Data Engineering & AI Data Readiness
Most AI pilots die on data, not on models — scattered sources, silent quality failures, nothing joined. Data engineering fixes that in the right order: pipelines, models and quality checks first, AI second. The proof pattern: a multi-system pipeline whose LLM stages worked because the data reaching them was engineered — 300+ records a week, manual entry down roughly 80% in month one.
The free 30-minute session maps your data flows — where they live, how they move, what breaks silently — and marks what an AI project would trip over first. You keep the map either way.
How data kills AI projects — and quarters
The AI pilot met your data and stopped
The model was fine; the inputs were the problem — customer records in four systems that disagree, fields free-texted for years, history that means three different things by era. The fix is not a better model; it is engineering the data the model eats.
Every report is a manual expedition
Someone exports from three systems, joins in a spreadsheet, fixes the mismatches from memory and emails the result. The company runs on numbers that take days to produce and cannot be reproduced twice the same way.
The systems never agree
CRM says 4,120 customers, billing says 3,980, the warehouse says both are wrong. Without a defined source of truth and reconciliation that runs automatically, every decision starts with an argument about whose number is real.
Data quality fails silently
A partner changed a file format in March; nobody noticed until the June numbers looked odd. Quality checks that alert on arrival — schema, ranges, volumes — are cheap; quarters of decisions on bad data are not.
Every important number should have one home and one owner. Most companies have neither — that is the first fix, and the cheapest.
What we build
Pipelines that move data reliably
Ingestion from your systems, APIs and partners — scheduled, idempotent, monitored, with retries and alerting when reality misbehaves.
You get: data flowing automatically, with someone paged when it stops.
Models and a source of truth
Warehouse schemas designed for the questions you actually ask, entity resolution across systems, and one defined place where each number is true.
You get: joined, queryable data with the disagreements engineered away.
Quality gates and observability
Schema, range, freshness and volume checks on every flow, with drift alerts — so bad data announces itself instead of surfacing in a board deck.
You get: data quality as a monitored system, not an annual cleanup.
The AI Data Readiness Audit
Your data estate assessed specifically against what AI work needs: coverage, quality, joinability, permissions, freshness — with a prioritised fix plan.
You get: a written verdict on what AI you can feed today, and what it takes to feed more.
How it works
Map the estate
Sources, flows, owners and the silent failure points — from the systems and the people, spreadsheets included.
First pipeline, first truth
The most painful flow automated end to end, with its quality gates and one reconciled source of truth.
Extend and gate
Remaining flows land one by one, each with checks — reports switch from expeditions to queries.
AI on top
With the foundation holding, AI features finally get inputs they can survive on — extraction, classification, assistants.
AI is a data business wearing a model costume.
Proof, not claims
Data engineering proof is systems that keep flowing — and AI features that survived because of it.
Heavy location data made queryable
Geospatial analytics over serious data volumes — ingestion, modelling and aggregation engineered so the product could answer in seconds.
Live market data behind an operational surface
Market feeds, CRM, billing and analytics — four data rhythms reconciled behind one admin platform used all day, for years.
The LLM pipeline that cut manual entry ~80% worked for an unglamorous reason: the data reaching the model was cleaned, joined and validated before a single token was spent.
The pipeline is unglamorous and decisive: clean, joined, validated data is why our LLM projects ship while pilots elsewhere stall.
Start with the AI Data Readiness Audit
Your data estate assessed against what AI work actually needs — coverage, quality, joinability, permissions, freshness — ending in a written verdict and a prioritised fix plan. Scoped in writing on the call, priced before it starts.
The free session is a genuine subset of the audit: your flows mapped in 30 minutes, the biggest AI-blocker named. You keep the map either way.
Clients also buy
AI Strategy Consulting
A fixed-price consulting engagement, payable online: from "we should use AI" to a costed, buildable plan your board can approve.
AI Integration
AI into your existing product and workflows this quarter — scoped as product features with usage metrics attached.
Custom ERP, CRM & Operations
Systems shaped around how your company actually runs — built, run and continuously developed for a monthly fee.
Data first, AI second. Reversing that order is the most expensive mistake in applied AI right now.
The questions buyers actually ask
What does "AI-ready data" actually mean?
Five checkable properties: coverage (the data exists), quality (it is right), joinability (systems agree on entities), permissions (you may use it for this), and freshness (it arrives in time). The audit scores your estate on all five and prices the gaps.
Do we need a data warehouse?
Usually some defined source of truth, yes — but sized to your reality, which for a mid-size company is often a modest warehouse and a handful of pipelines, not a data platform programme. Over-building the foundation is its own failure mode; the fix plan stays proportionate.
Can you work with our existing BI tools?
Yes — the engineering happens under whatever your team already reads: your dashboards keep their faces and gain trustworthy numbers underneath.
How is this different from hiring a data analyst?
Analysts answer questions with data; data engineers build the system that makes answers reproducible. If your analysts spend their week exporting and reconciling, they are doing engineering work by hand — that is precisely what we automate away.
What breaks first without quality gates?
Silently changed inputs: a partner alters a file format, a field starts arriving empty, volumes drop by half. Gates catch these on arrival with an alert; without them, the discovery happens months later in a number nobody believes.
How long until AI features can sit on top?
Often weeks, not quarters: the first pipeline with quality gates typically lands within a month, and a first AI feature — extraction or classification on that flow — can follow immediately. The audit gives dates for your estate specifically.
Team, scale and run — the one-page version
The seven Teams & Scale services on one printable page: what each covers, when it is the right door, and how the first month works. Built to be forwarded to whoever holds the budget.
Feed your AI something it can survive on
Book the free 30-minute session and we map your data flows and name the biggest AI-blocker — or describe your data mess in two sentences and an engineer replies in one business day.
- 30 minutes, an engineer on the call
- You keep the written notes either way
- Nobody follows up more than once
