Evaluate, Audit, and Improve AI Systems Before They Fail in Production

The Data Layer Behind Reliable AI Systems That Must Be Evaluated Before Production

Public leaderboards are useful orientation, not a production acceptance test. A model can score well on a broad benchmark and still fail on an organization’s policies, users, documents, languages, tools, workflows, security boundaries, or cost constraints. Model integrity requires evaluation specific to the intended context and designed around the failure or harm the team needs to prevent.

No single metric proves that a model or AI system is safe, reliable, or ready. A credible program combines deterministic tests, expert human judgment, model-based evaluators calibrated against people, adversarial testing, slice analysis, system-level observation, and continuous regression. It documents the limits of the evidence rather than turning a benchmark score into a universal claim.

Our role is not to sell a fixed, generic dataset. We design a program around the target model, deployment environment, failure profile, data rights, and acceptance criteria. Every engagement begins with a concrete definition of what a usable training or evaluation unit means for the customer—and how that unit will be verified before delivery.

Built for Teams That Need More Than Volume

This product is for teams selecting models, launching AI features, validating fine-tunes, governing enterprise deployments, or investigating production incidents. The buyer needs a trusted release gate, clear failure taxonomy, repeatable test harness, and an actionable path from findings to data, prompt, model, tool, interface, or policy changes.

Common engagement triggers

• Public benchmark results do not reflect the customer’s domain, user population, workflow, or risk tolerance.
• Model comparison is dominated by anecdotal demos or an uncalibrated LLM judge.
• Evaluation prompts have leaked into development, are too predictable, or no longer distinguish current models.
• A multimodal or agentic system needs system-level testing beyond the base model’s text output.
• Safety testing focuses on a static refusal set and misses adaptive, indirect, tool-enabled, or multilingual attacks.
• Governance teams need traceable evidence, ownership, versioning, and remediation records for release decisions.

What This Product Can Support

Private Benchmark Design

Private benchmarks translate actual use cases and risks into protected, versioned tasks. They can combine customer artifacts, synthetic scenarios, expert-authored cases, and model-mined failures while controlling contamination.

• Capability, domain, policy, and operational task suites.
• Held-out references and protected test data.
• Difficulty, language, modality, and user-slice design.
• Deterministic, rubric, and hybrid scoring.
• Benchmark refresh and retirement policy.

Human Evaluation and Adjudication

Human review is essential when quality depends on context, evidence, professional judgment, communication, or user impact. The process must be designed as measurement, not informal opinion.

• Dimension-level rubrics and anchored scales.
• Expert or target-user panels.
• Pairwise, ranking, pointwise, and task-based methods.
• Blinding, randomization, calibration, and disagreement analysis.
• Senior adjudication and evidence recording.

Hallucination, Grounding, and Factuality

Factual evaluation should distinguish unsupported claims, incorrect claims, stale information, citation mismatch, source misinterpretation, and acceptable uncertainty.

• Closed-book and source-grounded tasks.
• Citation entailment and evidence completeness.
• Answerability and abstention evaluation.
• Date-sensitive and domain-expert fact review.
• Retrieval, generation, and synthesis error separation.

Safety, Security, and Red Teaming

Risk testing uses threat models, policy requirements, and realistic system interfaces. The goal is not only to elicit failures, but to reproduce, classify, prioritize, and convert them into mitigations and regressions.

• Prompt injection, jailbreak, data leakage, and unsafe output testing.
• Agentic excessive-agency and authorization scenarios.
• Multilingual, multimodal, multi-turn, and indirect attacks.
• Over-refusal and safe-completion quality.
• Adaptive mutation and post-mitigation regression.

Agent, Multimodal, and System Evaluation

The deployed system includes retrieval, tools, prompts, memory, interfaces, permissions, and people. Evaluation should inspect observable state and artifacts rather than assign every failure to the model.

• Agent task success, state changes, intervention, and cost.
• Multimodal evidence grounding and temporal accuracy.
• RAG retrieval, citation, and synthesis components.
• Latency, availability, fallback, and recovery.
• End-to-end scenario and human-factors evaluation.

Continuous Evaluation and Monitoring

Integrity is maintained through versioned regressions, production-sampled cases, drift analysis, incident replay, and refreshed adversarial tests.

• Pre-release model and system comparisons.
• Recurring regression and red-team runs.
• Production failure intake and triage.
• Benchmark saturation and contamination review.
• Remediation tracking and governance evidence.

Data We Build

The delivery unit is defined at the level required by the model and the evaluation harness—not merely as a row of text or a media file. Depending on the program, one record may include source inputs, structured intermediate state, human judgments, provenance, quality evidence, and model- or environment-derived verification.

