A small, senior team you can hand the work to.
ProAnalytiqs builds agentic AI and analytics on data you can defend. We are not an AI agency that discovered data — it is the other way round: the practice grew out of thirteen years of building analytics inside organisations that had to answer for the numbers afterwards.
We exist because the same gap kept turning up. Companies needed a senior AI and analytics team and could only hire one person at a time.
Six decisions about the shape of the thing
Most engagements fail on the shape of the arrangement rather than the skill of the people. These are the choices we made about that shape, written down before you ask.
A pod, not a body shop
You get a small team that has worked together, with its own lead and its own standards, on one invoice. You brief the lead rather than four contractors, and if someone is unavailable the pod absorbs it.
One thread before any width
The first thing we build is the narrowest end-to-end slice that reaches production. Not a platform, not a strategy — one decision, working, with monitoring on it.
Context before models
Nothing goes in front of a model until the metrics it will read are defined once, tested and described. Most of what arrives labelled as a model problem is a definition problem.
Built to be questioned
Evals, tracing, lineage and sign-off trails come as standard rather than as a later phase, because the awkward question always arrives eventually.
Cheap to leave
Thirty days notice, no exit fee, and documentation written continuously rather than at the end. A supplier confident in the work does not need to lock you in.
We will tell you to stop
If AI is the wrong tool, or the work is done, or the real problem is organisational, we say so. We would rather lose a retainer than bill for motion.
Thirteen years, four regulated industries
These are our leadership’s own figures. We do not publish client outcome numbers on a website — ask on a call and we will walk you through the work properly, under NDA where it needs to be.
Madhukar Reddy V
Founder & Principal AI and Analytics Lead · CFA Level III Candidate
“Anyone can get a model to answer a question. The hard part is being able to say, eighteen months later, exactly why it gave that answer — and most teams find that out far too late.”
Thirteen years building analytics, BI and data engineering for Microsoft, Sony, Wells Fargo, Fidelity Investments, Hudson's Bay and Royal Bank of Canada. Now leads the analytics and AI stack for a multi-market European fintech, with generative AI running in production against it.
- Led analytics teams for Microsoft, Sony, Wells Fargo and Fidelity Investments
- Analytics strategy for Hudson’s Bay, Sobeys and Royal Bank of Canada
- Generative AI, Databricks, Snowflake and Power BI running in production today
- Direct experience of regulator, audit and funder scrutiny — the reason guardrails come first here
Who we are — and are not — right for
Saying this out loud saves everyone a fortnight.
A good fit
- Companies putting AI anywhere near a decision somebody can appeal
- Copilot or assistant rollouts that are quietly losing users
- Series A to Series C businesses that need a senior team before they can justify hiring one
- Teams with a dbt project that has drifted and an AI roadmap that depends on it
Not a good fit
- Anyone who wants a chatbot demo by Friday
- Projects where the real disagreement is about who owns a number
- Work that needs full-time overlap with US Pacific hours
- Teams looking to outsource the judgement as well as the building
Have the conversation with the person who will run the work.
Not a salesperson, not an account manager. The fit call is with the lead who would be reviewing every model, prompt and eval your pod ships.
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