crux of all fintech worries right now.

Storytelling still makes a difference- but in how these campaigns make a risk-averse buyer feel confident enough to progress further.

Every campaign asset must ask: Does this reduce the buyer’s perceived risk?

2. Committee Fluency

Individuals never decide B2B fintech purchases. The buying committee today spans a CFO evaluating ROI to an Operations leader focused on implementation timelines. And it’s the end users who ultimately decide on the tech’s adoption.

Almost every fintech campaign dilutes all these individual voices and writes exclusively for just one. It’s usually the CFO because the budget is in their hands. But today’s fintech marketing campaigns must diverge. They must focus on these different voices and each of their pain points. It’s not just the right account, the right message at the right time, but also the right stakeholder.

Marketing teams must become more proactive in understanding each stakeholder’s objection and address it before blocking the deal in the conversion stage.

3. Applying Playbooks with Intention

The consumer fintech wins in this blog aren’t standard enterprise plays. Yet, they offer vital enterprise lessons.

Revolut realized their buying committee wasn’t just financial pros; it included the excluded and the hesitant who felt the system wasn’t built for them. In an enterprise, the parallel is that non-financial stakeholders- the IT Director tasked with a platform they didn’t ask for, or the Ops lead skeptical of another integration. A campaign speaking only to “financial authority” misses the people who can quietly kill a deal.

Nuvei’s Reynolds campaign is the ultimate example of deliberate borrowing: a B2B brand that diagnosed a structural awareness gap and used a B2C tactic- not because it was trendy, but because the conventional approach wasn’t solving it.

4. Coherence

Enterprise fintech deals don’t wrap up in a quarter. They close over months or years, navigating multiple stakeholders and shifting internal budget cycles. That is where most fintech campaigns structurally fail- they’re built for short-term noise, not long-cycle coherence.

A message that bounces between a product launch, a thought leadership push, and a demand gen sprint mimics chaos to a buyer tracking you across a twelve-month procurement process.

The campaigns that win in enterprise are those where a buyer encounters the brand at month one and ten and leaves with the same clear, consistent understanding of your goals. That coherence isn’t just a creative luxury. it’s also measurable through the right ABM metrics to measure your campaign success.

In the world of enterprise fintech, it is a strict competitive requirement.

Marketing Campaigns as a Growth Lever for Fintech.

The Real Challenge is Surviving Internal Dilution

The biggest trap in enterprise fintech marketing isn’t a lack of know-how. It’s the internal tug-of-war. Product teams, legal, and leadership all drag the campaign toward their own agendas, often impacting execution across channels like optimizing ad campaigns with Google Display Ads. resulting in Frankenstein content that breezes through internal approvals but falls completely flat in the market.

Marketers often ship jargon-heavy features simply because it’s what gets signed off.

The core problem is having the organizational courage to build campaigns for the actual buyer at the right level, without letting internal politics water down the message. And no campaign framework can solve that on its own.

For most investors and newcomers, fintech remains a sparkling diamond. When combined with the promise of digital transformation, the spark becomes brighter. And that’s what fintech is trying to master now- A balance.

In the midst of chasing the tech fever, this up-and-coming industry has forgotten a crucial aspect- storytelling. Storytelling with emotions. Fintechs might be great at underlining the how, but they aren’t that good at explaining the why.

Underneath all the layers of security, complex features, and algorithms, the story loses its meaning.

Numbers are easy to explain. But this has erased the human story. Fintech must bring this back.

Because numbers may stale. But human experiences and their stories don’t.

Mapping B2B Content
Blogs

Mapping B2B Content to Each Stage of the Funnel- But with a Twist

Mapping B2B Content to Each Stage of the Funnel- But with a Twist

Buyers are completing 70% of their research before talking to you. The content that wins is not the content that answers their questions. It is the content that answers the questions they have not formed yet.

The funnel model of content mapping is comfortable.

Awareness content at the top. Consideration content in the middle. Decision content at the bottom. Map each piece to a stage. Measure progression. Optimize. Track performance through defined content marketing KPIs rather than rigid funnel stages.

It is a clean framework built for a buyer behavior that no longer exists.

The modern B2B buyer does not announce their stage. They do not move through your funnel in the sequence you designed. They research privately, form opinions before they talk to anyone, and arrive at the first conversation already knowing things about your category, your competitors, and often your product that you did not know they knew. By the time they are visible to your sales team, 70% of the journey is done.

