Generative AI and LLM solutions
Copilots, retrieval-augmented generation (RAG) pipelines, and fine-tuned language models grounded in your own data and deployed securely into the tools your teams already use.
All servicesAI and ML services
AI that earns its place in production.
Strategy, generative AI, custom models, and MLOps, delivered by one team from the first use-case workshop to the system your people rely on every day.
A model that only works in a demo is a cost. We design, build, and run AI systems that keep performing once real users and real data arrive.
Capabilities
The full lifecycle, in one practice.
From a focused proof of concept to a production platform: strategy, build, deployment, and governance.
AI strategy and roadmapping
High-value use cases identified, data readiness assessed, and a phased roadmap tied to business outcomes.
Custom ML models
Predictive and recommendation models built end to end, from feature engineering to production rollout.
Computer vision
Image and video analysis for inspection, document processing, and visual search, tuned for real-world conditions.
Natural language processing
Classification, extraction, and conversational AI that turn documents and conversations into structured data.
Intelligent automation and agents
Agentic workflows that combine language models with your systems and APIs to automate multi-step processes.
MLOps and cloud deployment
Reproducible training pipelines, model versioning, and monitoring, so models keep performing after they ship.
Governance and responsible AI
Model risk assessment, bias testing, and documentation that hold up to audit and regulatory scrutiny.
Enablement and training
Training, documentation, and hand-over so your teams can run, maintain, and extend what we build together, without depending on us.
How it runs
From idea to production.
We prove value before we scale it, so investment follows evidence.
- Step 01
Discover and prioritise
Workshops surface candidate use cases, then a shortlist ranked by value, feasibility, and data readiness.
- Step 02
Prove value
A short pilot on your data, with success criteria agreed before it starts.
- Step 03
Build and integrate
Production-grade models and applications delivered into your systems with tests, monitoring, and MLOps.
- Step 04
Operate and improve
Monitoring, retraining, and governance reviews so performance holds as data and regulation change.
Responsible by design
AI you can explain, measure, and trust.
The practices that separate a production system from an experiment.
Grounded in your data
Models and retrieval pipelines are built on your own data, inside environments you control.
Measured before it scales
Every pilot has success criteria agreed up front. If it does not meet them, we say so.
Observable in production
Accuracy, drift, latency, and cost are monitored, with clear owners when something changes.
Governed from day one
Risk, bias, and privacy reviews are part of discovery, not a hurdle before launch.
Use cases
Where AI pays off by industry.
Typical starting points. Each industry page has more on the systems and regulation involved.
Fintech
Fraud monitoring, credit scoring, AI-assisted loan decisions, and document processing for onboarding and KYC.
Fintech industry pageHealthcare
Clinical document processing, predictive analytics for patient outcomes, and patient-facing assistants.
Healthcare industry pageLogistics
Demand forecasting, route optimisation, predictive maintenance, and document extraction for customs.
Logistics industry pageRetail and manufacturing
Demand forecasting, visual quality inspection, and recommendation and personalisation engines.
Retail and manufacturing industry pageHi-tech and media
Content recommendation, tagging and search, and moderation for media catalogues and communities.
Hi-tech and media industry pageGaming
Game AI and NPC behaviour, adaptive difficulty, and player analytics for balancing and retention.
Gaming industry pageAI we run ourselves
Tested on our own product first.
SkillDoom, our skill-management platform, is built and operated with the same model-development and MLOps practices we bring to client work.
Have a use case in mind?
Tell us about the decision, process, or product you want AI to improve, and we will suggest how to prove it quickly.