Generative AI
LLM applications
Copilots and assistants, document intelligence, retrieval-augmented search and summarisation on frontier models — Claude, GPT, Gemini — or open-weight models you host yourself.
Pillar 01 · Lead offering
AI & ML Solutions that leave the notebook.
We design, build and operate intelligent systems on your data and today's best models — with the evaluation, monitoring and guardrails that keep them accurate, safe and affordable once real users arrive.
What we build
Five capabilities. One production standard.
Agents
Agentic workflows
Multi-step AI agents that execute business processes end-to-end — triage, research, drafting, data entry — with tool access, approvals and human oversight built in.
Predictive ML
Decision models
Forecasting, churn, pricing, credit and fraud risk, recommendation and anomaly detection — tuned to your data and wired into the systems where decisions are made.
Vision & speech
Perception systems
Visual inspection, document OCR, video analytics, speech-to-text and voice interfaces for the moments where data isn't text yet.
MLOps
AI platforms & operations
Pipelines, evaluation harnesses, monitoring, cost control, prompt and model versioning, and responsible-AI guardrails so what we ship stays good.
Enablement
Model portability
Abstractions and evals that let you switch vendors or move to open-weight models when price, privacy or performance demands it.
How we deliver
Pilot in weeks. Production in months. Measured throughout.
Every AI engagement runs on the same four-step model: a short discovery, a time-boxed pilot against an agreed metric, a production build integrated with your stack, and ongoing operation. You see working software by week six, not a deck.
- Evaluation first. We define how "good" is measured before the first prompt is written.
- Your data stays yours. Private deployments, vendor data-use opt-outs, PII handling by default.
- Humans in the loop wherever a wrong answer has a cost.
- Cost visibility. Token and compute spend tracked per feature from day one.
- Hand-over ready. Documentation, runbooks and training so your team can own it.
What you get
Deliverables, not demos.
Discovery
Use-case backlog ranked by value and feasibility · data readiness report · pilot plan with success metric and budget.
Pilot
Working system on your data · evaluation report against the agreed metric · go / no-go recommendation with production estimate.
Production
Deployed, integrated system · test suite and eval harness · monitoring dashboards · security review · documentation and training.
Questions we hear
Straight answers.
Which models do you use?
Whichever fits the job. We build on Claude, GPT and Gemini for most generative work and on open-weight models where privacy, latency or cost demand it. Our evaluation harness lets you compare and switch.
Can this run inside our own cloud or on-premise?
Yes. We deploy to your AWS, Azure or GCP account, or to on-premise infrastructure for regulated workloads.
What if the pilot shows AI isn't the answer?
We say so. A pilot that stops a bad investment is a successful pilot. You keep the data-readiness work and the backlog either way.
How do you price?
Fixed scope and price for discovery and pilots. Build and run are time & materials or, where the metric is clean, outcome-linked.
Related
The other two pillars.
Have a use case in mind?
Bring it to a 30-minute discovery call. We'll tell you honestly whether it's worth building.