What does Anemoi Solution do?

Anemoi Solution is an AI and blockchain SaaS development agency. We partner with startup founders in Europe and India to design, build, and launch AI-powered and blockchain-powered SaaS products — acting as a technical co-founder rather than just a vendor.

Which countries does Anemoi Solution work with?

We primarily serve startup founders in Germany, the Netherlands, Belgium, Dubai, Malaysia, and India. We operate in CET-friendly hours and understand the DACH and Benelux startup ecosystems.

How long does it take to build an AI SaaS MVP?

A typical AI SaaS MVP takes 10–16 weeks depending on complexity. We follow a structured process: Discovery → Design → Development → Launch, with weekly demos so founders are looped in throughout.

Does Anemoi Solution offer blockchain development services?

Yes. We offer smart contract development on Ethereum, Polygon, Hyperledger, and Hedera. We also build NFT marketplaces and blockchain-integrated web applications.

How do I get started with Anemoi Solution?

Book a free Discovery Call via our website at anemoisolution.com. We will discuss your idea, your market, and determine whether AI or blockchain can genuinely create a competitive advantage for your product.

Why Investors and Enterprise Buyers Are Rejecting "Wrapper" Products in 2026

Alekh & Jasleen
22 hours ago
9 min read

The Real Cost of Building a Thin Product

Founders often ask us how much technical depth their product actually needs before it can raise money or win an enterprise contract. The honest answer is that the bar has moved. A clean interface sitting on top of a language model used to be enough to get a meeting. Today it is in decline, and the reason is almost always financial rather than technical.

A product that any competitor can rebuild over a weekend does not hold its valuation. It does not retain enterprise customers once those customers realize the switching cost to a rival product is close to zero. Investors and enterprise buyers have both learned this the expensive way over the last two years, and it now shapes every serious conversation about what a fundable, sellable software company actually looks like.

Addressing the thinning of the SaaS field, SmartWinnr CEO Anindita Banik stated in an interview with the Economic Times: "A lot of products are essentially an AI layer sitting on top of an existing workflow, and users see through that quickly. The platforms that last will be the ones that tie AI to a real business problem and can show the outcome it produced."

At its core, this comes down to one question every founder should be asking before they write another line of code or take another investor meeting. Is the end user willing to pay for the product or will the product hold when the product scales? This piece walks through the business reasoning behind that question, along with the research and numbers that back it up, so you can pressure test your own product against it.

Reliability Failures Are a Revenue Problem, Not Just an Engineering One

Businesses do not buy software because it is technically interesting. They buy it because it reduces cost, reduces risk, or generates revenue, and they stop paying for it the moment it fails to do those things reliably. This is the practical reason multi agent systems, which coordinate several AI agents through a long business process such as a financial audit or an insurance claim, have become such a serious commercial risk when they are built without proper safeguards.

When an agent fails midway through a ten step workflow and the whole process crashes or produces a wrong result, that is not an inconvenience. It is a customer escalation, a support ticket, and in regulated industries potentially a compliance incident. Gartner's research reflects exactly this pattern. Their analysts project that more than 40 percent of agentic AI projects will be canceled before the end of 2027 [5], and the reasons cited are escalating costs, unclear business value, and inadequate risk controls rather than any shortcoming in the underlying models. Gartner has also estimated that of the thousands of vendors currently marketing agentic AI, only about 130 are building systems that actually justify the label [5]. The rest are exposing their customers to exactly this kind of operational risk while charging enterprise prices for it.

From a business standpoint, the fix is straightforward to describe even though it takes real engineering to deliver. A workflow needs to recover gracefully when a single step fails, rather than forcing a client to restart an entire process or, worse, silently delivering a wrong answer. Vendors who can demonstrate this kind of resilience win longer contracts and face fewer renewal conversations that start with a complaint. Vendors who cannot are the ones showing up in Gartner's cancellation statistics.

