Our services in detail
Every project is different, but the descriptions below give you a realistic picture of scope, timeline and deliverables.
Predictive analytics
We build models that turn your existing data into forecasts you can act on. A typical engagement starts with a data audit: we look at what you collect, how clean it is and whether the signal is strong enough to justify a model. If the data is thin, we tell you that upfront rather than wasting your budget.
Once the audit is done, we train candidate models (gradient-boosted trees, time-series methods, sometimes neural nets if the dataset warrants it) and benchmark them against a holdout set drawn from your own history. You get a report showing precision, recall and expected error margins in business terms, not just abstract metrics.
- Demand forecasting for inventory or staffing
- Churn prediction with ranked risk scores
- Delivery-window estimation for logistics teams
- Price-elasticity modelling for revenue management
Timeline: four to eight weeks from kickoff to a deployed API endpoint your systems can call.
Natural-language processing
Language is messy. Customers misspell things, use slang, mix languages mid-sentence. Our NLP pipelines are designed for real-world text, not clean academic datasets. We fine-tune transformer-based models on your domain vocabulary so the system understands that "dodgy widget" in a support ticket means the same thing as "defective component" in an engineering report.
Common applications we have delivered include automated email triage for a Glasgow-based insurance broker (reduced manual sorting by 72%), sentiment dashboards for a consumer-goods brand tracking product launches across Twitter and Reddit, and a contract-clause extractor for a mid-size law firm that cut review time from hours to minutes per document.
- Intent classification and ticket routing
- Sentiment and topic tracking across social channels
- Document summarisation and clause extraction
- Chatbot training on your knowledge base
We deliver a containerised service, an evaluation report and a retraining guide your internal team can follow.
Computer vision
Cameras are cheap. The hard part is making them see what matters. We design object-detection and image-classification pipelines for manufacturing, retail and document processing. Our team handles everything from camera placement advice to model training and edge deployment on devices like NVIDIA Jetson or Intel NCS.
For a Scottish seafood processor, we built a grading system that classifies fish by size and quality grade on a conveyor belt at 120 items per minute with 96.3% accuracy. The system paid for itself within five months through reduced manual inspection costs and more consistent grading.
- Defect detection on production lines
- Shelf-stock and planogram compliance in retail
- Document digitisation and OCR for archives
- Safety-gear compliance monitoring on construction sites
MLOps and model management
A model in a notebook is a science experiment. A model in production is a product. We bridge that gap. Our MLOps service covers containerisation (Docker, Kubernetes), CI/CD pipelines for model artefacts, automated data-drift monitoring and scheduled retraining jobs.
We set up dashboards so your team can see at a glance whether prediction accuracy is holding, whether input distributions have shifted and whether latency is within SLA. When something drifts, the system alerts the right people before customers notice.
- Docker and Kubernetes deployment
- Automated retraining pipelines triggered by drift thresholds
- Monitoring dashboards (Grafana or custom)
- A/B testing infrastructure for model variants
This service can be added to any of our other offerings, or you can bring us a model your own data-science team built and we will productionise it.
AI strategy consulting
Not sure where to start? We run a focused two-day workshop with your leadership and technical teams. Day one maps your current data assets, pain points and business goals. Day two produces a prioritised roadmap of AI opportunities ranked by expected return and implementation difficulty.
The output is a written report (typically 15 to 25 pages) with concrete recommendations, estimated budgets and a suggested sequence. No 200-slide decks full of buzzwords. We have run these workshops for organisations ranging from 20-person startups to NHS trusts with thousands of staff.
- Data-readiness assessment
- Use-case identification and prioritisation
- Build-vs-buy analysis for each opportunity
- Talent and tooling recommendations
How a typical project runs
Five phases, each with a clear deliverable. You approve each phase before we move to the next, so there are no surprises.
Discovery
Two-week sprint: data audit, stakeholder interviews, feasibility assessment. Deliverable: go/no-go recommendation.
Prototype
Four weeks: build a working proof of concept on a representative data sample. You test it against real scenarios.
Refinement
Two to three weeks: incorporate feedback, expand training data, optimise for speed and accuracy.
Deployment
One to two weeks: containerise, deploy to your infrastructure, connect to downstream systems.
Monitoring
Ongoing: dashboards, drift alerts, quarterly model reviews. Optional retainer or handoff to your team.
We price by project, not by the hour. After the discovery phase we give you a fixed quote for the remaining work. If the scope changes mid-project, we agree the adjustment in writing before any extra cost is incurred. Request a quote.
Frequently asked questions
How much data do we need to get started?
It depends on the problem. For tabular predictive models, a few thousand rows of clean historical data is usually enough for a useful prototype. Computer-vision projects need at least 500 labelled images per class, though we can help you label them. The discovery phase will tell us whether your data is sufficient or whether we need to collect more before building anything.
Can you work with our existing cloud provider?
Yes. We deploy to AWS, Azure and Google Cloud. If you run on-premises infrastructure, we can work with that too, though edge deployments sometimes need specific hardware we will spec out during discovery.
What happens if the model does not perform well enough?
The discovery phase is designed to catch this early. If the data cannot support the accuracy you need, we will tell you before you commit to a full build. If performance drops after deployment, our monitoring layer detects it and we retrain the model on fresh data.
Do you offer ongoing support after delivery?
We offer a monthly retainer that covers monitoring, retraining and minor feature additions. Alternatively, we can hand everything over to your internal team with full documentation and a training session.
Who owns the intellectual property?
You do. All custom code and trained models transfer to you upon final payment. We retain the right to use general techniques and open-source components, but your data and your specific models belong to you.