The content that influenced that journey, or failed to, was encountered in those dark months. In searches you did not know were happening. In conversations you were not part of. In comparison, pieces you could not see.

Content mapping in this environment is not about assigning assets to funnel stages. It’s about building a connected content ecosystem that surfaces when buyers search. It is about having something worth finding when a buyer’s research runs into the problem you solve.

The Truth About B2B Buyer Behavior

There is an uncomfortable truth at the center of this conversation.

You do not know what your buyers are searching for when they are in active research mode. You know what you think they are searching for. You know what your SEO tools tell you they search for. You know what they tell you in discovery calls.

None of these is the same as what they actually type into a search bar, something modern content performance marketing tries to decode more effectively. at 11 pm, when they are trying to understand whether the problem they have been ignoring is as serious as they suspect.

The dark research problem is not a data gap you can close with better analytics. It is a fundamental feature of how serious B2B buyers operate. They do not want to talk to vendors while they are still trying to understand the problem. They want to think through it on their own terms, using sources that feel neutral enough to trust. They come to vendor conversations with conclusions already forming.

The question content mapping should be answering is not which stage this buyer is in, but how content aligns with the real buyer’s journey. It is: what are the real questions they are asking that they would never ask a vendor, and can we be the source they find when they ask them?

Understanding Your B2B Buyers for Strategic Content Mapping

What they search and actually type

Steve Jobs was not a market researcher in the conventional sense. His argument against customer research was not that customer insight does not matter. It was that customers describe the constraints of their current situation, not the possibilities beyond it.

Asked what they wanted before the iPhone, people described better phones. Faster, better cameras, longer battery life. Nobody described the category collapse that was coming: a device that made the phone the least interesting thing it could do.

The insight that made Apple’s product strategy work was anticipatory. Not what do customers want, but what would they want if they understood what was possible? What problem are they tolerating right now that they have accepted as permanent that does not have to be?

Content teams applying this thinking stop asking what questions buyers are asking and instead rely on structured content classification to map deeper intent. and start asking what questions they should be asking. What does the buyer know about their incomplete problem? What assumption are they carrying that will cost them if they do not examine it? What is the category conversation missing that would change how they think about the decision?

The content that answers this is not built from keyword research. It is built from a deep and honest understanding of what it feels like to have the problem your product solves, before someone knows that a solution exists, before they have the language to search for it precisely.

This is old. It is what good editorial has always done. It is what the best trade publications built their authority on for decades. The insight arrived before the reader knew they needed it, and because it arrived that way, the publication became the place they returned to when the need became concrete.

The problem with ROI calculators and interactive tools

ROI calculators are useful at one specific moment, and measuring their impact requires a clear understanding of content marketing ROI. When a buyer already believes in the solution and needs to justify it internally. They are a closing tool dressed up as a discovery tool.

The organization that leads with an ROI calculator is telling the buyer something about how they see the relationship. You calculate the return on our product. The implicit message is that the decision is about numbers, and the product’s job is to win on the numbers.

For a buyer who does not yet know whether they have a problem worth solving, this is the wrong conversation entirely. They are not at the calculator stage. They are at the what is this, why does it matter, should I care stage, and the ROI calculator does not meet them there.

Interactive content, webinars, benchmarks, and comparison guides are valuable formats, but they are only one part of broader content marketing strategies that drive results in B2B. They are useful to buyers who are already in active evaluation. They do not help buyers understand whether an evaluation is warranted.

The old schoolbook of communicating a problem works because it addresses a prior need. Before anyone can evaluate a solution, they need to recognize a problem. Before they can recognize a problem, they need a framework for understanding their situation clearly enough to notice that something is wrong.

Content that communicates problems well does not describe problems generically. It describes specific symptoms in specific contexts in enough detail that the reader stops and thinks: “This is exactly what we are dealing with, and I did not have language for it until just now.”

That moment of recognition is worth more than any ROI calculation, because it creates the question. The calculator only answers questions that already exist.

Mapping content to what buyers actually experience, not what the funnel says they experience

Content jobs for the real journey not the funnel

Here is what a real buyer journey looks like in a complex B2B purchase.

Something happens that makes a problem impossible to ignore any longer. A product fails at a critical moment. A new leader arrives and asks a question nobody can answer. A competitor does something that makes an existing approach look inadequate. A budget cycle opens up and a long-deferred problem finally has space to be addressed.

The buyer does not think: I am now entering the awareness stage of a purchase journey. They think: we need to figure this out.

