Ship AI changes with evidence, not vibes.
One line of code turns on OpenTelemetry-native observability, calibrated evaluation, and human-in-the-loop quality gates — from dev through production.
Hallucinations, regressions, drift, cost blowouts, jailbreaks, runaway verbosity — surfaced, scored, and flagged before a user ever runs into them.
Ungrounded claim, not in retrieved context.
Prompt v4 quietly dropped quality vs v3.
Input distribution shifted off the golden set.
Retries tripled token spend per call.
Adversarial probe slipped past the guardrail.
Answers padded well past what users read.
evalOS sits on an OpenTelemetry foundation, so it captures everything — LLMs, vector DBs, even GPUs — from a single SDK. Every run feeds the next.
Not a dashboard bolted onto logs. Tracing, evals, judges, cost, and quality gates run on the same OpenTelemetry trace — so the number you ship on is the number you measured.
Latency, throughput, and quality trends across every run.
Per-call spend by model — including your own deployments.
Version prompts, diff changes, tie each edit to a score delta.
Position-swapped LLM judges across model families, with consensus and agreement.
Every span — retriever, LLM, tool — captured from one SDK.
Catch failures and bad outputs before users report them.
Prototype evals and judges against live production traces.
Scoped keys and managed secrets for every environment.
Public benchmarks don't run your prompts, your tools, or your data. evalOS scores every model on the tasks you actually ship, with cost and latency sitting right next to quality.
Switch the suite — coding, agents, RAG — and the ranking changes. The right model is the one that wins on your work.
No SDK sprawl, no per-vendor adapters. Drop in the import, name your service, and every LLM call, tool, and vector query starts streaming as OpenTelemetry spans.
// the spans that show up — automatically
evalOS implements the methods peer review trusts — so your scores hold up when an auditor, a board, or a skeptical engineer asks exactly how you got them.
Coarse rubrics decomposed into fine-grained, correlation-filtered sub-criteria with whitened-uniform weights.
arXiv:2602.05125Turns subjective judgments into verifiable preference pairs for GRPO judge training.
Meta · arXiv:2505.10320Custom judge models trained with GRPO / LoRA and validated on RewardBench — not an off-the-shelf prompt.
RewardBench4-stage behavioral probing — understand, ideate, rollout, judge — with bootstrap CIs and Wilcoxon tests.
pass@k · bootstrap CIEvery judgment is run with options swapped to cancel order bias.
Judges are tuned against human labels and checked on RewardBench.
A held-out reference set flags when production drifts away from it.
Bootstrap confidence intervals and Wilcoxon tests, not single-run noise.
Private beta. We're onboarding a small number of teams running production AI — not a waitlist for a landing page.
Anything OpenTelemetry can see: LLM calls, vector DBs, tools, even GPUs — captured from a single SDK, no per-vendor wiring.
The judges are calibrated, position-swapped, and trained (GRPO/LoRA) against RewardBench — and they run on the same OpenTelemetry trace as your production traffic, with drift detection and CI gates around them.
Early-access teams start on a managed workspace. Self-hosting is on the roadmap for teams with data-residency needs.
Book a short call. If your AI is in production and you're guessing whether the last change helped, you're exactly who this is for.
evalOS is in private beta. We're onboarding teams running production AI who are tired of guessing whether their changes made things better or worse.
Request early accessOr email rachitt@transfrm.in — response in 48h, no sales process