Surface tests miss plausible hallucinations
LLMs sound articulate while citing imaginary API methods or hallucinated theorems. Our specialist evaluators verify actual runtime execution and proof validity.
The Evaluation Command Center
Better LLM evaluations start with better reviewers.
Institutional-grade model validation powered by a curated network of domain experts, elite software engineers, and specialized PhDs. We catch the edge cases automated evals miss.
Vetted Specialist Reviewers•24h Turnaround SLA•Zero Public Leakage
FRONTIER AI EVALUATION
“I think evaluations, we’re still very early in our ability to study evaluations, particularly for dynamic systems acting in the world.”
Dario AmodeiAnthropic
CEO & Co-Founder, Anthropic
Kaudh Private LimitedHuman-in-the-Loop Imperative
Expert-Led
Evaluation Specialists
Senior SWEs, PhD Logicians & Domain Leads
8-Stage
Quality & Governance OS
From task scoping to cryptographic delivery
24h
Pilot Turnaround SLA
Fast scoping and calibration for urgent eval needs
100%
Human-Verified Ground Truth
Rigorous test-backed reference annotations
Why Teams Come To Us
Surface-level evals look clean until the edge cases arrive.
Automated benchmarks and crowd-sourced annotators miss subtle logical fallacies, edge-case bugs, and silent instruction drift. Kaudh Private Limited provides expert, rubric-calibrated evaluation.
Coding models hide latent defects
A generated snippet can pass a basic unit test while harboring race conditions, unhandled edge cases, or brittle assumptions that fail in production.
Multi-step reasoning drifts over long horizons
In 10-step plans or agentic loops, small errors at step 3 compound catastrophically. Our domain reviewers inspect every intermediate decision node.
Public benchmarks are contaminated
Frontier LLMs have memorized public eval sets (HumanEval, GSM8K). We engineer fresh, private, leak-proof benchmark suites tailored to your use case.
Capabilities & Workflows
Explore All CapabilitiesEngineered evaluation suites for high-stakes models.
Core Service
LLM Evaluation & Auditing
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.
10 Quality DimensionsDouble-Blind ReviewAdjudication Logs
High-Skill SWE
Coding Benchmark Engineering
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.
Multi-file ReposUnit Test SuitesStatic AST Checks
PhD Logicians
Reasoning Benchmark Creation
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.
Formal ProofsMulti-step TrajectoriesAdversarial Stress Sets
Data Ops
Dataset Operations & RLHF/DPO
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.
Preference RankingError TaxonomyJSONL / Parquet Export
The Quality Operating System
How evaluation is engineered from intake to delivery.
Click through our 8-stage operational pipeline to inspect how rubrics, golden calibration, and senior adjudication guarantee consistent ground-truth data.
STAGE INSPECTOR
ACTIVE PHASE01. Scope & Objective Mapping
Operational Objective
Align with client engineering teams on target capabilities, acceptable error thresholds, and custom failure taxonomies.
Deliverable & Quality Threshold
Evaluation Protocol Spec & KPI Framework
Full Operations SpecStage 1 of 8
Deployment Archetypes
Built for AI teams where errors are expensive.
01Pre & Post-Training
Frontier AI Research Labs
Private pre-release benchmark suites, post-training RLHF preference datasets, and adversarial red-teaming sweeps.
02Software Engineering
Coding Copilots & Developer Tools
Multi-file repository refactor tasks, test generation benchmarking, AST safety verification, and bug triage evaluation.
03Agentic Tool-Use
Autonomous Agent Teams
Multi-step tool execution, SQL/API schema validation, error recovery trajectories, and long-horizon goal verification.
04Vendor Comparison
Enterprise Model Bake-offs
Objective head-to-head comparison between frontier models (OpenAI vs. Anthropic vs. Open Source) across proprietary tasks.
05Regulated Verticals
High-Stakes Domain Evals
Healthcare clinical guideline adherence, financial document reasoning, and legal regulatory compliance checks.
06Data Quality
Human-in-the-Loop Adjudication
Resolving high-ambiguity edge cases, converting messy edge cases into clean training labels, and synthetic data auditing.
Security & Confidentiality
Review Security ArchitectureAir-gapped discipline for sensitive models and data.
Isolated Customer Tenancy
Dedicated logical and physical segregation of evaluation data. Zero cross-contamination between client evaluation queues.
Air-Gapped Reviewer Sandboxes
Sandboxed browser environments with clipboard restrictions, watermark overlays, and zero local device persistence.
Automated & Manual PII Scrubbing
Pre-ingestion scrubbing of API keys, employee PII, proprietary source code tokens, and internal database URIs.
Role-Based Access Control (RBAC)
Least-privilege access model. Reviewers only see the exact tasks assigned to their authenticated session.
Strict NDA & Legal Perimeter
100% of internal evaluators are background-verified and bound by strict confidentiality and intellectual property agreements.
Immutable Audit Trails
Every score, reviewer edit, QA sign-off, and export is timestamped and recorded in auditable append-only logs.
Pilot Intake Open (Q3/Q4)
From pilot to production.
Share your model objective, target evaluation criteria, or prompt samples. Kaudh Private Limited will scope a calibrated human-in-the-loop evaluation workflow with 24-hour turnaround.
Pilot Ingestion Checklist
Sample batch: 100 to 5,000 tasks
JSONL, CSV, or API endpoint input
Double-blind review + Adjudication
24h pilot baseline report SLA