They start researching. They read whatever they can find that seems credible and disinterested. They talk to people in their network who have faced similar situations. They form a rough sense of what the solution space looks like and which approaches seem legitimate. They develop opinions about vendors without talking to any of them.

At some point, weeks or months in, they surface. They fill out a form. They respond to an outreach. They show up at an event.

The content mapping exercise that most teams do assigns content to stages instead of aligning with evolving B2B content marketing trends. of a funnel that do not match this journey. The awareness content tries to create awareness that the buyer has already passed. The consideration content describes the evaluation criteria that the buyer has already developed independently. The decision content argues for a choice the buyer is already close to making.

The mapping that actually serves this buyer is built around the moments in the real journey. The moment of recognition when the problem becomes impossible to ignore. The private research phase when they are trying to understand the landscape before anyone can sell to them. The moment of comparison when they are trying to distinguish between approaches that all claim to solve the same thing. The internal justification phase when they need to convince people who were not part of their research.

Each of these moments has a content job. And the jobs look very different from awareness, consideration, and decision.

The content mapping that works in the dark

Buyers research privately because they do not trust vendor content to be honest about limitations, trade-offs, and failure modes. They go to communities, independent publications, peer networks, and anything that feels like it was not written to sell them something.

This creates a counterintuitive implication for content strategy. especially when scaling distribution through content syndication for lead generation. The content most likely to influence private research is the content that does not try to sell. The case study describes what went wrong and what the team had to change. The analysis acknowledges where the approach does not work. The framework that helps the buyer think about their problem in a way that makes the right category obvious, even if that category is not always yours.

This is not altruism. It is a calculated understanding of what earns trust in an environment where trust is scarce.

The organization that publishes genuinely useful analysis often builds authority through strong content marketing case studies. of a market problem, without packaging it as a product pitch, builds a different kind of authority than the organization that publishes polished content about how their solution is the best one. The reader knows the difference. The reader is always the one who decides.

B2B buyers increasingly want to be treated as intelligent adults who are capable of reaching their own conclusions. Content that respects that capacity, that gives them the raw material to think rather than the conclusion to accept, is the content that gets shared internally, that gets bookmarked, that gets forwarded to the colleague who is now on the buying committee.

The fundamentals of editorial content have not changed. Communicate a problem honestly. Give the reader something they can use to understand their situation better. Let them make the connection to why it matters to them. The connection they make themselves is stronger than the one you made for them.

How teams should actually adapt to the dark journey

The practical implication is a different kind of content planning process.

Start with the problem, not the product. What is the hardest, most specific version of the problem your product solves? Not the generic version in the category description. The version that a buyer who has been living with it would recognize immediately as true. That specificity is what breaks through in private research.

Map the questions that precede the questions your content currently answers. Your current content probably answers: what does this product do, how does it compare, and what is the ROI. Before those questions come: do we have this problem, is it serious enough to address, what are the approaches have organizations like us tried, what went wrong with those approaches, and how do we know if we are ready to make this kind of change?

Build content for those questions.

Treat content as institutional knowledge, supported by a scalable content supply chain that captures and distributes insights consistently. not as campaign output. The best editorial in any B2B category is built by organizations that have learned things about the problem that nobody else has documented. The insight that comes from working with hundreds of customers on the same problem, from watching what works and what fails, from developing a perspective on the market that is grounded in observation rather than aspiration. That knowledge is the raw material of content that does not get ignored.

Measure what happens in the dark using deeper content performance metrics beyond surface-level attribution. Attribution for content that influences private research will always be imperfect. But closing the gap matters. First-touch attribution undercredits content. Last-touch ignores everything that built the relationship before the form fill. Asking buyers in discovery calls what they read, where they researched, what they found most useful before they ever talked to anyone — this is qualitative intelligence that no dashboard provides but that tells you which content is doing real work.

A formula that has not changed

Experience and growth.

The buyer is trying to grow something. Revenue, capability, market position, and organizational health. They are experiencing a problem that is in the way.

Content that understands both sides of that equation, that earns the right to speak to the experience before it talks about the growth, is the content that gets read when nobody is looking.

The organizations that figure this out are not the ones with the best keyword strategy, but those adapting to emerging content marketing trends in 2026. or the most sophisticated content operations. They are the ones who understood their buyers well enough to answer questions those buyers had not yet learned to ask.

That is the content mapping problem worth solving.

