Automated AI Evaluation and Observability

Why do AI applications pass evaluations in development but still fail in production?

LLMs are non-deterministic and, in production, the data, context, and prompts keep changing. K2view continuously observes, captures, and replays the full trace of any interaction to pinpoint root cause and fix production issues fast.

Ensure AI reliability in production

Prove your AI works before production

Evaluate every AI interaction against your own criteria and metrics, on realistic eval cases built from production data, so problems are caught in evaluation and not by customers.

See what your AI is doing in production

Observe and visualize business behavior patterns, quality, token usage, and performance trends via dashboards that instantly surface problematic interactions requiring further investigation.

Find and fix any issues immediately

Replay any problematic interaction with its full trace, pinpoint the root cause, fix it, and package it as a regression test to prevent recurrence.

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.