Automated AI Evaluation and Observability Platform

Why does AI behavior change in production?

Because LLMs are non-deterministic, and the conditions they rely on can change after deployment. K2view captures the point-in-time operational context and execution behind each interaction, so teams can investigate failures with the exact conditions that produced them.

Evaluate before deployment.
Observe and improve in production.

Evaluate AI behavior before deployment

Run representative evaluation cases against configurable dimensions and criteria. Use synthetic data, production-derived cases, or both to assess behavior before changes reach production.

Evaluate & observe every production interaction

Continuously score interactions across configurable dimensions and capture the operational context, execution path, latency, tokens, and cost. Surface low-scoring or anomalous cases for review.

Investigate failures and prevent regressions

Package problematic production interactions with their original context and configuration, replay them in a lower environment for root cause analysis, and add them to regression evaluation suites.

Building AI trust in five steps

Automate the AI validation process in a closed feedback loop, where people ultimately confirm and fix the problems, as follows:

1. Generate eval cases

Turn production data into realistic eval cases with expected responses.

2. Provision test data for each case

Mask PII in production data subsets, adding synthetic data to fill in the gaps.

3. Run and score the evaluations

An LLM-as-a-judge scores each AI output against defined metrics – correctness, relevance, compliance, tone, and any custom metrics you define – as a weighted result.

4. Observe live behavior

Customizable dashboards show you top conversation topics, customer sentiment, conversation volume over time, the overall satisfaction trend, resolution pathways, token consumption, and more – and immediately alert you to problematic interactions.

5. Feed issues back to dev

Package any issues with a full trace of the conversation including end-to-end context, the customer’s Micro-Database™, and metadata – and then ship it to a developer for immediate replay and remediation.