Anthropic's
News

Anthropic’s “Safety” Play Includes Sitting Down with the EU

Anthropic’s “Safety” Play Includes Sitting Down with the EU

Anthropic is writing the rules of AI security. But are they protecting the world from hackers, or just their market share from competition?

Anthropic isn’t in Brussels for a standard policy chat. They are meeting with the EU Commission to discuss their new, restricted cybersecurity model called Claude Mythos.

That isn’t your average chatbot- it’s essentially a professional-grade hacker in a box. In early tests, it found security flaws that had been hiding for 27 years. It’s so good at finding exploits that Anthropic has locked it behind a heavy door, only letting a “private club” of tech giants like Google and NVIDIA play with it under a project called Glasswing.

But here’s where it gets interesting. Anthropic is basically telling the EU- “Look how dangerous our tech is, so please regulate us.”

On the surface, it sounds like corporate responsibility, but it’s actually a brilliant, high-stakes power play. If Anthropic can convince the EU that cybersecurity AI is a “systemic risk” requiring massive oversight, they effectively build a $100 billion moat.

A small startup in Berlin or Paris won’t have the legal budget to jump through the hoops Anthropic is volunteering for. It’s a classic case of regulatory capture- setting the rules of the game so that only the biggest players can even afford to get on the field.

That is as much about business as it is about safety.

By framing their model as a restricted asset, Anthropic is positioning itself as the trusted gatekeeper for the West. Maybe the only way to stay safe is to let a US-based startup hold the keys to the continent’s digital locks. It’s a masterclass in diplomacy, but it forces a tough question- are we just handing control of our digital infrastructure to a private company?

If the EU bites, they might be signing over their digital sovereignty in the name of safety.

Data Management Platform
Blogs

8 Data Management Platform Alternatives Beyond Governance

8 Data Management Platform Alternatives Beyond Governance

Every data management alternative list covers the same ground: better governance, cleaner pipelines, smarter catalogs. This one covers those, then goes somewhere else entirely.

The governance conversation is settled.

Everyone agrees you need data catalogs, lineage tracking, access controls, quality pipelines, and compliance frameworks as part of building a modern data stack. The tools exist. The best practices are documented. If your organization is still arguing about whether to implement governance, that is a different problem, and this piece is not for you.

This piece is for the organizations that have the governance layer and are asking what comes next in building a future-first data foundation. What does data management look like when the AI workloads are real, the energy costs are showing up on finance’s radar, and the systems processing all of this information are starting to look fundamentally different from the systems they were designed to manage?

The first four entries are conventional. Established categories, strong tooling, clear ROI cases. The last four are not. They are where the conversation goes if you are willing to follow it.

The Conventional Four

1. Data Observability Platforms: Real-Time Monitoring for Data Pipeline Health

Governance tells you what data you have and who can access it. Observability tells you whether the data is behaving the way it is supposed to, right now, while it is moving.

The distinction matters because bad data does not always announce itself. A field that is suddenly arriving null at 3 am does not file a ticket. A table that stopped updating when the upstream schema changed does not send an alert unless someone builds one. Most organizations discover data quality problems when a downstream consumer, a report, a model, or a dashboard produces something obviously wrong. By then, the problem has been propagating for hours or days.

Data observability platforms like Monte Carlo, Bigeye, and Acceldata sit in the pipeline watching for anomalies in real time. Volume drops. Schema changes. Distribution shifts. Null rate spikes. Freshness failures. They catch these patterns before they reach the consumer and create an incident trail that makes root cause analysis possible instead of guesswork.

The value here is not just catching problems faster. It is changing the organizational relationship with data reliability through better data hygiene practices. A team that sees its data quality score on a dashboard behaves differently toward data quality than one that only hears about problems in retrospect.

For organizations running AI and ML workloads, this is not optional infrastructure, especially as data science continues to transform business outcomes. A model trained on silently degraded data is worse than a model trained on less data, because it is confident about the wrong things. Observability is the immune system of the data stack.

Data Mesh Architecture: Decentralizing Data Ownership Across Business Domains

The centralized data warehouse model has a scaling problem that most organizations discover after they have already committed to it, particularly when dealing with evolving data lake architectures.

All data flows to one place. One team manages it. Every consumer routes requests through that team. The team becomes a bottleneck. Business domains that need fast access to their own data wait in a queue. The data platform team works constantly and is still blamed for being slow.

