We are engineers who begin with the business.

We did not begin Ahum Labs with a plan to build an AI company. We began by building things.

The three founders came from different parts of India and followed different paths into technology.

One grew up in Jharkhand and was not comfortable speaking English for a long time. Another grew up in Mumbai and explored technology in Malaysia, Dubai, and New York. The third came from a small village in Uttar Pradesh and became curious about how large organisations worked.

All three once wanted to study at IIT. None did.

What felt like failure forced us to find our own way into engineering. We learned by building products, entering hackathons, speaking with people, and walking into unfamiliar rooms before we felt ready.

One of our first international experiences began with a first-ever flight to Singapore for a sponsored conference and hackathon. We finished among the top teams.

Trips became opportunities to learn. In New York, Malaysia, Dubai, and elsewhere, we wanted to understand what interesting people were building.

For nearly three years, we built products, tested new tools, explored emerging models, and competed in international hackathons.

We became good at learning quickly and turning unclear ideas into working software.

But hackathons have one advantage that real businesses do not: the problem is usually written down.

Inside a company, it rarely is.

Our thinking changed when organisations began asking us to solve operational problems.

We entered these companies, spoke with their teams, studied their systems, and tried to understand what they wanted us to build.

We assumed the difficult part would be engineering. It was not.

The difficult part was understanding what the problem really was.

A company might ask for an application when the actual problem was a broken hand-off between teams.

It might ask for AI when its information was scattered across emails, spreadsheets, old software, and the memories of experienced employees.

It might ask to replace a system even though replacement would create more risk than keeping it.

We realised that building software was only one part of the work.

Before writing code, someone had to understand how the company made decisions, where time was being lost, which systems could not be disturbed, and how employees actually behaved.

One engagement made this especially clear. An organisation wanted AI to become part of the way the company worked—not a collection of isolated experiments.

We spent about six weeks studying its industry, tools, workflows, and the available technology.

We reached a simple conclusion: no single model, product, or framework could make a company AI-native.

AI had to be introduced wherever work was already happening.

Most employees managed delegation and coordination through Microsoft Teams. Another application would have created another login, interface, and habit to learn.

So we did not ask them to move. We brought AI into Microsoft Teams.

We created governed AI workers with specific responsibilities, organisational identities, approved tools, controlled access, and clear limits.

Employees continued working in an environment they understood. Intelligence became part of normal work.

We followed the same principle in maritime operations.

Complex workflows involved several systems, documents, rules, and actions. A traditional application would have introduced more menus and training.

Employees already knew how to send messages and explain what they needed, so we made natural language the interface.

These projects changed how we thought about software.

Software was becoming faster and easier to build. Understanding a business was not.

A model could write code, read documents, and reason over information. It could not automatically understand why an employee followed an unusual process, which exceptions mattered, who held authority, or what the organisation feared losing.

That knowledge had to be learned inside the company.

A Forward Deployed Engineer can build software, but does not begin with code. They begin with the company.

They learn how the business creates value. They observe how employees work. They understand the systems, constraints, responsibilities, and exceptions.

They research the possible approaches. Then they build and remain involved until the system works in the real operation.

Today, we have trained a growing team of Forward Deployed Engineers around this way of working.

We are still learning from every organisation we enter, but the way we begin remains the same.

We speak with the people doing the work. We study the systems they depend on. We find the constraint that matters.

Then we build the smallest useful intervention. When it works, we expand.

That is how Ahum Labs started. It is still how we work today.