What the pods actually build.
Business intelligence, data modelling, data architecture and product analytics — and the AI engineering that stands on top of them. You do not buy these individually; you buy a pod that covers the ones your problem needs. This page is here so you can see what sits underneath the names.
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.
Semantic models, governed metrics and dashboards leadership opens without being asked to — in Power BI, Fabric or Tableau.
The usual symptomReporting exists, but three teams quote three different revenue figures and the board pack is rebuilt by hand every month.
How we approach it
- Agree the metric definitions first, in writing, with the people who will argue about them later.
- Build one governed semantic model rather than a dashboard per request.
- Push row-level security down into the model, not into copies of the file.
- Hand the self-serve layer to your teams and then get out of the way.
What you get
- Power BI, Fabric and Tableau delivery end to end
- Dimensional and semantic modelling
- KPI frameworks and metric governance
- Report performance and capacity tuning
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.
The usual symptomThree teams quote three different revenue figures, and whichever one lands in the board pack depends on who built the query.
How we approach it
- Define each metric once, in dbt, with the exclusions inside the definition rather than in whichever query remembered them.
- Write descriptions for a machine as well as a person — a model reads your column comments literally.
- Expose metrics as tools through MCP, so the assistant selects a metric instead of composing SQL.
- Put contracts and tests on the sources underneath, because a context layer built on a broken pipeline is just a faster way to be wrong.
What you get
- Conformed dimensional models with a declared grain
- Metric definitions, data contracts and tests
- MCP servers exposing metrics rather than schemas
- Retrieval over governed business logic
The architecture underneath everything: ingestion, warehouse or lakehouse design, orchestration, and the tests and alerting that keep it honest.
The usual symptomPipelines run on somebody’s laptop, break on a Monday, and nobody can tell you which source a number came from.
How we approach it
- Map the sources and the read patterns before choosing an architecture, not after.
- Land raw and immutable, model in the middle, serve at the edge — with tests at every layer.
- Orchestrate with retries, alerting and a runbook a stranger could follow at 3am.
- Ship through CI/CD, so a change does not need a change freeze.
What you get
- Azure Data Factory, Databricks, Synapse, Data Lake
- Snowflake, lakehouse and medallion architectures
- dbt project structure, testing and CI/CD
- Streaming and near-real-time ingestion
Event taxonomies, funnels, cohorts and experiments — so product decisions stop being opinions with a chart attached.
The usual symptomYou ship features and then argue about whether they worked, because the events were named by whoever built them.
How we approach it
- Design the event taxonomy with product and engineering in the same room, before anything is instrumented.
- Instrument the funnel end to end, including the parts that happen off the app.
- Build cohort and retention views that survive a change in definition.
- Set up experiment readouts with a power calculation rather than a feeling.
What you get
- Tracking plans and event taxonomy design
- Activation, retention and revenue analytics
- Experiment design, power analysis and readouts
- Segmentation and lifetime value modelling
The system around the model: retrieval, tool use, orchestration and the plumbing that makes an assistant useful rather than impressive.
The usual symptomThe prototype impressed everyone in a meeting and has been stuck at ninety percent ready for four months.
How we approach it
- Start from the decision, not the model. What is being decided, by whom, how often, and what does being wrong cost?
- Retrieve from governed structures first and free text second — most business questions are a metric lookup wearing a sentence.
- Give the agent a small set of well-described tools and hard boundaries rather than broad database access.
- Instrument tokens, latency, cost and failure modes from the first day, not after the bill arrives.
What you get
- Retrieval-augmented assistants over your own data
- Agents with tool access and defined boundaries
- Azure OpenAI, Claude and open-weight models
- Cost, latency and token observability
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.
The usual symptomThe dashboard exists and nobody opens it. By the time somebody does, the cohort is already written and the money is already gone.
How we approach it
- One agent per KPI domain, each with a learned baseline per series rather than a threshold somebody guessed at in a meeting.
- Decompose before escalating — the agent works out which segment, source or rule moved before it interrupts anybody.
- Write in the language the business uses, and always link back to the rows behind the claim.
- Review false positives weekly and tighten the agent, because the failure mode is not missing an alert, it is being muted.
What you get
- Learned baselines per series rather than fixed thresholds
- Decomposition before escalation: driver, size, and evidence
- A daily written brief in the language the business uses
- False-positive review that tightens the agent rather than muting it
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.
The usual symptomA prompt was tweaked to fix one complaint and quietly broke eleven answers that used to be right. Nobody found out for a month.
How we approach it
- Build a golden set from the questions your business actually asks, not the ones that demo well.
- Score every change: exact match where you can, LLM-as-judge where you cannot, and a human review sample either way.
- Check the answer against the retrieved context before it is shown — an unsupported claim should fail, not ship.
- Trace every call end to end, so eighteen months later you can reproduce exactly what the system saw and said.
What you get
- Golden sets, LLM-as-judge and regression suites
- Grounding checks and refusal handling
- PII redaction and output validation
- Model cards, AI inventory and EU AI Act readiness
The models that sit inside the decision itself — scoring, fraud, pricing, forecasting — benchmarked, monitored and written up for a risk committee.
The usual symptomA model was built, demoed well, and quietly stopped being retrained about four months ago. Nobody is quite sure when.
How we approach it
- Benchmark against the boring baseline — your current rules — before anything clever is allowed near production.
- Make training reproducible: same data, same code, same result, from a versioned feature pipeline.
- Run champion and challenger in parallel and monitor PSI and drift on a schedule, not on request.
- Document the model the way a risk committee needs to read it, while you still remember why you made each choice.
What you get
- Scoring and propensity models
- Fraud and anomaly detection
- Demand, cash-flow and collections forecasting
- Champion/challenger, PSI and drift monitoring
A head of data and AI one or two days a week, for companies that need the judgement long before they need the headcount.
The usual symptomThe board has asked what your AI strategy is, and the honest answer involves three pilots and a vendor demo.
How we approach it
- Rank candidate use cases by value and by whether your data can actually carry them. Most lists collapse at this step.
- Decide build versus buy with no reseller relationship sitting behind the advice.
- Design the team you should hire next, and help you hire it.
- Report progress to the board in outcomes rather than model names.
What you get
- Analytics and AI strategy against the commercial plan
- Build, buy and model selection with no reseller behind it
- Team design, hiring and mentoring
- Board and investor reporting
The tools we are fluent in
We work in your stack, not ours. This is what we bring fluency in rather than a learning curve you pay for.
Reporting & BI
Modelling & context
Product analytics
AI & retrieval
Evals & observability
Platform
Languages
Not on the list? Ask. Most of this transfers, and we will tell you plainly when it does not rather than learning on your budget.
Which of these is your bottleneck?
Bring the symptom, not the solution. Half the time the thing that is broken is not the thing people think is broken, and half an hour is usually enough to tell the difference.
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