Data mesh is the architectural response. The principle: data is owned and managed by the domain that generates it, not by a central platform team. The marketing domain owns its data. The sales domain owns its. Each domain is responsible for making its data available as a product to the rest of the organization, meeting shared standards for quality, discoverability, and access.

The central team shifts from being a data factory to being an infrastructure and standards provider. They build the platform that domains operate on. They define the interoperability rules. They do not build every pipeline.

The organizational requirement is real. Data mesh does not work in companies that are not willing to give business domains genuine accountability for data quality. It requires distributed expertise that many organizations have not built yet. But for enterprises at scale where the centralized model has already shown its ceiling, it is the architecture that reflects how work is actually done rather than how someone hoped it would be organized.

3. Unified Data Lakehouse Platforms: Combining Data Warehouse and Data Lake Capabilities

The data lake was supposed to solve the warehouse problem, but many organizations struggled with implementation and clarity. Store everything, schema on read, no up-front transformation. The reality was that data lakes became what people called data swamps: large volumes of ungoverned, poorly documented data that were technically accessible and practically unusable.

The data warehouse was structured and reliable, but expensive to put everything in and slow to adapt to new use cases.

The lakehouse collapses the distinction. An open table format layer, Apache Iceberg or Delta Lake being the dominant implementations, sits on top of cloud object storage and provides ACID transactions, schema enforcement, time travel, and the kind of reliability that warehouses offered without requiring all data to live in a proprietary format.

Platforms like Databricks, Apache Hudi, and the cloud-native implementations from AWS and Google have matured enough that the lakehouse is no longer an architecture conversation. It is a procurement decision.

The practical advantage for organizations running diverse workloads is significant, especially when data analytics drives informed decision-making across teams. Data scientists working in notebooks, analytics engineers building dbt models, ML engineers training models, and BI teams running dashboards can all operate against the same storage layer with the same data. The transformation layers and tooling differ. The underlying data does not.

4. Master Data Management Tools: Creating a Single Source of Truth for Critical Business Data

Revenue numbers do not match between sales and finance. Customer records that exist in three systems with three slightly different spellings of the same name. Product data that contradicts itself between the catalog and the ERP.

These are Master Data Management problems often rooted in inconsistent customer data across systems. And they predate cloud computing, AI, and every architectural trend of the last decade. They are organizational problems wearing a technological face.

MDM tools like Informatica, Semarchy, and Reltio provide the infrastructure to define, manage, and synchronize the data that the rest of the organization depends on being accurate and consistent: customers, products, suppliers, locations, and employees. The canonical records that every other system should be reading from, rather than maintaining its own version of.

The implementation challenge is always political before it is technical. MDM requires someone to own each data domain, to make decisions about which source is authoritative when sources conflict, and to maintain that standard over time as systems change and new data sources get added.

Organizations that get MDM right do not usually talk about it as an MDM project. They talk about it as the moment their organization stopped having arguments about which number is correct.

The Unconventional Four

This is where the list departs from what the category expects.

The following entries are not product categories with established vendor landscapes. They are frameworks, architectures, and ideas that reframe what data management means in the AI era. Some of them are emerging. One of them is rewriting the physics of what computing is.

5. Event-Driven Architecture as Data Management: Managing Data in Motion Instead of Data at Rest

The data management conversation almost always assumes data at rest. Data sitting in a warehouse, a lake, a database. Managed, cataloged, governed.

But an increasing share of the most valuable data in any organization is data in motion. Events. The customer clicked. The sensor reading changed. The transaction has been processed. The model produced an output. These events are happening continuously, and they carry information that batch pipelines, by design, capture late and incompletely.

Event-driven architecture treats every meaningful state change in a system as a first-class data artifact, enabling real-time data-driven strategies. Kafka, Pulsar, and the stream processing frameworks built around them create a persistent, replayable log of what happened, when it happened, and in what sequence. The data management layer shifts from managing records to managing events.

This matters for AI in a specific way, particularly as organizations increasingly rely on data-driven marketing trends to stay competitive. Models making real-time decisions need real-time context. A fraud detection model needs to know what happened two seconds ago, not what was in last night’s batch load. A recommendation system operating at the moment of customer decision needs the signal from that session, not the aggregated profile from last week.

The organizations building serious AI capabilities are increasingly discovering that their data architecture is the bottleneck, not their models. Event-driven architecture is the infrastructure answer to that problem.

The governance challenge is real. Events are harder to catalog and query than records. Schemas evolve faster. The volume is higher. But managing data as events rather than as tables is a more accurate model of how information is actually generated and how AI systems actually consume it.

