Agentium Probe
Know what your AI actually delivers.
Asynchronous evaluation of completed AI conversations. Trace findings back to source turns and inspect supporting evidence.
Why aggregate accuracy percentages hide critical conversational failures
Deploying generative AI into customer-facing products without granular, asynchronous evaluation is a blind risk. A model boasting a '94% benchmark accuracy' can still hallucinate pricing terms, leak confidential context, or offer confusing instructions on turn 5 of a customer interaction.
- Aggregate benchmarks obscure edge-case failures in multi-turn conversations.
- Manual inspection of thousands of logs is completely unsustainable for small teams.
- Synthetically generated evaluation scores lack verifiable ground-truth evidence.
Who Probe is designed for
The three-step inspection workflow
Asynchronous Session Ingestion
Completed multi-turn production transcripts and synthetic test runs are ingested into an isolated, zero-exposure evaluation pipeline without impacting production latency.
Evaluate Against Domain Criteria
Transcripts are scored against strict domain criteria: Grounding Integrity, Hallucination Prevention, Protocol Adherence, and Tone Calibration.
Inspect Evidence-Backed Findings
Every defect surfaces a granular finding linked to the exact turn index, the violated rule, and an isolated transcript snippet for rapid human review.
Granular Conversation Finding
Inspectable evaluation report from session transcript #eval-9842
Note: Every finding links the conversation, the evaluation rule, and the verifiable ground-truth evidence together.
Current Availability
Internal dogfooding & partner testing: Pilot rollouts scheduled across companion and enterprise SaaS apps.
We evaluate synthetic conversation datasets and export human-reviewable evaluation sheets.