Your AI change passed eval and shipped last week.Are your customers actually getting better answers - or did you just hope?
Most evaluation tools score one dimension on synthetic prompts. The failures your customers actually see - wrong numbers, drifting context, hallucinated citations - slip through because they don't show up until the response is in the wild.
variA/Bly is the deterministic evaluation layer for production AI. 40+ scored dimensions, claim-level grounding, hallucination detection - same input, same score, every run. Experimentation built on top, so when you ship a change, you know whether it actually improved the system.
1,000 free credits.
Catch hallucinations, verify grounding, defend every claim with an auditable trail - powered by deterministic scoring across 40+ dimensions.
What teams measure with variA/Bly
Your eval said 91%. The hallucination still reached the customer.
Most teams score AI responses with shallow metrics - fluency, relevance, a thumbs up from an LLM judge. The score lands at 91%. The ungrounded answer still ships.
Why? Because standard eval never sees the retrieved chunks from your RAG pipeline. It never checks whether claims exist in source documents. It never tracks whether turn 5 contradicts turn 2 across multi-turn interactions.
The result? Two variants both score 91%. You ship one and hope. That's not evaluation - that's a coin flip dressed up as a metric.
Shallow evaluation makes every deployment a gamble.
The evaluation layer your AI deployments have been missing.
Observability tells you what happened. variA/Bly tells you whether the answer was actually grounded - with deterministic scoring against retrieved context, claim-level verification, and full interaction history.
Grounding Verification
Every factual claim in your RAG output traced back to retrieved chunks. Ungrounded claims flagged with the exact sentence that hallucinated.
Multi-Turn Coherence
Track consistency across multi-turn interactions. Detect contradictions, context loss, and coreference failures automatically.
Deterministic Audit Trail
Every score reproduces byte-for-byte on the same input. Per-claim grounding traces, NLI entailment scores, numeric verification - the artifact your compliance reviewer needs.
6× cleaner on the failure mode that produces compliance breaches.
We benchmarked variA/Bly head-to-head against RAGAS on the public RGB dataset. Same 592 question + answer + reference tuples, both scorers, scripts open-sourced. Here's the failure rate that matters.
When variA/Bly says “grounded”, it's right 9 out of 10 times. Built for regulated, customer-facing AI.
The LLM judge defaults to “probably verifiable” on keyword overlap — even when the passage doesn't actually support the claim.
Methodology · 12-phase journey · reproduction scripts · downloadable PDFs
Everything you need to ship better AI
Deep evaluation. Auditable decisions. Confident deployments.
Claim-Level Verification
Trace every factual claim back to retrieved source documents.
Faithfulness Scoring
Measure how accurately responses reflect the provided context.
Multi-Turn Consistency
Detect contradictions and context loss across conversation turns.
A/B Testing
Run controlled experiments with configurable traffic splits.
Quality Drift Monitoring
Track evaluation scores over time and detect degradation.
Cost vs Quality vs Time
Compare quality improvements against cost, latency, and iteration time impact.
How teams decide what to ship
Statistical confidence meets behavioral understanding.
Statistical Significance
Bayesian analysis and confidence intervals tell you when a change is real and reproducible - not noise dressed up as a metric.
Behavioral Insight
Understand not just whether a change is real, but why. See which dimensions improved and which regressed.
Cost-Quality-Time Tradeoff
Compare quality improvements against cost, latency, and iteration time. Ship the change that holds up across your priorities.
Regression Detection
Automatic alerts when a change degrades on safety, consistency, or any critical dimension - even if the overall score improves.
From observation to deployment
Five steps to context-aware AI improvement.
Observe
Log production traffic with full context — retrieved chunks, conversation history, system instructions.
Evaluate
Multi-dimensional scoring with 40+ metrics and grounding verification. Every claim checked against source documents.
Experiment
A/B test prompt, model, and retrieval changes on real production traffic with controlled splits.
Decide
Statistical significance meets behavioral insight. Know the winner with confidence.
Ship
Deploy the winner and monitor for drift. Get alerted if quality degrades.
Works with any LLM
Provider-agnostic SDK. Call your model however you want; score the response with one line. Swap models without changing your evaluation pipeline.
...and any other provider with an HTTP API. The SDK doesn't care which model produced the response.
Your data stays yours
We never use your data for model training or any other purpose beyond serving you.
No training on your data
Your prompts, evaluations, and scoring results are never used to train AI models. Ever.
Secure by default
All traffic encrypted over HTTPS. Your data is stored securely and never shared with third parties.
Full data isolation
Each organization gets fully isolated data storage with org-level access controls.
Ready to ship AI you can defend?
Deterministic evaluation gives you the audit trail behind every response.
1,000 free credits.