Predictive analytics
We train gradient-boosted and deep-learning models on your historical data to forecast demand, churn, or equipment failure. Most clients see usable predictions within four weeks of handing over their first dataset.
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What we build
Each engagement starts with your data, not our product catalogue. Here are the areas where we have the deepest bench strength.
Document automation
Invoices, contracts, medical forms: our NLP pipelines extract structured fields from scanned or digital documents at 97%+ accuracy. We integrate directly into your ERP or case-management system via REST API.
Computer vision
Quality inspection on production lines, shelf-gap detection in retail, safety-helmet compliance on construction sites. We fine-tune detection models on as few as 200 labelled images, then deploy to edge hardware or cloud endpoints.
Data strategy and pipelines
Before any model can work, the data needs to be clean, joined, and flowing. We design ETL pipelines in Python and dbt, set up feature stores, and write the monitoring dashboards that keep everything honest over time.
LLM integration and fine-tuning
We connect large language models to your internal knowledge base using retrieval-augmented generation. Customer-support bots, internal search, contract summarisation: we pick the right model size so you are not burning money on tokens you do not need.
How an engagement unfolds
We follow a fixed rhythm. Every project passes through these stages, though the duration of each depends on data readiness and problem complexity.
Week 1: problem scoping
We sit with the people who own the problem, not just the IT team. By Friday we produce a one-page brief that defines the target metric, the data sources, and the success threshold.
Weeks 2–3: data audit and baseline
Our engineers connect to your databases, assess quality, and build a simple baseline model. If the baseline already hits 80% of your target, we tell you, and you can stop there.
Weeks 4–6: model development
We run experiments tracked in MLflow, share results every Friday, and let your domain experts challenge the outputs. No black-box surprises at the end.
Week 7: deployment and monitoring
The model goes live behind an API or inside your existing application. We set up drift-detection alerts so you know the moment performance degrades.
Ongoing: support and retraining
Models decay as the world changes. We retrain on fresh data, tune thresholds, and add new features when your business needs evolve. Retainer clients get a guaranteed 24-hour response SLA.
Industries we know well
Artificial Intelligence is only useful when someone understands both the algorithm and the domain. Our team has delivered production systems in these verticals.
Logistics and supply chain
Route optimisation for a 400-vehicle fleet reduced fuel spend by 11% in six months. Demand forecasting for a 3PL warehouse cut overstock by £2.1m annually.
Financial services
Fraud-detection model for a payments processor flagged 94% of confirmed fraud while keeping false-positive rates under 0.3%. Deployed on AWS Lambda with sub-200ms latency.
Healthcare and life sciences
Automated extraction of clinical-trial endpoints from 12,000 PDF protocols. Processing time dropped from 14 person-days to 45 minutes per batch.
Retail and e-commerce
Recommendation engine for a fashion retailer increased average basket value by 8.4%. The model runs on-device in the mobile app, so it works offline in stores with poor signal.
Common questions
It depends on the task. For tabular prediction, a few thousand rows with clean labels is often enough. Computer vision usually needs 200–500 annotated images per class. If you have less, we can sometimes augment or use transfer learning, but we will be upfront about the accuracy ceiling.
A scoping sprint is £4,500. Full model-development engagements range from £18k to £65k depending on complexity, data volume, and deployment requirements. We quote fixed price after the scoping sprint, so there are no surprise invoices.
Yes. We deploy on AWS, GCP, and Azure. If you run on-premises Kubernetes clusters, we handle that too. We adapt to your infrastructure, not the other way around.
You do. All model weights, training code, and documentation transfer to you at project close. We retain no licence to your data or models. Our standard contract spells this out in clause 7.
We define a success threshold during scoping. If our best model falls short after the development phase, you pay only for the scoping sprint and the hours logged, not the full project fee. We have invoked this clause twice in 73 projects.
Talk to us
Describe what you are trying to achieve. We read every message and reply within one working day.
Office
82 Aspen Close, London EC1V 9NR, Greater London, United Kingdom
82 Aspen Close, London EC1V 9NR, Greater London, United Kingdom
Phone
+44 20 7824 9201
+44 20 7824 9201
Email
[email protected]
[email protected]