6. Data Contract Frameworks: Enforcing Data Quality at the Source Through Producer-Consumer Agreements

Data quality is usually managed downstream, which often leads to inefficiencies in analytics processes. A pipeline runs, data arrives, a quality check catches the problem, and someone gets paged.

Data contracts invert this. A data contract is a formal agreement between the team producing data and the teams consuming it, specifying the schema, the quality standards, the SLAs, and the expectations on both sides. Before data moves, both parties have agreed on what the data will look like and what happens when it does not.

This sounds administrative. It is actually architectural.

When producers own quality at the source, the entire quality management burden does not fall on the platform team or the consumers. Problems get caught where they are cheapest to fix, before the bad data has traveled through five systems and is now embedded in a production model.

The practical implementation looks like versioned schema definitions, automated contract testing in CI/CD pipelines, and monitoring that alerts the producer when their data violates the contract their consumers depend on. Tools like Soda, Great Expectations, and dbt tests are the infrastructure. The discipline is organizational.

For AI specifically, data contracts are the mechanism that makes model retraining reliable and supports consistent data-driven outcomes. If the training data is governed by a contract, changes to the underlying data are surfaced as contract violations before they silently change model behavior. This is not a governance concern. It is a model reliability concern.

7. Hardware as Data Infrastructure: Why the Physical Layer of AI Computing Is a Data Management Problem

This is the entry that the conventional list skips entirely because it does not look like a data management problem from the outside. It is.

Every AI workload is a data problem at two levels simultaneously. The logical level: what data is being processed, how it is structured, where it lives, and who can access it. This is what DMP conversations usually address.

And the physical level: how is that data moving through hardware, at what energy cost, and what does the architecture of the hardware itself do to the efficiency of the computation?

These two levels are not independent. The hardware determines what data operations are efficient, which determines what AI architectures are practical, which determines what data management strategies make sense.

The dominant hardware paradigm for AI has been the GPU. GPUs were designed for graphics rendering, which turned out to share enough mathematical structure with deep learning to make them useful. Not designed for the job. Adapted for it. And the adaptation has a cost: enormous energy consumption, communication-heavy architectures where moving data between memory and compute is the primary energy expense, and deterministic processing that has to simulate probabilistic behavior rather than performing it natively.

GPU-based computing

Managing data in an AI organization without understanding the hardware constraints is like managing a logistics operation without understanding what the trucks can carry, especially when building a scalable data-centric stack. The physical layer shapes every decision above it.

8. Thermodynamic Computing: When Energy and Data Are the Same Management Problem

This is where the frame changes completely.

Extropic, a company building what they call thermodynamic computing hardware, made a bet three years ago: energy would become the limiting factor for AI scaling. They were right.

Almost every new data center today is experiencing difficulties sourcing power. Serving advanced AI models to everyone, all the time, at the scale the industry is imagining, would consume vastly more energy than humanity currently produces. The AI scaling problem is not a software problem or a data problem. It is an energy problem.

Extropic’s answer is hardware built on a fundamentally different principle. Instead of the deterministic computation model that GPUs inherited from graphics rendering, their Thermodynamic Sampling Unit operates probabilistically at the hardware level. It produces samples from probability distributions directly, using the physics of thermal noise as a computational resource rather than fighting against it.

The result, according to their published research, is orders of magnitude less energy per AI workload.

The connection to data management is not metaphorical. It is structural.

Data has thermodynamic properties. Information theory and thermodynamics are mathematically related in ways that physicists have understood for decades, but that the computing industry largely set aside when deterministic silicon became dominant. Claude Shannon’s measure of information entropy and Ludwig Boltzmann’s measure of physical entropy share the same mathematical form. Information, at a fundamental level, is physical. Moving it costs energy. Processing it costs energy. Storing it costs energy. The energy cost of a data operation is not separate from the data management problem. It is part of it.

When an organization asks how to manage data more efficiently in the AI era, the full answer has to include: what is it costing to process this data at the hardware level, and is that cost sustainable as the workloads scale?

Organizations that have invested deeply in data management at the logical layer: governance, lineage, quality, and observability, and have not yet asked this question, are managing half the problem.

managing half the problem

The hardware abstraction that has let software engineers ignore the physical layer is starting to fail. The energy wall that Extropic identified is real. And the response to it, whether through thermodynamic computing, photonic computing, neuromorphic architectures, or approaches not yet built, will change what data management means at a fundamental level.

