Kaudh Private Limited focuses on the high-skill work that helps AI teams understand model behavior: coding tasks, reasoning benchmarks, dataset operations, and quality-controlled human evaluation.
Review model outputs for factuality, instruction following, reasoning rigor, safety, and hallucination risks.
Ideal for comparing model versions, validating prompt updates, and ensuring enterprise release readiness.
Design difficult, multi-file coding challenges, repository-level bug-fix tasks, and unit-test backed ground truths.
Goes beyond simple pass/fail tests by inspecting AST syntax, runtime safety, edge cases, and concurrency.
Construct complex multi-step tasks across math, symbolic logic, planning, data interpretation, and adversarial traps.
Evaluates multi-turn reasoning chains to uncover where intermediate logic breaks down.
Produce high-signal preference pairs, fine-grained error taxonomy labels, and golden calibration sets.
Delivers clean, production-ready JSONL/Parquet packages with complete audit trails and QA notes.