LLM Evaluation Services

Structured measurement of how well a language model performs the task it was built for — factual accuracy, groundedness, instruction following, multi-turn coherence, domain correctness, safety behaviour and language quality — against a written rubric on a test set that reflects real usage. It differs from red teaming, which tries to break the model, and from A/B testing, which measures what users prefer in production.

Because the judge needs a reference point. LLM judges reach over 80% agreement with humans on well-structured tasks, but they carry documented position, verbosity and self-preference biases, and a RAND study found no judge uniformly reliable across benchmarks. Industry guidance holds that divergence above 20–25% from human spot-checks means the judge needs recalibrating for your domain. Most teams have never measured their divergence. We baseline it, diagnose the bias pattern, recalibrate and monitor for drift — so you can keep evaluating at automated scale with numbers you can defend.

A systematic preference for a response based on where it appears rather than how good it is. Research across roughly 150,000 evaluation instances and 15 judges found position bias varies significantly by judge and task and is not attributable to chance — some judges favour the first response, some the last. It matters most in pairwise comparison, which is how most model-versus-model evaluation is run. We randomise and counterbalance presentation order in human evaluation, and test for it explicitly when calibrating a judge.

We separate unsupported claims from contradicted ones, because they have different causes and different fixes. For RAG systems we check each claim against the retrieved context and verify that citations exist and actually support the claim attached to them. For open-domain generation, domain SMEs verify factual claims against authoritative sources. Fluent, confident, well-cited and wrong is the hardest case and the one that most needs a human who knows the field.

Yes, and we measure retrieval and generation separately — context precision, context recall, faithfulness, answer relevance, citation accuracy and noise sensitivity. A blended quality score can't tell you whether retrieval missed the document or generation ignored it, and those need completely different fixes.

Yes. Agent evaluation covers task completion, tool selection, parameter accuracy, trajectory efficiency, error recovery, stopping behaviour and whether the agent's own report of what it did matches what it actually did. A correct final answer reached through a broken path passes end-state testing and fails in production.

Four reasons: benchmarks are published and may be in the training data; they measure benchmark-shaped questions rather than your users' messy real ones; a single headline number averages over exactly the slices you care about; and most are English-first, so strong benchmark performance says little about Tamil or Odia. Benchmarks are useful context. A golden set built from your own traffic is evidence.

Yes — 12+ Indian languages including romanised and code-mixed variants, with evaluation sets authored natively rather than translated from English. We report per language and never blend into a single multilingual score, because a strong English average routinely hides a weak result elsewhere.

A first engagement covering one or two languages typically runs 5–6 weeks, most of it in golden set construction and rubric calibration. Once those exist, subsequent cycles run 1–2 weeks. Judge calibration against an existing test set runs 2–3 weeks.

You do. They are built from your traffic, versioned, and delivered to you with the report — along with the calibrated judge configuration where that's in scope. The report describes one model at one moment; the golden set keeps working every release afterwards.