There is also a procurement dimension to this that founders often underestimate. Enterprise buyers increasingly ask vendors directly how a system behaves when a step fails, what the fallback process looks like, and who gets notified when an agent produces a low confidence result. These questions now show up in security reviews and vendor questionnaires alongside the usual data privacy and uptime requirements. A team that cannot answer them with specifics loses the deal to a competitor who can, regardless of how good either product looks in a demo.

Regulated Industries Will Not Buy a 90 Percent Accurate Product

The industries with the largest software budgets, including legal, insurance, compliance, and B2B banking, also carry the least tolerance for error. A wire transfer processed incorrectly or a compliance filing built on a hallucinated figure does not cost a company a support ticket. It costs a regulatory fine, a lawsuit, or a client relationship that took years to build.

This is the exact gap MIT's Media Lab identified in its 2025 study of enterprise AI adoption. Researchers examined roughly 300 public AI deployments and interviewed more than 150 executives. Despite an estimated 30 to 40 billion dollars in enterprise AI spending, about 95 percent of generative AI pilots failed to produce any measurable financial return. The study attributed this to a learning gap, where tools were added to existing workflows without the structure needed to retain feedback or meet the accuracy standards a regulated business actually requires. [3][4]

The same research contains a number every founder deciding how to build should pay attention to. Pilots that combined internal teams with external specialist partners succeeded at a rate of roughly 67 percent. Projects built entirely in house without outside expertise succeeded at a rate of about 22 percent. That gap is often the difference between a product that generates renewal revenue and one that gets quietly shelved after the first budget cycle. [3][4]

Winning these buyers commercially requires building AI output through layers that behave predictably, even when the model underneath does not. That includes evaluation systems that continuously check output quality rather than relying on a one time demo, validation steps that catch an incorrect answer before it reaches a client or a database, and cost controls such as semantic caching that keep inference spending from eating into margin as usage scales. None of this is visible in a sales demo, but it is exactly what a procurement team and a compliance officer will ask about before they sign, and it is what determines whether a contract renews the following year or not.

The sales cycle itself changes once this reliability is in place. A vendor selling into a bank or an insurance carrier is rarely negotiating with a single technical buyer. The deal usually needs sign off from legal, compliance, and risk teams as well, and each of those groups is evaluating the product on a different axis. Legal wants clarity on liability if the system produces an incorrect output. Compliance wants an audit trail showing exactly why a given answer was generated.

And do you know what is the biggest compliance risk today? AI washing.

It is a deceptive marketing practice where companies overstate, exaggerate, or completely fabricate the use of artificial intelligence in their products, services, or corporate operations.

The term is modelled after greenwashing when companies fake being eco-friendly and is used to cash in on the intense market hype surrounding AI.

Data Access Is What Determines Company Value

The most durable and highly valued software companies right now are not winning because their model is smarter than a competitor's. They are winning because they built the connections that let their product reach the data buried inside a client's decades old systems, the legacy ERP, the on premise banking database, or the insurance mainframe nobody else wants to touch.

This is where real acquisition value gets created. A competitor can copy an interface in a weekend. Copying eighteen months of integration work with a client's legacy claims system takes far longer, and that gap in time is exactly what an acquirer or an investor is paying for when they value a company above its immediate revenue.

This kind of access also determines whether a company keeps its customers. Research into enterprise AI adoption has found that in the large majority of firms, employees keep using personal AI tools even after an official company pilot stalls, usually because the sanctioned product never got proper access to the systems where the useful data actually lives. Building the secure, reliable pipelines that connect a modern product to a client's legacy environment is often the single largest factor separating a vendor that gets renewed year after year from one that gets quietly replaced at the next contract review.

This is also why deep integration work tends to show up directly in a company's financial metrics, not just its product quality. Once a client's data and workflows are wired into a vendor's system, switching to a competitor means redoing that integration work from scratch, which is exactly the kind of friction that keeps churn low and contract values high. Investors evaluating a company at renewal time or ahead of an acquisition look closely at how much of this integration work exists, because it is one of the more reliable predictors of whether current revenue will still be there in three years.

