Demonstration
Agent Control Flow
A representative agent receives a request, retrieves approved context, selects from permitted tools, validates the proposed action and escalates when policy or confidence requires human review.
Demos & Results
Inspect the system, the controls and the evaluation method.
This page uses demonstrations and representative systems until permissioned client evidence is available. Demonstrations are not presented as client results.
Evaluation Artifact
Knowledge Quality Matrix
A test set separates retrieval relevance, source grounding, answer completeness and refusal behavior so failures can be diagnosed instead of hidden in a single score.
Reference Architecture
Document Intelligence Pipeline
Ingest → classify → extract → validate → review exceptions → write approved output to a downstream system, with evidence retained for evaluation.
Representative Workflow
Bounded agent decision loop.
REQUEST → POLICY CHECK → APPROVED CONTEXT → MODEL DECISION
→ ALLOWED TOOL? → VALIDATE INPUT → EXECUTE / REQUIRE APPROVAL
→ VERIFY RESULT → LOG OUTCOME → RETURN / ESCALATE
→ ALLOWED TOOL? → VALIDATE INPUT → EXECUTE / REQUIRE APPROVAL
→ VERIFY RESULT → LOG OUTCOME → RETURN / ESCALATE
Representative architecture only. Actual controls depend on the workflow, data and risk.
What We Measure
Evidence is specific to the system.
Task success and quality
Acceptance criteria are defined for the actual use case before scaling.
Retrieval relevance and grounding
Acceptance criteria are defined for the actual use case before scaling.
Tool-use success and exceptions
Acceptance criteria are defined for the actual use case before scaling.
Latency and cost
Acceptance criteria are defined for the actual use case before scaling.
Human review and escalation
Acceptance criteria are defined for the actual use case before scaling.
Adoption and workflow impact
Acceptance criteria are defined for the actual use case before scaling.
Future Evidence
Client results will appear only when documented and permissioned.
Case studies, logos, testimonials and performance metrics should be published only when they are real, properly contextualized and authorized for public use.
Start with the problem
Have a problem AI might solve?
You do not need a specification. Tell us what you are trying to improve.