The problem we keep seeing
Most mid-size companies know they should be using AI somewhere. They have years of customer data sitting in spreadsheets or legacy databases. They hear competitors talking about machine learning. But the gap between "we should do something with AI" and a running system that actually saves money feels enormous.
Hiring a full data-science team is expensive. Off-the-shelf tools promise everything and deliver generic dashboards. Internal IT teams are already stretched thin keeping the lights on.
What we do differently
We work as an embedded AI partner. Our engagements start with a two-week diagnostic: we look at your data, talk to the people who use it daily, and identify the three or four places where a trained model will have the highest financial impact. Then we build, test, and deploy those models inside your existing stack.
You get a working system, not a report. If the model does not beat the baseline we agree on, you do not pay for the build phase.
How an engagement works
Four stages, each with a clear deliverable you can evaluate before we move on.
Data audit
We connect to your data sources, profile quality, and flag gaps. Takes five to ten working days. You receive a written data-readiness report with a risk score for each proposed use case.
Prototype model
We build a minimum viable model on a sample of your data and benchmark it against your current process. This usually takes two to three weeks. If the numbers are not promising, we stop here and you owe nothing beyond the audit fee.
Production deployment
The validated model gets containerised and connected to your live systems through APIs or batch jobs, depending on latency needs. We handle monitoring, retraining schedules, and alerting.
Ongoing support
Models drift as data changes. We provide monthly performance reviews, retraining when accuracy drops below threshold, and quarterly strategy calls to identify new opportunities.
What we build
Six areas where we have delivered measurable results for UK businesses in retail, logistics, finance, and professional services.
Demand forecasting
Time-series models trained on your sales, weather, and event data. One retail client reduced overstock by 22% in six months. We support Prophet, LSTM, and gradient-boosted approaches depending on data volume.
Document processing
Invoices, contracts, compliance forms: we train extraction pipelines that pull structured data from PDFs and scanned images. Average accuracy above 96% on English-language documents after fine-tuning on your templates.
Churn prediction
Identify which customers are likely to leave before they do. We combine behavioural signals, support-ticket sentiment, and usage patterns into a single risk score your account managers can act on weekly.
Natural language search
Let your internal teams or customers search product catalogues, knowledge bases, or policy documents using plain English. We deploy retrieval-augmented generation on your own corpus, hosted on your infrastructure or a UK cloud region.
Image and video analysis
Quality control on production lines, vehicle damage assessment for insurers, shelf-compliance checks for FMCG brands. We fine-tune detection models on as few as 200 labelled images from your environment.
Data pipeline architecture
Before any model can run, data needs to flow reliably. We design and implement ETL pipelines, feature stores, and monitoring dashboards so your AI systems have clean, timely inputs every day.
Questions we hear often
Let us know what you are working on
Whether you have a clear use case or just a hunch that your data could be doing more, we are happy to talk it through. The initial call is free and takes about half an hour.