WHAT WE DO / APPLIED AI
AI that reaches production
We help you move from pilots to governed AI running inside the systems you already use.
Most AI pilots stall for the same reasons: the demo runs on sample data, nobody owns integration or security, and nobody can say what the model touches or who approved its output. We design, build and run AI inside your systems, with your data boundaries, human approval where liability sits and evaluation against a target you set. Designs aligned to EU AI Act risk tiers, ISO/IEC 42001 and the NIST AI RMF — alignment, not certification.
Our capabilities
Claude practice
Claude enterprise adoption, Claude Code for engineering teams, agents built with Claude and secure connections to your systems.
Agentic automation
Agents for document-heavy processes such as document intake and claims, with human approval gates where liability sits.
Enterprise AI platforms
A shared foundation for every team: model gateway, routing, prompt and version control, evaluation, observability and cost control.
Knowledge assistants
Answers grounded in your documents, with citations, and retrieval scoped to what each user can already see.
AI governance
Data boundaries, audit trails, evaluation and policy controls, so the compliance team signs off rather than shuts it down.
AI-assisted engineering
Coding assistants such as Claude Code, rolled out with guardrails, review practices and honest measurement.
How it works
Human + AI in control
AI drafts, checks and routes. People approve where the decision carries liability, and every step is logged.
Human approval
- What happens
- A named person reviews the case, the agents’ reasons and any flags, then approves, amends or rejects it.
- What’s logged
- Reviewer, decision, any changes made and the time taken.
- Who approves
- The accountable person in your team, under your approval rules.
Model-neutral
Model-neutral by design
We work with Claude, OpenAI models and open-weight models, run on Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry or direct APIs. We pick the model for the task and the cloud for your estate, and keep the option to switch.
Models we build with
- Claude
- OpenAI models
- Open-weight models
Where they run
- Amazon Bedrock
- Google Cloud Vertex AI
- Microsoft Foundry
- Direct APIs
Our solutions
Forms, packs and correspondence read, classified and extracted into your systems, with exceptions routed to a person.
- Classification and field extraction with confidence scores
- Validation against your business rules
- Human review queue for low-confidence items
- Full audit trail of each document and decision
Agents that gather evidence, summarize a case and draft a recommendation, while a handler makes the decision.
- Evidence gathering across your existing systems
- Case summaries with links back to sources
- Draft recommendations for human approval
- Logged reasoning and approvals for audit
Ask a question in plain language and get a cited answer from policies, procedures and past work.
- Answers with citations to the source document
- Results limited to what each user can already access
- Feedback loop to improve answer quality
Coding assistants introduced with policies, review practices and measurement, so speed does not cost quality.
- Usage policies and repository guardrails
- Review and testing practices for AI-written code
- Measurement agreed before rollout
Outcomes
- Pilots that become production systems with an owner, a run guide and monitoring.
- Fewer manual re-keying steps in document-heavy processes.
- A clear record of what data each AI system touches and who approved its output.
- Accuracy tested on your data against a target you set before go-live.
- Freedom to change model or cloud without rebuilding.
- An honest answer when a simpler fix solves the problem better than AI.