Controlled AI
Give AI a clear job, limited tools, source grounding, and a human boundary.
Nigat designs assistants and agents that retrieve, draft, route, and follow up inside a defined workflow with permissions, evaluation, and auditability.
The operating problem
The business wants to use generative AI, but generic chatbots are unreliable, disconnected from real work, and difficult to govern.
The first goal is not a platform. It is a reliable path from the current evidence to a better decision or completed workflow.
What changes
- A narrow workflow with a named owner and success metric
- Permission-aware access to approved sources and tools
- Human approval for consequential actions
- Evaluation, cost, latency, and incident visibility
Use cases
Where this capability earns the right to stay.
Examples are illustrative and must be validated against the customer’s systems, data, and economics.
Missing-document requests and case follow-up
Drafting reports, messages, and evidence packages
Internal triage, routing, and next-action support
Delivery path
From current state to an operated system.
- 01
Define purpose and human boundary
- 02
Map identity, data, and tools
- 03
Build evaluation scenarios
- 04
Pilot with approvals
- 05
Operate and re-test
Engagement limits
Four limits apply to every Nigat engagement regardless of capability, and they are stated once rather than restated on each service page.
Read the full boundariesStart with the operating problem
Show us where the work slows down.
You do not need a polished technical brief. Describe the decision, handoff, report, document workflow, or AI initiative that is harder than it should be.