Predictive analytics
We build forecasting models for demand, churn, pricing and maintenance scheduling. Most clients see usable accuracy within the first two-week sprint because we focus on the 20% of features that carry 80% of signal.
Artificial Intelligence that earns its place on your balance sheet
We design, train and deploy AI models for mid-size businesses in the UK. If a task involves repetitive decisions, messy data or slow human review, we can probably automate most of it within eight weeks.
Show me what's possible
73%
Average time saved on document review
12
Industries served since 2021
4.2m
Predictions made monthly across client systems
8 wks
Typical first deployment
What we build
Each project starts from your data and your bottleneck, not from a product brochure.
Document intelligence
Invoices, contracts, claims forms, compliance filings. We train extraction pipelines that pull structured fields out of PDFs and scanned images, then push them straight into your ERP or case-management system. Error rates typically drop below 2%.
Natural language processing
Sentiment analysis on support tickets, automatic categorisation of feedback, and internal search that actually understands questions. We fine-tune large language models on your own corpus so the answers stay relevant to your domain.
Computer vision
Quality inspection on production lines, shelf-stock monitoring in retail, and safety-compliance checks on construction sites. We deploy models that run on-device or in the cloud, depending on latency requirements and your existing camera hardware.
AI strategy and audits
Not sure where to start? We run a two-day audit of your data estate, map every manual decision point, and hand you a ranked list of opportunities with estimated ROI and implementation complexity. No code written until you say go.
How a project moves
Five stages, clear milestones, no mystery.
1
Discovery call
We listen for 45 minutes. You describe the pain; we ask about data sources, volumes and current tooling.
2
Data review
Our engineers access a sample of your data under NDA and assess quality, gaps and labelling needs within five business days.
3
Prototype sprint
Two weeks, one focused model. You see real outputs on real data before committing to a full build.
4
Production build
We harden the model, add monitoring, write integration code and deploy into your infrastructure or a managed cloud.
5
Ongoing tuning
Models drift. We retrain on fresh data monthly or quarterly, depending on your domain's pace of change.
Results from recent work
Two snapshots from the past year.
Logistics firm, East Midlands
This company processed around 14,000 delivery notes per week by hand. We trained an OCR-plus-NLP pipeline that extracts sender, recipient, weight and SKU fields with 98.4% accuracy. The ops team now handles exceptions only.
Reduced manual keying by 91%
Private healthcare group, South East
Patient no-shows were costing this group roughly £380,000 a year. We built a gradient-boosted model that predicts no-show probability 72 hours before each appointment, triggering targeted reminder calls. No-show rate fell from 18% to 7% within four months.
£220k annual savings in recovered appointments
Common questions
How much data do we need before AI is worth trying?
It depends on the task. For document extraction, a few hundred labelled examples often suffice. For predictive models, we generally want at least 10,000 historical records. During the discovery call we can estimate what's feasible with what you have today.
Do you work with data that stays on-premise?
Yes. Several of our healthcare and legal clients cannot move data off-site. We develop locally, train on your hardware or a private cloud instance, and deploy behind your firewall. Latency is usually better this way too.
What does a typical engagement cost?
A two-day strategy audit runs £3,200. Prototype sprints start at £8,500. Full production builds vary widely, from £25k for a focused single-model project to six figures for multi-system integrations. We quote fixed price after the data review stage.
Will AI replace our staff?
In our experience, no. The teams we work with usually redeploy hours saved into higher-value tasks: customer relationships, exception handling, strategic analysis. Headcount tends to stay the same; throughput goes up.
How do you handle model bias?
Every model we ship includes a fairness report that tests predictions across demographic segments your data contains. If we detect skew above an agreed threshold, we retrain with adjusted sampling or feature exclusion before go-live.
Talk to us
Describe your problem in a few sentences. We reply within one working day.
Office
410 Bobby Corner, Beahan-upon-Jacobi, England, NH0 7KX, United Kingdom
Phone
+44 1825 990809
Email
[email protected]
