Predictive maintenance
Sensor data from your equipment feeds a time-series model that flags failures 48 to 72 hours before they happen. One logistics client cut unplanned downtime by 41% in the first quarter after deployment.
Artificial Intelligence that actually ships
Most AI projects stall at the proof-of-concept stage. We take yours from messy data to a production system your team uses every day, usually within twelve weeks.
Show me what my data can do
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How a project moves from idea to running system
We follow a five-phase process. Each phase has a defined deliverable so you never wonder what you are paying for.
1
Data audit
We connect to your existing databases, spreadsheets, or APIs and produce a written report within five working days. The report covers data quality, volume gaps, labelling issues, and a frank assessment of whether AI is the right tool for your problem. Sometimes it is not, and we will tell you so.
2
Feasibility model
A lightweight model trained on a sample of your data, tested against a hold-out set, and presented with precision/recall metrics you can compare to your current process. This takes two to three weeks and costs a fixed fee agreed up front. If the numbers disappoint, you walk away having spent a fraction of a full build.
3
Production build
We retrain on your full dataset, containerise the model, and deploy it behind a REST API or embed it directly in your application. Infrastructure runs on your cloud account so you own everything. Typical timeline: four to six weeks depending on data volume and integration complexity.
4
Team handover
Your engineers get a recorded walkthrough, annotated code, and a runbook covering retraining, monitoring, and rollback. We run two live Q&A sessions so nothing gets lost in documentation.
5
Ongoing monitoring
Models drift. We set up automated accuracy checks that alert you when performance drops below a threshold you choose. On a retained plan we handle retraining; otherwise your team follows the runbook. Either way, you know the moment something changes.
What we build
Scroll sideways to see the full range. Each service can run independently or combine into a larger platform.
Demand forecasting
Combines your sales history with weather, calendar, and promotional data to predict daily or weekly demand at SKU level. Typical accuracy improvement over spreadsheet methods: 18 to 25 percentage points.
Computer vision inspection
Cameras on your production line feed images to a convolutional network trained on your own defect library. False-positive rates below 2% are standard after two retraining cycles.
Document intelligence
Invoices, contracts, medical letters: our NLP pipeline extracts structured fields, classifies document type, and routes data to your ERP or CRM. Processing time drops from minutes per document to under two seconds.
Customer churn prediction
Identifies which accounts are likely to leave within the next 90 days and surfaces the three strongest risk signals for each. Retention teams stop guessing and start prioritising.
Anomaly detection
Real-time monitoring of transactions, network traffic, or process metrics. The model learns normal patterns and flags deviations within milliseconds, well before rule-based systems would catch them.
Industries we have delivered in
Each sector has its own data quirks. We have learned them the hard way so you do not have to.
Retail and e-commerce
Demand, pricing, recommendation engines
Property management
Valuation models, tenant risk scoring
Healthcare
Triage prioritisation, clinical NLP
Financial services
Fraud detection, credit risk, KYC automation
A small team, on purpose
We are nine people: six machine learning engineers, one data engineer, one designer who builds dashboards, and one project lead who keeps timelines honest. No sales department. No account managers. The person who scopes your project is the same person who writes the training pipeline.
Founded in 2019, we have kept the team tight because AI projects fail when communication layers multiply. Every client gets a direct Slack channel with the engineers doing the work.
Common questions
If yours is not here, the contact form is right below.
How much data do I need before AI is viable?+
It depends on the problem. Classification tasks like spam filtering can work with a few thousand labelled examples. Time-series forecasting usually needs at least two years of history at the granularity you want to predict. During the data audit we give you a straight answer, not a hedge.
What does a typical project cost?+
The feasibility phase is a fixed fee, usually between £4,000 and £8,000. Full production builds range from £25,000 to £90,000 depending on data complexity, number of integrations, and whether you need real-time inference or batch processing. We quote after the audit, not before.
Do you use off-the-shelf models or build from scratch?+
Both. For NLP tasks we fine-tune open-source transformer models. For tabular data we usually train gradient-boosted trees or neural architectures designed for your feature set. We pick the approach that gives the best accuracy-to-latency ratio for your use case, not the one that sounds most impressive.
Who owns the model and the code?+
You do. Everything runs in your cloud account. Source code, trained weights, and documentation are handed over at the end of the project under a perpetual licence with no usage fees.
Can you work with on-premise data that cannot leave our network?+
Yes. We have delivered projects inside air-gapped environments for financial and healthcare clients. We set up a secure development VM on your infrastructure and work through a VPN or on-site sessions as needed.
Let's figure out if AI fits your problem
Describe what you are trying to improve. We will reply within one working day with an honest assessment, not a pitch deck.
7 Dooley Lane, Buckridgeworth, England, EZ3 5LT, United Kingdom