Eval@CoreContinuous AI & LLM evaluation
The AI evaluation flywheel that catches drift before users do.
An agent is only as good as its evaluations. Eval@Core is a closed-loop evaluation flywheel for AI agents and LLM applications that prevents undetected drift, hallucinations, and quality regressions from ever reaching production. Built on best-in-class open tooling — Ragas, LangSmith, Phoenix, Langfuse, DeepEval, TruLens, and OpenAI Evals — it makes quality verifiable, repeatable, and auditable across the entire AI SDLC.
Faithfulness · relevance · context precision/recall
Trajectory · tool-call effectiveness · step efficiency
Hallucination · toxicity/PII · prompt-injection robustness
Latency p50/p95/p99 · cost/query · drift · success rate
Capabilities
What Eval@Core does.
Four-stage continuous loop
Trace every invocation, tool call, and retrieval with payload, latency, and cost. Verify outputs against ground truth with pointwise and pairwise methods. Score against use-case metrics. Retrain by feeding findings back into prompts and tuning.
Comprehensive metric coverage
Measure quality across RAG, agents, safety, and operations — from faithfulness and trajectory correctness to hallucination rate, PII leakage, latency percentiles, and cost-per-query.
Bring your own tools (BYOT)
OpenTelemetry-compatible integration that drops into your existing stack instead of replacing it — Ragas, LangSmith, Phoenix, Langfuse, DeepEval, TruLens, and OpenAI Evals.
LLM-as-judge + human-in-the-loop
Combine automated judging with human labeling for production-grade evaluation you can defend in front of auditors and regulators.
Custom scorers
Domain-specific scorers for compliance, financial accuracy, medical safety, and any bar your use case demands.
CI/CD evaluation gates
Offline evals in development, gates that fail builds on score regression, A/B comparison in production, and continuous drift and regression alerting.
How it works
4 stages, one accountable loop.
- 1
Trace
Full logging of invocations, tool calls, and retrievals — with payloads, latency, and cost captured end to end.
- 2
Verify
Validate outputs against ground-truth datasets using pointwise and pairwise evaluation methods.
- 3
Score
Quantitative metrics aligned to the requirements of your specific use case.
- 4
Retrain
Findings feed back into prompt refinement and model tuning, closing the loop between monitoring and improvement.
Benefits
Why teams choose it
- Catch performance drift before users experience it
- Stop hallucinations from reaching production
- Make quality verifiable and auditable
- Establish repeatable, production-grade evaluation patterns
- Close the feedback loop between monitoring and improvement
Use cases
Where it fits
- RAG systems that require source-fidelity verification
- Multi-step agent workflows needing trajectory validation
- Safety-critical applications in medical, financial, and regulatory domains
- Production systems requiring continuous quality monitoring
FAQ
Common questions
Does Eval@Core replace my current observability stack?+
No. It is bring-your-own-tools and OpenTelemetry-compatible, so it integrates with the tracing, logging, and eval tools you already run rather than replacing them.
Can it run inside CI/CD?+
Yes. Eval@Core provides CI/CD gates that fail builds on score regression, plus offline evaluation during development and A/B comparison in production.
How are domain-specific requirements handled?+
Custom scorers let you encode the exact bar your domain requires — compliance, financial accuracy, or medical safety — alongside LLM-as-judge and human-in-the-loop labeling.
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ExploreReady to deploy Eval@Core?
We’ll map Eval@Core to your stack, constraints, and compliance requirements — and keep humans in command.
Talk to humaineeti