Get the reporting right. Then put AI on top.
Data architecture, dimensional modelling in dbt, Power BI reporting people actually open, and product analytics that settle arguments — then the AI engineering and agents that stand on top of all of it. In that order, for anyone who eventually gets asked how the number was produced.
Where our leadership has delivered · 2011–2026
How this usually goes wrong
It is nearly always the same sequence, and it starts long before anyone says the word AI.
Nobody trusts the numbers
Three teams quote three different revenue figures. The board pack is rebuilt by hand every month because no one will sign off the automated one.
The dashboards go unopened
A reporting layer was built and launched. Usage was forty in the first week and six a month later. Everyone concluded people needed training.
AI gets pointed at it anyway
A model is put in front of data nobody trusted to begin with. It answers confidently, it is wrong often enough to matter, and nobody can tell which times.
Fix it in the order it broke.
Architecture first, so the numbers arrive. Then a dimensional model in dbt, so a metric means one thing. Then reporting people open without being asked. Only then is there anything worth pointing a model at — and at that point the same model that would have guessed is answering from metrics you defined yourself.
Nine things we build, in the order they matter
Business intelligence, data modelling, data architecture and product analytics first — because they are what everything above them stands on. AI engineering and agents last, and only once the rest is true.
Business intelligence & reporting
Semantic models, governed metrics and dashboards leadership opens without being asked to — in Power BI, Fabric or Tableau.
Data modelling & dbt
Conformed dimensional models with a declared grain, metrics defined once in dbt, and descriptions written so a person and a machine can both read them.
Data architecture & platform
The architecture underneath everything: ingestion, warehouse or lakehouse design, orchestration, and the tests and alerting that keep it honest.
Product analytics
Event taxonomies, funnels, cohorts and experiments — so product decisions stop being opinions with a chart attached.
AI engineering
The system around the model: retrieval, tool use, orchestration and the plumbing that makes an assistant useful rather than impressive.
Agentic AI on your data
An agent per KPI domain, watching the business the way a good analyst would if they never slept — and interrupting you only when it has already worked out why.
Guardrails & evals
Evals are the unit tests of an AI system. Without them you are shipping prompt changes on hope, and you will not know the day it stops working.
Decision science
The models that sit inside the decision itself — scoring, fraud, pricing, forecasting — benchmarked, monitored and written up for a risk committee.
Fractional AI & data leadership
A head of data and AI one or two days a week, for companies that need the judgement long before they need the headcount.
One working thread first
A thread is an engineering term: the thinnest end-to-end slice of a system that proves the whole path works. It is also how we sell, because it is the only honest way to find out whether this holds up in your business.
One decision, wired from source data through context, model and guardrail into production. Thin, complete, provably working. Then we widen it.
If the thread holds, we widen it. If it does not, you have spent six weeks rather than six months — and you have learned something true about your data.
Fit call
30 minutesYou describe the decision you want to improve. We tell you whether AI is the right tool for it, and say so plainly when it is not. No deck.
Readiness Check
2 weeksA fixed-fee diagnostic. Where AI would pay, whether your data can carry it, what any existing AI would fail on, and a prioritised 90-day plan you own outright.
The first thread
3 to 6 weeksOne decision, wired end to end: source data, context layer, model, guardrail, production, monitoring. Deliberately narrow. It either works or it tells us something true.
Widen it
OngoingOnce the thread holds, the pod broadens it on 21-day cycles. Every cycle ends with something in production and a written readout. Thirty days notice, no exit fee.
Two families. Most people start with the first.
Analytics pods build the estate — pipelines, models, dashboards, product analytics. AI pods build what stands on it. Buying the second without the first is the most common and most expensive mistake in this market.
Analytics pods
The reporting estate itself: pipelines, models, dashboards people open, and the product analytics that settle arguments. This is where most engagements start, and where AI has to stand on later.
Foundation Pod
The pipelines and the warehouse that everything else stands on.
- 1× AI & Analytics Lead (0.25 FTE)
- 2× Data Engineer
- 1× Analytics Engineer
Insight Pod
One set of numbers the whole company argues from, not about.
- 1× AI & Analytics Lead (0.25 FTE)
- 2× BI Engineer
- 1× Analytics Engineer
Product Analytics Pod
Your product team stops arguing about whose number is right.
- 1× AI & Analytics Lead (0.25 FTE)
- 1× Product Analyst
- 1× BI Engineer
- 1× Analytics Engineer
Copilot Pod
The business asks in plain English and gets your answer, not a plausible one.
- 1× AI & Analytics Lead (0.25 FTE)
- 1× Analytics Engineer
- 1× BI Engineer
- 1× AI Engineer
AI pods
What you build once the numbers are trustworthy: a context layer a model can read, decisions with guardrails on them, and agents that watch the business without making things up.
Context Pod
AI that answers with your numbers, not numbers it invented.
- 1× AI & Analytics Lead (0.25 FTE)
- 2× Analytics Engineer
- 1× Data Engineer
Decision Pipeline Pod
A decision that runs itself, and can still be explained afterwards.
