AI & Data Craftsmanship

Your POC worked.
So why isn't it
in production yet?

Most companies have the AI ideas. Most teams can run a successful proof-of-concept. The gap between a working prototype and a production system is where things quietly fall apart. That's the gap we work in.

Let's talk about your challenge Learn more →
The challenge we're solving

A question that doesn't get enough attention

How much should the end goal shape the starting point of your AI work? It sounds simple. But the answer changes everything.

Most organisations focus hard on making the POC work — validating the idea, impressing stakeholders, showing what's possible. And that's right. But somewhere in the process, the question of "how do we get this into production?" gets deferred. And deferred. And then it's too late.

The domain logic — the core business knowledge your team brings — is usually there. That's rarely the bottleneck. What's missing is something else: the engineering decisions, the architectural choices, the design discipline that makes production a natural outcome rather than a separate, painful project.

"Too much production thinking during a POC derails it. Too little, and the POC succeeds but production never follows."

Finding the right balance is what we do.

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The POC succeeds. Production doesn't.

Your proof-of-concept impressed everyone in the room. Eighteen months later, it's still not live. This pattern is more common than anyone admits.

🔨

One product, every problem.

Product thinking scales beautifully — until it doesn't. When scalability becomes the goal, you stop solving the actual problem. Every situation deserves its own careful thinking.

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The gap isn't company-specific.

This isn't about your team or your industry. The POC-to-production gap is structural — and almost nobody is addressing it at the engineering level.

Why Datamistry

Data meets mistry — the craft of making things work.

In Hindi, a mistry is a craftsman. Someone who doesn't just understand the theory — they know how things are built, why they hold together, and what makes them last.

We think AI and data work deserves that same level of craftsmanship. Not just smart models and clever ideas — but careful, considered engineering that's built to go the distance.

That's what Datamistry means. And it's what we bring to every engagement.

01

Engineering first

We focus on the decisions that determine whether your AI work makes it to production — not just whether it works in a demo.

02

Advisory depth

Every situation is different. We bring genuine consulting rigour to understanding your specific context before recommending anything.

03

Honest about what we don't know

We're still figuring out the perfect formula — and we'll tell you that. What we know is the right questions to ask, and how to ask them early enough to matter.

Our story

Nearly two decades of learning what doesn't work

Datamistry wasn't built on a theory. It was built on a series of honest realisations — each one pointing to something the previous chapter couldn't solve.

The beginning

Software can build the tool. It can't define the strategy.

Starting as a software engineer taught precision and craft. But code alone has a ceiling. The strategy — the why behind the tool — lives somewhere else.

BCG & Diamond

Strategy has its own ceiling too.

Consulting taught how organisations think and how decisions get made. But sharp insights handed off to others is a limited model. The execution always gets complicated.

Mu Sigma & Deloitte

Analytics: value delivered here and now.

Data and analytics felt like the bridge — grounded in reality, not deferred to a future slide deck. But pure advisory is hard to scale. And hard to sustain.

Mastercard

Products scale. But they also give you a hammer.

The product mindset is seductive — and powerful. But when scalability becomes the primary goal, you stop solving the actual problem. Every challenge becomes a nail.

Scalene Group

The POC-to-production problem revealed itself fully.

Co-founding Scalene — combining advisory with software for retail analytics — is where the real complexity surfaced. Domain logic wasn't the hard part. The engineering journey from prototype to production was. That gap turned out to be structural, not situational.

Today

Datamistry. Still asking the right questions.

We don't have all the answers. But we know the question worth asking — and we're building the methods, tools and thinking to help companies navigate it. The journey continues.

Get in touch

Wrestling with AI that stalls between pilot and scale?

We'd genuinely love to hear about what you're working on — even if you're not sure what kind of help you need yet. That's usually the best place to start.

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