The organizations that understand this early will not have to rebuild their thinking when it becomes unavoidable. The ones that treat data management as purely a software concern will find that the physical layer eventually makes its presence known in ways they did not plan for.

Data and energy flows. And at some level of the stack, they are the same flow.

Managing one without understanding the other is going to look, in retrospect, like an incomplete answer to a question that was always asking for more.

ROI-Focused Performance Marketing for SaaS
Blogs

Own the Category with ROI-Focused Performance Marketing for SaaS

Own the Category with ROI-Focused Performance Marketing for SaaS

Everyone is obsessed with CPL, but the revenue graveyard is full of cheap leads. What happens when you stop buying clicks and build a sustainable SaaS moat?

Most SaaS performance marketing is just a high-speed way to burn through a Series C round.

Companies treat their ad spend like a box to check, much like they treat media production. They hire agencies to chase clicks, celebrate a lower CPL, and ignore the fact that their sales team is drowning in “leads” that have no intention of buying.

It is a distraction from the real problem.

The truth is that most companies choose the wrong path because they focus on the format instead of the actual outcome: revenue, rather than building a solid B2B SaaS market strategy.

The line between audio, video, and paid performance has almost vanished in your current market. If you want to leverage performance marketing to scale a SaaS company, you have to stop chasing vanity metrics and start building a system that drives trust at scale.

The Lead Generation Mirage

Why does traditional lead generation for SaaS often fail to convert into real revenue?

Most SaaS leaders are obsessed with the lead magnet. They create a generic “State of the Industry” PDF, spend $50,000 on LinkedIn ads to promote it, and then wonder why their pipeline is stagnant.

actually works

These companies are invisible. They are following a tired playbook because their competitors are doing it, recording generic content that offers the same “thought leadership” advice everyone else shares.

The problem with this approach is that it treats performance marketing as a closed ecosystem where you buy attention and expect an immediate transaction. But buyers don’t find solutions by clicking on a boring banner; they find them through trust and specific recommendations.

If you rely on a “Get a Demo” ad to find new leads without any prior relationship, you are going to fail.

Performance marketing should not just be about the top of the funnel but should align with smarter lead scoring methods in SaaS to qualify real intent. It is most effective when it bridges the gap into the middle of the funnel, building long-term trust with prospects who are starting to become familiar with your brand.

The real value is staying in a prospect’s ear or feed consistently, which is a massive advantage in a world where attention spans are measured in seconds.

Creative Strategy as the Only Real Targeting

In the world of ROI-focused marketing, video is the engine that solves the discovery problem, especially when integrated into a broader SaaS social media marketing strategy. Platforms like LinkedIn, YouTube, and TikTok have algorithms that push video content to people who don’t follow you yet.

A single sixty-second performance clip can generate more new awareness in one day than a hidden whitepaper can in a year.

However, the friction is much higher with video.

If your ad feels like a commercial- complete with stock music, generic graphics, and a robotic narrator- buyers will scroll away immediately.

To win with performance marketing, you must show the product in action.

Don’t just run ads talking about “streamlining workflows”.

Record your screen. Illustrate exactly how your software saves a user ten hours a week. Show the messy parts of the process. This transparency builds a level of credibility that polished marketing can never touch.

Buyers are tired of the “corporate” look; they want to see the tool’s reality.

Navigating the Enterprise Decision Loop

In enterprise SaaS, you aren’t selling to a single person.

You are selling to a committee of ten to fifteen people. Every decision-maker on that committee processes information differently. If your performance marketing only targets the “V.P. of Something,” you are ignoring the people who actually influence the check.

  • The end-user needs a quick two-minute video showing a specific feature that solves their daily headache.
  • The director might prefer a forty-minute deep-dive or a long-form discussion they can listen to during their commute.
  • The CFO doesn’t want to watch or listen to anything; they want a one-page summary of the financial results.

If you only produce one type of media or ad, you are ignoring a significant part of the buying committee.

You need a strategy that covers the entire spectrum. It doesn’t mean you need a massive team; it means you need an integrated production process. You record one high-quality conversation and use it as the raw material for everything else- long-form videos for YouTube, audio for podcasts, and short, punchy clips for your paid social feeds.

Why Educating for Free is Your Greatest ROI

The foundation of effective thought leadership in SaaS marketing. Your product features are not a sustainable moat; your competitors will copy them in months. Your pricing is not a moat either; someone will always be willing to go lower. Your only real moat is the trust you build with your market.

