GenAI & Agents

Generative AI and agents built on your data, with controls.

Assistants, copilots, and agents connected to your systems, tested before launch, and limited to what they are allowed to do.

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The problem

A demo takes an afternoon; production takes far longer. Assistants make things up, agents get permissions no employee would have, quality is judged by anecdote, and security is asked to approve systems it cannot inspect.

How we solve it

We connect generative AI to your data with retrieval that respects access rules, test it before launch and after every change, and limit what each agent can see and do. Every deployment has an owner, a budget, and an off switch.

What we deliver

What you get.

The outcome

Generative AI that uses your data, stays within its permissions, and can be audited.

  • LLM applications: assistants, copilots, and knowledge systems
  • Retrieval-augmented generation on governed enterprise data
  • Agentic workflows and multi-agent systems with limited permissions
  • Evaluation, red-teaming, and quality measurement
  • Model selection, cost management, and vendor strategy
Accelerators

What we bring to this work.

Starting points we adapt to your data and systems. You keep what we adapt.

GenAI Evaluation Tests

Tests that measure assistant and agent quality before and after launch.

  • A reference test set built with your subject-matter experts
  • Scoring for accuracy, source use, refusals, and safety
  • Red-team and prompt-injection tests
  • Automated test runs in your delivery pipeline, with cost tracking
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Secure RAG Starter

Connect generative AI to your documents and keep existing access rules.

  • Ingestion and indexing that keep source permissions
  • Answers with citations to the source documents
  • Request logging, usage limits, and cost controls
  • Connectors for common document and collaboration platforms
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AI Agent Controls

Scope, permissions, logging, and approvals for AI agents.

  • Scope and permission templates for each agent
  • Action logging and audit trail
  • Human approval steps for high-impact actions
  • Switch-off procedure, spending limits, and escalation runbook
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How it starts

The first step.

Before building anything else, we write a test set from your own content with your subject-matter experts. Then we connect the assistant or agent to your data with your access rules intact, set its permissions and approvals, and release it when it passes the tests.

How we work
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