A prototype is not a production system.
Production AI needs real data, permissions, evaluation and monitoring. Most prototypes have none of these.
- Answers that can’t be traced to a source
- No evaluation, so quality is a guess
- Access rules ignored in the prototype
- No monitoring once users arrive
Retrieve, act, evaluate.
We connect and prepare the documents and data the use case needs.
- Document ingestion and parsing
- Retrieval with source evidence
- Access control per user
Agents run the workflow, with people reviewing the key steps.
- Agentic workflows
- Human review points
- Integration with your systems
Quality is measured before and after launch.
- Evaluation datasets
- Quality and cost monitoring
- Audit trail
Answers link to the evidence they came from.
Quality measured against agreed test sets.
Claude, platform models or open-source models.
Results in production
Questions we hear
Which models do you use?+
Claude, the models on your platform (Bedrock, Cortex, Vertex AI, Agent Bricks) or open-source models you host.
How do you measure quality?+
With evaluation datasets agreed at the start and monitored after launch.
Can it run on-premises?+
Yes, with open-source LLMs on your own NVIDIA GPU servers. See on-prem RAG & AI agents.
Where does it run, and who controls it?+
Everything we build for you is yours: pipelines, data models, infrastructure code and documentation, in your own cloud account and repositories. Our agents and accelerators come with a licence that keeps working even if you stop working with us. If you need full source access, we offer that too.
Put your AI use case into production.
Tell us the use case and the platform you run.