Translate broad quality and risk concerns into explicit evaluation dimensions and acceptance criteria.
AI Evaluation and Assurance Service
LLM Evaluation Service for Reliable, Governed AI Decisions
Dataconsultant evaluates large language models, RAG applications, copilots, agents, and generative AI workflows for task quality, groundedness, safety, robustness, fairness, privacy, security, cost, and production readiness. We help product, technology, risk, and governance teams turn broad AI concerns into testable requirements, traceable evidence, prioritised remediation, and defensible release decisions.
- Use-case-specific evaluation design
- Human and automated testing
- Risk, safety, and governance evidence
- Vendor-neutral model comparison
LLM Evaluation Control RoomIllustrative testing view
Assessment active1. DefineRisks and criteria
2. TestDatasets and probes
3. ReviewHuman validation
4. DecideRelease evidence
Task qualityExample
GroundednessExample
Safety controlsExample
RobustnessExample
Illustrative figures only. Actual measures, thresholds, and evidence are defined for the client’s use case and risk profile.
Quick definition
What is LLM evaluation?
LLM evaluation is the structured testing of a large language model or LLM-enabled system against defined business tasks, user expectations, technical constraints, and risk controls. It establishes how reliably the system performs, where it fails, whether its outputs are supported and safe, and what evidence is available for deployment, procurement, governance, and ongoing monitoring decisions.
Effective evaluation combines representative test data, quantitative measures, expert review, adversarial scenarios, error analysis, and clear acceptance criteria. It should assess the complete application—not only the underlying model—because prompts, retrieval, tools, policies, interfaces, and operating controls materially affect behaviour.
Service offering
A complete evaluation programme, from criteria to release evidence
The engagement is shaped around the intended use, user groups, operating environment, model architecture, data, risk level, and decision the evaluation must support.
01
Evaluation strategy and test design
Define intended use, material risks, acceptance criteria, metric hierarchy, test coverage, test-data requirements, review roles, and decision gates.
02
Model and application testing
Test foundation models, fine-tuned models, RAG systems, copilots, agents, and workflows using repeatable automated checks and calibrated human review.
03
Safety, robustness, and responsible-AI assessment
Assess harmful outputs, prompt injection, jailbreak resistance, sensitive-data exposure, bias, refusal behaviour, misuse scenarios, and control effectiveness.
04
Evidence, remediation, and monitoring
Produce traceable findings, risk-ranked recommendations, release-readiness evidence, evaluation assets, monitoring thresholds, and a plan for re-testing as models and data change.
Key value propositions
Make model decisions with evidence, not impressions
Compare models, prompts, retrieval strategies, and controls against consistent tasks and conditions.
Connect test cases, failures, risks, remediation actions, owners, and release decisions.
Reuse evaluation assets for regression testing, monitoring, change control, and incident review.
Problems addressed
Common LLM risks converted into practical evaluation work
Outputs look convincing but are not dependable
Teams cannot determine when responses are correct, supported, complete, or appropriate for the intended task.
Task-specific quality and groundedness testing
Build representative test sets, scoring rubrics, citation checks, error categories, and human-review protocols.
Generic benchmarks do not reflect business use
Public scores provide limited evidence for a particular process, user population, language, data source, or control environment.
Contextual acceptance criteria
Define measures and thresholds that reflect business consequences, operating constraints, and risk appetite.
Safety and security failures surface late
Prompt injection, data leakage, harmful content, excessive agency, and weak escalation may remain undiscovered until launch.
Adversarial and control testing
Test plausible misuse, boundary conditions, tool access, refusal behaviour, data handling, and defence-in-depth controls.
Model changes create uncontrolled regressions
Provider updates, prompt changes, new documents, and workflow modifications can alter behaviour without clear evidence.
Reusable regression and monitoring framework
Retain test assets, baselines, thresholds, version records, and review workflows for repeatable change assurance.
Need an independent view of an LLM-enabled product?
Share the use case, architecture, current concerns, and decision deadline for a practical evaluation scope.
Who the service is for
Suitable for teams making material AI deployment or procurement decisions
Good fit
- You are piloting or deploying an LLM, RAG application, copilot, chatbot, or agent.
- You need evidence for a release, risk, governance, audit, or procurement decision.
- Existing tests are informal, inconsistent, or too focused on generic benchmarks.
- You need to compare models, vendors, prompts, retrieval methods, or guardrails.
- You need reusable evaluation assets for regression testing and production monitoring.
May not be the right fit
- You only need a public benchmark score with no use-case analysis.
