Scope the uncertainty
Define the workflow, the technical uncertainty, the data boundary, and the evidence needed before deployment.
Assurance Sprint
A focused technical engagement, not open-ended consulting.
A focused technical engagement for teams building AI agents or sensitive AI workflows. Zansn defines the evaluation protocol, instruments the workflow, runs repeated tests, captures failures, measures cost and latency, tests security and privacy boundaries, and delivers an evidence pack that can guide architecture, deployment, and further R&D.
Start an assurance sprintHow it runs
Instrument the workflow
Wire up the evaluation protocol, agent trace layer, and security/privacy boundary tests around the real system.
Run repeated experiments
Execute seeded benchmark loops, capture failures, and measure cost, latency, and reliability under controlled conditions.
Deliver the evidence pack
Package traces, metrics, failure taxonomy, and a deployment-readiness view the team can review before scaling.
Sprint outputs
- 01Evaluation protocol
- 02Benchmark suite
- 03Agent trace map
- 04Failure-mode taxonomy
- 05Prompt injection and tool misuse tests
- 06Privacy boundary report
- 07Local/cloud cost and latency profile
- 08Deployment-readiness evidence pack
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