We build Artificial Intelligence that actually ships

Most AI projects stall between prototype and production. We take yours from messy data to a live, monitored system your team can rely on. Based in England, working with companies that have real operational problems to solve.

Talk to an engineer about your data
Data engineer working on neural network architecture in a modern office
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Models in production
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What we build

Every engagement starts with a question you need answered, not a technology you want to try. Here are the categories we work in most often.

Predictive analytics

Demand forecasting, churn scoring, pricing optimisation. We train on your historical data, validate against hold-out periods, and deploy behind an API your existing systems can call. Typical turnaround from kick-off to first live predictions: eight weeks.

Document intelligence

Invoices, contracts, medical letters: we extract structured fields from unstructured documents using a mix of OCR and fine-tuned language models. One logistics client cut manual data entry by 74% within three months.

Anomaly detection

Fraud flags, sensor drift, network intrusion. We build streaming pipelines that score events in under 200ms and push alerts to your ops dashboard or Slack channel. False-positive tuning is included in every contract.

Conversational AI

Internal knowledge bots, customer-facing assistants, voice-to-action agents. We use retrieval-augmented generation grounded in your own documentation so the model answers from facts, not hallucinations.

Data engineering

Before any model can work, the data pipeline needs to be reliable. We design extraction, transformation, and loading workflows on cloud platforms you already pay for, avoiding vendor lock-in wherever possible.

How a project moves forward

We have run this sequence enough times to know where the real risks hide. The order matters.

1. Data audit (week 1)

We sit with your team, look at the raw data, and write a frank assessment. Sometimes the answer is "you need six more months of clean collection before a model will help." We would rather say that upfront than bill for a doomed prototype.

2. Proof of concept (weeks 2–4)

A narrow experiment on a single use case. We pick the one with the clearest success metric and the least integration friction. You see results in a notebook, not a slide deck.

3. Production build (weeks 5–9)

The model gets containerised, tested against edge cases, and wired into your existing stack. We handle monitoring, alerting, and retraining schedules so the system does not silently degrade.

4. Handover and support

Your engineers receive full documentation, access to the training pipeline, and a 90-day support window. After that, you can run it independently or keep us on a lightweight retainer.

Results from real projects

We cannot name every client, but here are anonymised snapshots from the past two years.

Reduced returns by 31%

E-commerce, 12,000 SKUs

A fashion retailer asked us to predict which orders were most likely to be returned before dispatch. The model flagged high-risk orders so the warehouse could add extra quality checks. Return rate dropped from 28% to 19.3% over one quarter.

Invoice processing in 4 seconds

Logistics, 800 suppliers

Manual entry of supplier invoices was costing 1.5 FTE. Our document extraction pipeline now reads each PDF, extracts 14 fields, and pushes them into the ERP system. Accuracy sits at 96.2%, with human review only for low-confidence extractions.

Fraud alerts cut by 60%

Financial services

The existing rule-based system generated too many false positives. We trained a gradient-boosted model on two years of labelled transactions. Genuine fraud detection stayed flat; false alerts fell from 420 per day to around 170.

Internal knowledge bot for 300 staff

Professional services

Consultants were spending 40 minutes a day searching SharePoint. We built a retrieval-augmented chatbot grounded in 11,000 internal documents. Average search-to-answer time went from 8 minutes to under 30 seconds.

AI engineering team collaborating around a whiteboard

A small team, on purpose

Professional AI Core is nine people. Four machine-learning engineers, two data engineers, one designer who handles front-end interfaces, one project lead, and one person who keeps the finances straight.

We stay small because AI projects fail most often at the communication layer, not the algorithm layer. When the person who built the model is the same person on your Monday standup call, misunderstandings get caught early.

Our office is in Old Quitzon-Blockley, about 40 minutes from central London by rail. Most client work happens remotely, but we are happy to be on-site for kick-offs and data-access sessions.

Common questions

If yours is not here, the contact form below goes straight to an engineer.

It depends on the problem. For tabular predictions like churn or demand, a few thousand labelled rows is usually enough to start exploring. Document extraction can work with as few as 200 annotated samples if the layout is consistent. We will tell you honestly during the data audit if there is not enough signal yet.
A proof-of-concept phase runs between £8,000 and £18,000 depending on data complexity. A full production build, including deployment and monitoring setup, typically falls in the £25,000–£60,000 range. We quote fixed prices after the data audit so there are no surprises.
Both, depending on what makes sense. For language tasks where data privacy is critical, we fine-tune open-weight models that run on your own infrastructure. For less sensitive workloads, commercial APIs can be faster to deploy and cheaper to maintain. We lay out the trade-offs in writing before choosing.
Yes. We have deployed on AWS, Azure, and GCP. If you run on-premises hardware, we can work with that too, though GPU availability may limit model size. We avoid introducing new vendors unless there is a clear technical reason.
Every system we deliver includes automated performance monitoring. If accuracy drifts below an agreed threshold, the pipeline triggers a retraining run on fresh data and notifies your team. During the 90-day support window, we handle this directly. After that, your engineers can manage it using the runbooks we provide.

Get in touch

Describe the problem, not the solution. We will figure out whether AI is the right tool and get back to you within two working days.

Where to find us

5 Ondricka Rise, Old Quitzon-Blockley, SL5 6GS, England, United Kingdom

Phone: +44 344 751 5207

Email: [email protected]

Aerial view of Old Quitzon-Blockley, England