We built variA/Bly because shipping AI on hope is not a strategy.
Most AI teams ship prompts and models based on shallow eval - fluency, relevance, a thumbs up. Then they find out from support tickets what they actually shipped. We built variA/Bly to close that gap: deep evaluation, real experiments, audit-grade confidence before the response reaches a customer.
AI evaluation is blind.
Most evaluation tools score the response in isolation - they see the prompt and the output. They never check whether claims exist in retrieved chunks, whether citations are fabricated, or whether turn 5 contradicts turn 2. Hallucinations pass. Drift goes undetected. Teams ship changes based on gut feeling.
Deployments need audit-grade evaluation.
Shipping AI changes on shallow metrics produces inconclusive results - both variants score 0.91, and you pick one on gut feeling. Regulated deployments need evaluation that scores every response against retrieved context, verifies claim grounding, detects hallucinations, and produces a deterministic audit trail every reviewer can defend.
The evaluation layer your AI deployments have been missing.
Observability tells you what happened. variA/Bly tells you whether the answer was grounded. We combine deterministic, context-aware RAG evaluation with production scoring - so every response is verified against retrieved chunks, grounded claims, and full interaction history. The result: an audit trail your compliance reviewer can defend.
Built for focus.
Clear decisions, clear outcomes.
Context-aware depth
Every evaluation scores against retrieved chunks, source documents, and conversation history. Not just prompt and response.
Evaluation-first
Every AI change needs deterministic grounding evidence before it ships. Shallow benchmarks produce shallow decisions.
Developer-first
Clean APIs, production-ready SDKs, and tools that integrate into existing workflows. Get started in 10 minutes.
Measurable impact
We measure success by outcomes - hallucinations caught, audit trails defended, and deployments shipped with reproducible confidence.
The future of AI development is context-aware, audit-grade evaluation.
As RAG pipelines and multi-turn AI systems become the norm, evaluation must go deeper than surface metrics. Variably is building the infrastructure that scores every change against the full context that produced it - retrieved chunks, grounded claims, interaction history. Every change tested, every claim verified, every decision backed by data.
Ready to ship AI you can defend?
Deterministic evaluation gives you the audit trail behind every response. Start with 1,000 free credits.
1,000 free credits.