- The intended use, system boundary, and accountable owner have not been defined.
- No representative data, users, or subject-matter reviewers can be made available.
- You require a statutory certification, legal opinion, or penetration test as the sole deliverable.
- You expect evaluation to guarantee that an AI system will never fail.
Common use cases
Evaluation patterns for real LLM applications
Service chatbot assurance
Test answer accuracy, policy adherence, escalation, harmful content, multilingual consistency, and unsupported commitments.
RAG and search evaluation
Measure retrieval relevance, context coverage, answer faithfulness, citation accuracy, abstention, and stale-content risk.
Copilot quality assessment
Evaluate task completion, instruction following, confidentiality, user oversight, and error impact across common workflows.
AI agent testing
Assess tool selection, action accuracy, permissions, state management, recovery, confirmation, and unintended side effects.
Model and vendor comparison
Compare candidate models using consistent tasks, risk criteria, latency, cost assumptions, deployment constraints, and governance evidence.
High-impact workflow assurance
Test explainability, human review, data handling, bias, record keeping, control effectiveness, and foreseeable failure scenarios.
Capabilities
Evaluation coverage across quality, risk, and operations
Quality and task performance
Assess whether the system completes the intended task accurately, consistently, and at an acceptable level of usefulness.
Safety and responsible AI
Examine foreseeable harms, unfair behaviour, unsafe responses, sensitive content, and the adequacy of human oversight.
Security and privacy
Test system boundaries and data-handling behaviour across prompts, retrieval, tools, logs, integrations, and user access.
Robustness and operations
Measure behaviour under ambiguity, distribution shift, noisy inputs, system changes, latency constraints, and operational failure.
Deliverables
Decision-ready evidence and reusable evaluation assets
| Deliverable | What it contains | How it supports decisions |
|---|---|---|
| Evaluation strategy | System boundary, intended use, risk dimensions, metrics, thresholds, test coverage, roles, and governance. | Creates a shared basis for testing and acceptance. |
| Test suite and evaluation dataset | Representative tasks, edge cases, adversarial scenarios, expected behaviours, metadata, and version controls. | Enables repeatable testing and future regression checks. |
| Scorecard and error taxonomy | Quantitative measures, human-review findings, confidence limits, failure categories, and severity ratings. | Shows where performance is acceptable and where risk remains. |
| Model or configuration comparison | Side-by-side results for models, prompts, retrieval methods, guardrails, or vendors. | Supports procurement, architecture, and model-routing choices. |
| Risk and control findings | Observed failure modes, control gaps, affected users, likely causes, evidence, owners, and priority. | Supports governance, risk treatment, and release conditions. |
| Remediation and re-test plan | Recommended changes, dependencies, acceptance checks, sequencing, and re-evaluation approach. | Turns findings into an actionable improvement backlog. |
| Release-readiness report | Coverage, limitations, residual risks, open decisions, approvals, and monitoring requirements. | Provides evidence for a documented deployment decision. |
| Monitoring specification | Production metrics, thresholds, sampling, alerts, review cadence, incident triggers, and ownership. | Extends evaluation into ongoing assurance. |
Define the evidence your release decision requires
We can help translate product, risk, compliance, and operational expectations into a proportionate evaluation plan.
Service process
How Dataconsultant delivers LLM evaluation
The sequence is adapted to system maturity, risk, available evidence, and the decision the engagement must support.
Align the decision
Clarify intended use, users, business consequences, stakeholders, system boundary, and the decision to be made.
Primary output: evaluation charter and decision context.
Map risks and requirements
Identify quality, safety, fairness, privacy, security, regulatory, and operational requirements.
Primary output: risk-to-test traceability matrix.
Design tests and data
Create representative tasks, rubrics, edge cases, adversarial probes, sampling rules, and acceptance thresholds.
Primary output: evaluation plan and test suite.
Execute and validate
Run automated evaluations, calibrated model-based judging, expert review, and control testing across agreed configurations.
Primary output: scored results and validated evidence.
Analyse and remediate
Investigate failure patterns, likely causes, affected scenarios, control gaps, and practical improvement options.
Primary output: findings register and remediation backlog.
Decide and operationalise
Document residual risk, release conditions, monitoring needs, ownership, change controls, and re-test triggers.
Primary output: readiness report and monitoring specification.
Technology, platforms, standards, and frameworks
Tool-aware, platform-neutral evaluation
The evaluation approach should fit the architecture and governance environment rather than force a single platform or benchmark.