Deliverable	What it contains	Typical use
Private benchmark	Task set, evidence, answer keys, rubric, slices, version, access policy, and scoring harness.	Model selection, release gating, fine-tune validation.
Human evaluation study	Study design, panel, instructions, blinded outputs, raw judgments, reliability analysis, and findings.	Quality comparison, preference, user-impact assessment.
Safety red-team suite	Threat model, attack cases, transcripts, system state, severity, reproducibility, and regression tests.	Safety/security assurance and mitigation testing.
Hallucination/grounding evaluation	Claims, evidence, citations, answerability, error taxonomy, and adjudication.	RAG, research, document, and domain assistants.
Agent/system evaluation	Environment, task, tool/harness version, state checks, artifacts, interventions, cost, and outcome.	Workflow agents and computer-use systems.
Integrity report and remediation map	Executive summary, scope, methods, uncertainty, critical failures, root causes, and actions.	Governance review, release decision, continuous improvement.

Reference Record Design

A production schema is finalized during calibration, but a typical record may include the following fields:

• eval_item_id — Stable item identifier linked to benchmark version and access class.
• risk_or_capability — Mapped capability, failure mode, affected stakeholder, severity, and intended context.
• task_and_context — Prompt, media, documents, environment, system policy, tools, and user assumptions.
• reference_and_evidence — Expected answer, accepted variants, source evidence, verifier, or adjudication guide.
• model_system_version — Model, prompt, retrieval index, tools, harness, policy, and configuration.
• response_or_trajectory — Output, tool calls, artifacts, state changes, latency, and interventions.
• scores_and_findings — Dimension scores, pass/fail, severity, explanation, and evidence references.
• review_metadata — Reviewer qualification, blinding, calibration, disagreement, and adjudication.
• contamination_and_exposure — Creation source, public exposure, similarity checks, access logs, and retirement state.
• remediation_link — Owner, mitigation, due date, validation result, and regression test ID.

{
  "eval_item_id": "eval_rag_policy_00073",
  "risk_or_capability": {"category": "unsupported-policy-claim", "severity": "high", "context": "customer-facing"},
  "task_and_context": {"question": "...", "documents": ["policy_2026_04"], "system_policy": "answer-only-from-provided-sources"},
  "reference_and_evidence": {"answerability": "answerable", "evidence_spans": ["policy_2026_04#p12:l3-l9"]},
  "model_system_version": {"model": "candidate_b", "prompt": "system-v14", "retriever": "index-"},
  "response_or_trajectory": {"text": "...", "citations": ["policy_2026_04#p9"], "latency_ms": 1840},
  "scores_and_findings": {"correctness": 2, "citation_entailment": 1, "finding": "Citation does not support the eligibility condition."},
  "review_metadata": {"reviewers": 2, "adjudicated": true},
  "contamination_and_exposure": {"public": false, "near_duplicate_check": "pass"},
  "remediation_link": {"owner": "retrieval-team", "regression_test_id": "reg_0182"}
}

The schema is versioned. Changes to label definitions, evidence requirements, reviewer policy, or normalization rules are recorded so training and evaluation results can be traced to the exact specification used.

Program Workflow

1. Context and risk mapping. Document intended use, users, affected stakeholders, unacceptable outcomes, operational constraints, and decisions the evaluation must support.
2. Evaluation architecture. Choose tasks, evidence, system boundaries, slices, baselines, scoring methods, sample sizes, and human-review requirements.
3. Task and reference production. Author or collect cases, construct references and verifiers, create difficult negatives, and separate development from protected evaluation data.
4. Calibration and baseline runs. Test the rubric and harness on representative systems, compare human and automated scoring, and remove ambiguous or invalid items.
5. Controlled evaluation. Lock versions, run candidate systems, capture outputs and state, randomize or blind where appropriate, and monitor execution integrity.
6. Analysis and adjudication. Review critical failures, disagreement, judge bias, slice performance, uncertainty, cost, latency, and root cause across components.
7. Remediation and regression. Assign owners, convert failures into training or system changes, add reproducible regressions, and validate that fixes do not create new failures.
8. Governed refresh. Track exposure, saturation, policy/domain changes, new attacks, and model progress; refresh or retire tasks while preserving comparability.

A pilot is considered complete only when the customer and delivery team have aligned on the rubric, reviewed representative disagreements, validated the export, and confirmed that the data is useful in the intended training or evaluation loop.

Quality Controls

Quality is designed into the workflow rather than added as a final inspection step. The control plan depends on task ambiguity, domain risk, annotator expertise, and whether an item has an executable or external verifier.

