Illustrative enterprise data infrastructure

AI consulting & development

AI earns its place when it makes a real workflow better. We bring data science and application engineering together around a defined problem.

Discuss this work Get the free AI readiness report

Illustrative image · not client premises

A practical delivery view

Understand the work

Build & integrate

Evaluate & hand over

Choose the right problem

Review the work, users and available information before choosing a model. A narrow, measurable pilot is a more useful starting point than an open-ended transformation programme.

Workflow and data review
Pilot scope and evaluation criteria
Ownership and review responsibilities

Build for the actual user

Knowledge assistants, custom AI and agentic workflows should fit the systems people already use. Integration and evaluation are part of the work, not an afterthought.

Document-grounded assistants
AI features in existing products
Human review for consequential actions

Make the result usable

The work needs a clear way to distinguish a useful response from a convincing but incorrect one. Evaluation should use the documents and tasks the system will actually encounter.

Representative evaluation examples
Data-access and privacy boundaries
Integration and handover needs

Where AI projects usually stall

Most teams do not lack ideas for AI. They lack a clear first project. Pilots get stuck because the data is scattered, nobody owns the result, or the demo worked on tidy examples and failed on real ones. Others build a chatbot when the costly problem sits in a back-office process. Good consulting narrows the list to work that is frequent, measurable and backed by data you already hold.

Too many ideas, no clear first project
Pilots that fail on real data
No owner for the result
AI aimed at the wrong problem

What we have built

Data Prophets has delivered AI work since 2019. For EHR product owners and independent hospital chains, we built plug-and-play healthcare modules, including a staff notetaker tuned to each client’s medical terminology. For an Indian bank, we automated the reading of handwritten forms in Indian regional languages. We also build voice AI and NLP systems with custom RAG pipelines, and a company brain: a multi-purpose assistant trained on a company’s own data.

Healthcare AI modules inside existing systems
Handwritten regional-language form reading
Voice AI and NLP with custom RAG pipelines
Computer vision for safety, sorting and quality checks

How to start

Start with the free AI readiness report. Answer six questions on this website and the team sends a written report within one business day. It covers where your company stands today, how your industry is adopting AI, the challenges you are likely to face, and possible solutions with their benefits. If you already have a problem in mind, a short call with a practising data scientist is the quicker route.

Six questions on the website
Written report within one business day
Or a short call about one problem

Technology in the work

Python

SAP

Salesforce

Technology references, not partnership or certification claims. Fit and access are reviewed for each engagement.

Questions that come up

What does an AI consulting engagement produce?

A short list of problems worth solving with AI, the data each one needs, a scoped pilot with clear evaluation criteria, and a plan for who owns the result. If you want us to build it, the same team carries on into development.

Do you build the AI system, or only advise?

Both. We deliver custom AI and agentic automation as projects, from the first review through to integration with the systems your people already use. We can also build a whole team for you and hand it over as an AI centre of excellence.

What is agentic automation?

It is automation where an AI system plans and carries out a sequence of steps towards a goal, using the tools and data you give it, instead of following one fixed script. For consequential actions, a person should still review and approve.

Do we need clean data before we talk to you?

No. Most companies start with data spread across systems, spreadsheets and documents. Part of the first review is to see what you hold, what state it is in and what must be fixed first. Sometimes the right first project is data engineering.

Keep exploring

Close to the work, from first scope to handover AI use cases & client outcomes Free AI readiness report

Let’s talk about your next step.

The workflow, the data and what needs to change.

Book a call with a founder Contact our team