Where Y Combinator Data Fits In

Investor behavior offers one of the clearest signals of where this is all heading, and Y Combinator's most recent batch is a useful data point rather than the whole story. YC's Winter 2026 batch was the largest in the accelerator's history, with more than 180 companies, and it carried the sharpest tilt toward deep technical infrastructure the accelerator has funded to date. Roughly four out of five companies in that batch are AI focused, and nearly two thirds of them sell to other businesses rather than consumers, the kind of buyer that evaluates a vendor's technical depth before signing a contract. [1]

Growth also looked different this cycle. Three times as many companies in the W26 batch reached one million dollars in annualized revenue compared to the batch before it, even though the batch itself was larger. That points to something founders should take seriously. Reaching early revenue is no longer the hard part. Building something durable enough to keep that revenue is. Global venture funding reached an estimated $510 billion in the first half of 2026 alone, more than the whole of 2025, and the majority of that capital went toward AI companies building real infrastructure rather than interface layers. [2]

The lesson from this one data point mirrors everything in the sections above it. Capital is following durability, not novelty.

A Checklist for Founders Before You Build or Raise

Everything above is easier to apply if you turn it into questions you can actually answer about your own product. Before your next investor meeting or enterprise sales call, work through these honestly.

On reliability, can you explain exactly what happens when a single step in your workflow fails. Does the system recover, retry, or escalate to a human, or does it simply crash or return a wrong answer silently. If you cannot answer this in one sentence, an enterprise security review will find the gap for you.

On accuracy, do you have a repeatable way to measure whether your output is correct, or does your confidence come from a handful of demos that happened to go well. A system that has never been tested against known correct answers at scale is not ready for a regulated buyer, no matter how good it looked in your pitch.

On data transformation, can your product turn the data it is given access to into inputs the AI can actually use? The challenge is handling different formats, systems and permissions without compromising the quality of the output. A product is only as useful as the data it can reliably work with.

On cost, do you know what it actually costs you to serve one customer at scale, including inference spend, and have you built anything to control that cost as usage grows. Margins that look fine in a pilot can disappear entirely once real usage hits, and investors will ask about this before they ask about your growth rate.

If you can answer all four with specifics rather than intentions, you are already ahead of most products currently being pitched.

Building for the Buyer, Not the Demo

Every section here points to the same conclusion from a different angle. Reliability protects revenue. Accuracy wins regulated buyers. Data transformation determines company value. Capital rewards durability. None of that is about having a clever prompt. It is about the business outcomes that architecture makes possible, which is exactly why a copyable interface cannot compete with it for long.

If you have a vision for a software product and you are weighing whether to build this kind of architecture in house or bring in a team that has already solved these problems, that decision is worth making before your next funding conversation or your next enterprise sales cycle, not after a competitor with better usability and infrastructure wins the deal instead. This is the exact problem our team at Anemoi Solution works on with founders every day.

If you have an idea and want to outshine competitors? Let us map out your journey while designing and building products.

About Anemoi Solution

We design and build the next generation of SaaS

Anemoi Solution is an AI and Blockchain SaaS development agency founded by Alekh Johari, with offices in Delhi, India, Spain and Malaysia. We partner with founders and companies to design and build intelligent, scalable products integrating AI agents, blockchain infrastructure, and immersive technology into solutions that are built to last. With a presence across three continents, we bring global perspective and on-the-ground expertise to every engagement we take on.

We work with founders who want to build products that can withstand real users, real data and real business requirements. Our work focuses on the user experience, architecture, product decisions and technical depth needed to turn an AI vision into a durable product, rather than a thin layer around an existing model. Where needed, we evaluate and help enterprises integrate AI and blockchain into their existing products and workflows with the reliability, security and infrastructure those environments demand.

Connect with us: info@anemoisolution.com