• Version-locked execution: Model, prompt, retrieval, tools, harness, policy, dependencies, and random seeds are recorded for every run.
• Protected evaluation data: Access, exposure, similarity, and reuse are controlled to reduce leakage and benchmark overfitting.
• Scoring-method triangulation: Deterministic checks, expert review, calibrated model judges, and user/task measures are combined as appropriate.
• Judge calibration and bias tests: Automated judges are compared with human decisions and tested for position, verbosity, style, self-preference, and reference sensitivity.
• Critical-failure review: High-severity cases receive independent confirmation and reproducibility checks even if aggregate performance is strong.
• Slice and variance reporting: Results are broken down by domain, language, difficulty, modality, user group, risk, and repeated-run variance.
• System-component attribution: Failures are localized to model, retrieval, prompt, tool, data, policy, interface, or human process when evidence permits.
• Limitations disclosure: Reports state what the evaluation does not measure and avoid generalizing beyond the tested context.

Recommended acceptance metrics

• Task/capability performance: Accuracy, pass rate, rubric score, partial credit, or task success appropriate to the use case.
• Risk rate and severity: Frequency of defined harmful or unacceptable outcomes, with confidence and critical-case review.
• Grounding and factuality: Claim support, citation entailment, answerability, contradiction, and calibrated abstention.
• Robustness and regression: Change under perturbation, repeated runs, version upgrades, attacks, and domain/language slices.
• Human-system performance: User success, time, effort, correction burden, escalation, and trust calibration where relevant.
• Operational performance: Latency, cost, availability, tool-call efficiency, intervention, and failure recovery.

No single aggregate score is sufficient. Agreement can diagnose ambiguity, but high agreement does not by itself prove correctness; disagreement can reveal plural preferences, unclear policy, underspecified context, or difficult edge cases. The QA report therefore pairs quantitative measures with sampled error analysis and adjudication notes.

Delivery and Integration

Supported delivery patterns

• Versioned batch delivery for controlled model-training releases.
• Incremental delivery for active learning, post-training, or continuous evaluation.
• Secure customer-workspace delivery when source data cannot leave the customer environment.
• API- or object-storage-based transfer for high-volume or multimodal programs.
• Evaluation-ready task packs with rubrics, reference evidence, and scoring logic.

Common formats

JSONL, Parquet, evaluation harness repositories, containerized tasks, HTML/PDF scorecards, risk registers, regression suites, dashboard-ready tables

Evaluation can run against model APIs, internal endpoints, RAG systems, agent harnesses, multimodal pipelines, or human-in-the-loop products. Protected task assets remain separate from result tables; immutable run manifests support export into model registries, issue trackers, and governance workflows.

Each release can include a dataset card or delivery memo, schema and ontology version, quality summary, known limitations, rights and consent metadata where applicable, and a machine-readable manifest with checksums and file-level lineage.

Security, Rights, and Governance

Evaluation data may contain customer secrets, personal information, security vulnerabilities, harmful content, or unreleased product behavior. Access and retention should reflect sensitivity, and red-team findings need coordinated disclosure and remediation. NIST and ISO frameworks can inform process design, but an evaluation engagement does not itself establish legal compliance or certification.

Program controls may include role-based access, workspace isolation, least-privilege review queues, de-identification, retention limits, geographic routing, approved-tool restrictions, audit logs, and customer-defined deletion procedures. These controls are scoped contractually; the page does not imply a certification or regulatory status that has not been independently verified.

Engagement Models

Engagement	Best for	Typical output
Evaluation design sprint	Teams defining a launch gate or model selection.	Risk map, blueprint, pilot tasks, calibrated scoring, and implementation plan.
Private benchmark program	A stable use case requiring repeatable comparison.	Protected suite, harness, human-review protocol, baselines, and scorecard.
Red-team and remediation cycle	Teams assessing safety/security exposure.	Threat model, attacks, validated findings, severity, mitigations, and regressions.
Continuous model integrity	Frequently changing models and systems.	Scheduled evaluations, production-case intake, drift/saturation review, and governance reporting.

Illustrative Program Shapes

The examples below are representative program patterns, not claims about named customers or guaranteed outcomes.

1. Enterprise RAG release gate. Evaluate retrieval coverage, citation entailment, unsupported synthesis, stale documents, access control, answerability, latency, and user correction burden.
2. Customer-service model integrity. Test policy compliance, factual accuracy, escalation, over-refusal, multilingual behavior, sensitive data handling, and consistency across long conversations.
3. Agentic security evaluation. Run direct and indirect prompt injection, excessive agency, credential exposure, unsafe tool use, approval bypass, and recovery tasks in sandboxes.
4. Multimodal expert benchmark. Create protected image, document, audio, and video tasks with evidence-linked answers, specialist review, perturbation slices, and adjudication.

Why a Custom Program

Off-the-shelf datasets are useful for baseline experimentation, but production systems usually fail at the boundaries: domain-specific policy, uncommon languages, tool or sensor state, difficult negative examples, ambiguous evidence, long-tail user behavior, and deployment-specific risk. A custom program makes those boundaries explicit and converts them into measurable data requirements.

The result is not simply “more labels.” It is a controlled data asset with a defined purpose, documented provenance, repeatable quality process, and a path from observed model failure to the next training or evaluation cycle.