CPL mirage vs trust moat pick one

Performance marketing is the fastest way to build that trust at scale, but only if you shift from “marketing” to “education”.

When you use your ad budget to educate, you are the authority.

If you sell security software, don’t talk about your dashboard features; talk about how to prevent a data breach or how to audit a system.

When a buyer listens to you or watches your content for twenty hours over a year, they feel like they know you. They trust your philosophy. When they finally have the budget and the need, they won’t search for a generic vendor; they will go directly to the source of their education.

That’s why the “concise and direct” approach works. Stop trying to be “professional” and start trying to be useful. Talk like a human to other humans.

Escaping the Attribution Blind Spot

Stop looking at vanity metrics like downloads or impressions if those people don’t fit your ICP, and instead focus on meaningful marketing ROI for SaaS. You don’t need a million followers; you need the right five hundred people. The real ROI of performance marketing is often hidden in the “dark funnel”.

You must track how many customers mention your content during the sales process.

Ask your sales reps to document when a prospect says, “I saw that video you posted about X”. This is the qualitative data that proves your strategy is working. You will likely find that a buyer watches three videos and listens to two podcasts before requesting a demo.

That is the journey of a modern enterprise buyer.

The Low-Fidelity High-Impact Workflow

is a practical approach often overlooked in SaaS startup marketing. Many SaaS leaders hesitate to start because they think they need a professional studio with 4K cameras. It is a mistake.

High production value can actually make your ads feel cold and corporate. Some of the most successful creators use a simple webcam and a decent microphone.

The value is in the insight, not the frame rate.

However, you cannot compromise on audio quality. People will forgive a grainy video, but they will not listen to something with static or echoes. Buy a two-hundred-dollar microphone and a basic light- that is all you need to get started. Spend the rest of your budget on a good editor who can cut your long recordings into interesting, high-intent clips for your performance campaigns.

The choice isn’t between different ad platforms.

The actual choice is whether you will be a participant in your industry’s conversation or a spectator. Performance video gives you the reach to find new people; deeper content gives you the depth to convert them.

Begin with a video-first approach. Solve problems for free through your content. If you do this consistently, you won’t just grow your company; you will own your category.

How are you currently measuring whether your paid media is actually building trust with your target accounts?

AI
News

AI for Robots in Agenda for NVIDIA as It Partners Up with Cadence

AI for Robots in Agenda for NVIDIA as It Partners Up with Cadence

NVIDIA, Cadence collaborating seems like a natural progression in this AI-first world. But can AI truly parent its next generation of hardware? Seems questionable.

AI for design or design for AI- this is the question as NVIDIA enters into a partnership with Cadence Design Systems (CDS). The overview is that NVIDIA aims to create a virtuous cycle of AI design by breaking physical and computational bottlenecks.

Moore’s theory is a significant observation, a trend that the entire manufacturing industry operates on. And since 1965, the industry has been finding loopholes to shrink as many transistors as possible. But forcefully fitting several transistors together creates heat, and one cannot remove a single transistor without melting the chip.

And that was merely one of the many challenges that threaten to stall Moore’s law.

The NVIDIA-Cadence alliance is a strategic workaround to this dilemma.

Training inside simulations is obviously much easier than training robots in the real world. There are physical limitations (Moore’s law is one), and the training data is also readily available. Now Cadence is generating them through its physics engines- to train robots inside simulations.

But even that faces a conundrum. There’s little understanding of how real-world materials interact. However, this partnership might truly change that.

Cadence has designed a head agent, called the AgentStack, that’s fuelled by NVIDIA’s Nemotron models. This AI sifts through thousands of design possibilities to find the best one- it’s basically AI designing another AI.

It is the future of AI design.

Meanwhile, NVIDIA is using these head agents to design their own chips- it’s a loop: NVIDIA’s chips are being designed by AI running on NVIDIA’s chips.

It’s a dual-track strategy.

Cadence’s agents are basically expert copilots who can observe a design and suggest changes accordingly. AI is leveraging AI to build the next generation of AI hardware

– a feedback loop like this:

NVIDIA designs and builds a quicker GPU ⇒ Cadence leverages it to make their software more effective and speed up output ⇒ Software engineers use this to build faster GPUs.

Rinse and repeat.

The goal is to decrease the time needed to complete significant tasks- the focus is on building AI for robotic systems. And we’re beginning by actively zeroing in on the designs.