Data audit
We sit with your engineers and inspect the actual data: schemas, volumes, gaps, labelling quality. No slide decks. We open the database and look. If your data cannot support the goal, we say so on day two.
3–5 working daysMost AI projects stall at the prototype stage. Ours don't. We take messy, real-world data and turn it into deployed systems that generate measurable returns within weeks.
Send us your toughest problemEvery engagement follows the same structure. We wrote it down so you know exactly what happens at each stage, how long it takes and what you owe us if you walk away.
We sit with your engineers and inspect the actual data: schemas, volumes, gaps, labelling quality. No slide decks. We open the database and look. If your data cannot support the goal, we say so on day two.
3–5 working daysA small team builds a working model on a representative subset. You see predictions, error rates and edge cases before we write a single line of production code. This phase has a fixed fee; if the results disappoint, you stop here.
2–3 weeksWe containerise the model, write monitoring hooks, build fallback logic and integrate with your existing APIs. Load testing happens against realistic traffic patterns drawn from your own logs.
4–6 weeksYour team gets the code, the training pipeline and a runbook. We stay on a lightweight retainer for 90 days to handle drift, retraining triggers and any edge cases that surface in live traffic.
90-day retainerWe extract structured data from invoices, contracts and regulatory filings. Our pipelines handle scanned PDFs, handwritten notes and multi-language documents with field-level confidence scores.
Fraud patterns, equipment failures, supply-chain disruptions: our models learn normal behaviour from your historical data and flag deviations in real time. Alert fatigue is a design constraint we take seriously.
Custom chatbots and voice agents grounded in your knowledge base. We fine-tune language models on your support tickets and product manuals so the bot gives answers specific to your business, not generic fluff.
Time-series models trained on your sales, weather, marketing spend and competitor pricing. We deliver daily or weekly forecasts with prediction intervals so your planning team knows the confidence range, not just a point estimate.
They reduced our mis-sort rate from 4.1% to 0.6% in eleven weeks. The model paid for itself before the retainer period ended.
— Operations director, Belfast-based fulfilment company (name under NDA)
We trained a computer-vision classifier on 23,000 labelled parcel images captured from existing CCTV feeds. The system runs on two edge GPUs mounted above the main conveyor, processing 1,200 parcels per hour with sub-200ms latency per frame.
The data audit phase is fixed at £2,400. Prototype sprints range from £8,000 to £18,000 depending on model complexity. Production builds are scoped individually; most fall between £25,000 and £70,000. We quote after the audit, never before.
No. We hand over runbooks, monitoring dashboards and retraining scripts designed for a general backend engineer. If you later hire ML specialists, the codebase follows standard MLOps conventions and will be familiar to them.
That is normal. The audit phase exists precisely to measure the gaps. Sometimes we can work around missing fields with proxy features or synthetic augmentation. Other times we recommend a short data-collection sprint before modelling begins. We will not pretend bad data can produce good predictions.
Yes. We deploy on AWS, Azure and GCP. We also support on-premises GPU clusters for clients with data-residency requirements. Our container images are cloud-agnostic by default.
You do. Every model weight, training script and pipeline config produced during the engagement belongs to you. We retain no licence to your data or derived artefacts after the contract ends.
We read every message. Expect a reply within one working day. If your problem is urgent, call us directly.
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Effective from 15 March 2026.
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Last revised: 2026-03-15.
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Published 15/03/2026.
Performance figures, accuracy percentages and timelines mentioned on this website reflect past project outcomes and are not guarantees of future results. Every AI project depends on the quality and volume of available data, and results will vary.
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