- 1× AI & Analytics Lead (0.25 FTE)
- 1× Decision Scientist
- 1× AI Engineer
- 1× Data Engineer
Guardrail Pod
An AI system you would be comfortable putting in front of a regulator.
- 1× AI & Analytics Lead (0.25 FTE)
- 1× AI Engineer
- 1× AI Governance & Risk
- 1× Analytics Engineer
Monitoring Pod
The business watches itself, and only interrupts you when it matters.
- 1× AI & Analytics Lead (0.25 FTE)
- 1× AI Engineer
- 1× Analytics Engineer
- 1× Data Engineer
AI & Data Readiness Check
An honest answer to two questions: where AI would actually pay in your business, and whether your data can carry it yet.
Two weeks, and you own the output
- Interviews with the people who use, and mistrust, the numbers
- A review of your pipelines, dbt project, semantic models and reporting
- Any AI already in the path, and the specific things it would fail on
- Your EU AI Act exposure, if you have any, in plain language
- A scored assessment across the five capability areas
- A prioritised 90-day plan, with the first working thread specified
Every analytics role has been rewritten in eighteen months
Not replaced — rewritten. We staff and build for what these jobs are now, rather than what they were on the last org chart.
Analytics engineers
Have quietly become the most important people in the building. The dbt project is no longer just what feeds a dashboard — it is the context an AI system reads. A metric defined once and tested is a metric a model cannot get wrong.
BI developers
Spend less time building pages and more time preparing semantic models for Copilot: synonyms, hierarchies, descriptions, verified answers. Most Copilot disappointment is a model preparation problem wearing a Copilot costume.
Analysts
Draft with a model and validate by hand. The work moved up a level: less writing the query, more deciding what the question means and whether the answer can be trusted.
Data engineers
Scaffold pipelines and generate quality tests with assistance, then spend the time they got back on contracts, lineage and the freshness guarantees an AI system quietly depends on.
AI engineers
A role that barely existed three years ago and now sits in every serious data team. Retrieval, tool use, agent boundaries, evals — the systems work around a model somebody else trained.
Model risk and governance
Moved from an annual validation document to a live discipline: inventories, model cards, eval evidence and, since August 2026, Annex III obligations for anyone scoring creditworthiness.
Built by someone who has shipped this in production
ProAnalytiqs is led by Madhukar Reddy V, who has built analytics and AI programmes for Microsoft, Sony, Wells Fargo, Fidelity Investments and Hudson’s Bay, and now runs the analytics and AI stack for a multi-market European fintech. The figures below are his, not a client average.
We do not publish client outcome numbers. Ask on a call and we will walk you through the work in detail, under NDA where it needs to be.
Read the full profile
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
Notes from inside the work
What we have learned putting AI into regulated decisions, and what we have learned taking it back out again. Written for the people who have to live with the result.
How every analytics role changed in eighteen months
The headline everybody expected was replacement. What happened instead was a shift in which part of each job is scarce. In almost every analytics role, the…
ReadYour semantic layer is your context layer
Every company that has tried to put an assistant in front of its data has run the same experiment, whether or not they meant to. Point a capable model at the…
ReadText-to-SQL fails on your warehouse, and it is not the model's fault
Text-to-SQL benchmarks look encouraging. Then somebody runs the same model against a real warehouse and accuracy falls off a cliff, and the conclusion drawn is that…
ReadThe things people ask before signing
Are you just putting a chat box on top of our database?
No, and that is the thing we are usually hired to undo. Pointing a language model at a normalised warehouse produces confident, wrong answers, because the model has to guess your business logic from table names. The work is in the layer underneath: metrics defined once in dbt, documented in a way a model can read, and served to the assistant as tools rather than tables. The chat box is the last two percent.
We do not have dbt or a semantic layer yet. Are we too early?
You are not too early to talk, but you probably are too early to buy an AI assistant. That is what the Foundation and Context pods exist for, and it is what the Readiness Check is honest about. Building AI on top of pipelines nobody trusts just moves the mistrust somewhere more expensive.
How do you stop it hallucinating?
Three things, in order. Constrain what it can reach — it queries defined metrics through tools, not arbitrary SQL. Check the answer against the retrieved context before it is shown, so an unsupported claim fails rather than ships. And run an eval suite on every prompt, model or data change, with a golden set of real questions. Hallucination is not a prompt problem, it is a systems problem.
Does our data end up training somebody else’s model?
Not with the setups we build. We default to enterprise endpoints with no training on your data, run inside your own cloud tenancy where the workload allows, and pseudonymise or exclude personal data before anything leaves your environment. If a use case genuinely cannot be built that way, we say so before you sign, not afterwards.
Are we in scope for the EU AI Act?
Anything that materially affects a person — credit, employment, education, insurance pricing, access to essential services — is worth checking, because Annex III high-risk obligations started applying in August 2026. Plenty of AI is not in scope at all. Working out which of your systems are, and what evidence each one needs, is part of the Readiness Check and the whole point of the Guardrail Pod.
Tell us which decision you want to improve.
A 30-minute call, no deck and no pitch. You describe the decision, we tell you whether AI is the right tool for it — and say so plainly when it is not.
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