AI Consulting

Dabu Lab works with a small number of companies to design and implement AI systems built around how they actually operate. Not integrations of off-the-shelf tools. Not generic chatbots. AI that fits the specific workflows, data constraints, and risk tolerances of the business.

• Building internal AI tools. Designing and implementing an AI system for a specific internal use case — customer service, document processing, compliance review, internal knowledge retrieval. Scoped to the actual use case, not the broadest version of it.

• AI fit assessment. Evaluating whether a proposed AI system actually makes sense for the business, what the risks are, and what would need to be true for it to work. Sometimes the answer is: not yet, and here is why.

• AI system review. Auditing an existing AI deployment for misalignment between what the system does and what the business actually needs. This often surfaces in production before anyone names it.

Finance and communications are the primary focus. Both are regulated environments where AI needs to behave predictably and where the cost of a surprise in production is high. That constraint clarifies the design work: systems that can be tested, understood, and explained — not systems that work until they don’t.

Large consultancies deploy teams. By the end of the engagement, the team that understood your business is gone and the team finishing the work is reading the notes. Dabu Lab works with a short list of companies at a time — short enough that nothing falls between the cracks. The people who scope the work are the people who do it. That is a different kind of accountability.

What does AI consulting actually involve?

Scoping, design, implementation, and testing — not strategy decks. We work through what the business actually needs, design the system around that, build it, and make sure it behaves the way it should before it touches production.

How is boutique AI consulting different from a large consultancy?

At a large firm, the people who scope the engagement rarely do the work, and the team rotates before they understand the business. At Dabu Lab, the people who scope the work are the people who do it — same person from first call to final review. That continuity changes what’s possible.

How long does an AI consulting engagement take?

Typically 4 to 16 weeks, depending on scope. An AI fit assessment might take four weeks. Building and deploying an internal AI tool end-to-end is usually 10 to 16 weeks. We scope before we commit to either.

Do you work with companies outside NYC?

Yes. The work is primarily remote. We have clients in finance and communications across the US, UK, and the Gulf. Location hasn’t been a constraint.

What industries do you specialise in?

Finance and communications are the primary focus. Both are regulated environments where AI needs to be predictable and testable — not systems that produce surprising outputs in production. The constraints are real and we find them clarifying, not limiting.