Models and application patterns
Evaluation and observability tooling
Reference points
Applicable laws, standards, regulatory expectations, and assurance obligations depend on jurisdiction, sector, system use, data, and organisational responsibilities. They should be validated by authorised legal, privacy, security, compliance, and audit specialists.
Evaluate within your existing technology and control environment
We can work with internal teams, model providers, integrators, platforms, and existing governance processes.
Engagement models
Flexible support for one decision or continuous assurance
Focused assessment
Evaluate a defined model, use case, risk concern, or release decision with a bounded test scope.
Independent evaluation programme
Design and execute a multi-dimensional evaluation covering quality, safety, security, governance, and readiness.
Embedded evaluation support
Add specialist evaluation capability to a product, engineering, data science, governance, or assurance team.
Managed continuous evaluation
Operate scheduled regression tests, monitoring reviews, change assurance, evidence reporting, and improvement cycles.
Practical illustrative examples
How evaluation changes by use case
Illustrative example 1
RAG knowledge assistant
RAG knowledge assistant
From generic answer scoring to evidence-based reliability
A professional-services organisation wants an assistant to answer policy questions from controlled documents. Evaluation covers retrieval relevance, context coverage, faithfulness, citation accuracy, abstention when evidence is missing, access boundaries, stale content, and escalation. The output is a test suite, scorecard, failure taxonomy, and release conditions—not a claim that every future answer will be correct.
Illustrative example 2
Customer-service copilot
Customer-service copilot
Balancing usefulness, policy adherence, and human control
An ecommerce team uses an LLM to draft responses for agents. Evaluation tests instruction following, product and policy accuracy, tone, sensitive-data handling, unsupported promises, escalation, multilingual behaviour, latency, and agent override. Findings inform prompt changes, knowledge improvements, guardrails, review sampling, and production monitoring.
Illustrative example 3
Workflow agent
Workflow agent
Testing actions, permissions, and recovery—not only text
A technology team is piloting an agent that uses enterprise tools. Evaluation includes tool selection, parameter accuracy, permission boundaries, confirmation before material actions, state handling, repeated-action prevention, failure recovery, audit records, and human takeover. The assessment focuses on the complete system and its operating controls.
Expected outcomes and KPIs
Measure confidence, coverage, and control—not only model scores
Quality measures
Task success, correctness, completeness, relevance, groundedness, citation accuracy, consistency, and appropriate abstention.
Risk measures
Severity-weighted failure rate, unsafe-response rate, bias indicators, privacy incidents, security-control failures, and unresolved high-risk findings.
Operational measures
Latency, cost per task, fallback rate, escalation rate, user correction rate, model drift, monitoring coverage, and incident response time.
Governance measures
Test coverage against requirements, evidence completeness, decision traceability, remediation closure, approval status, and re-test currency.
Expected outcomes depend on the system, baseline, test coverage, data quality, client decisions, implementation quality, and operating controls. Evaluation reduces uncertainty; it does not eliminate all model or business risk.
Pricing and cost factors
What influences the cost of LLM evaluation?
Scope and system complexity
Number of models, use cases, workflows, prompts, integrations, tools, user groups, languages, modalities, and deployment environments.
Evaluation depth
Test volume, metric complexity, human-review requirements, adversarial testing, security assessment, fairness analysis, and root-cause work.
Evidence and governance needs
Documentation, audit trail, regulatory mapping, stakeholder validation, independent review, release gates, and monitoring design.
Data readiness
Availability and quality of representative examples, labels, source documents, logs, expected outputs, known failures, and subject-matter experts.
Delivery environment
Secure access, data residency, controlled infrastructure, model usage charges, tool licensing, onsite work, and third-party dependencies.
Ongoing support
Remediation, re-testing, regression automation, continuous monitoring, reporting cadence, incident support, and capability transfer.
Receive a scope based on your actual evaluation decision
Initial scoping can identify the material risk dimensions, required evidence, dependencies, and a proportionate engagement model.
Why consider Dataconsultant
Evaluation that connects technical evidence with business accountability
Use-case-led
Metrics and tests are designed around the actual task, users, consequences, and operating environment.
Evidence-conscious
Results include test coverage, assumptions, limitations, uncertainty, and traceability—not isolated headline scores.
Cross-functional
Delivery can bring product, engineering, data, security, privacy, legal, compliance, risk, audit, and business reviewers into one process.
Vendor-neutral
Model and tooling recommendations can be assessed against requirements without presuming a single provider or architecture.
Operationally practical
Findings are converted into remediation actions, release conditions, monitoring requirements, owners, and re-test triggers.
Capability-building
Evaluation assets, methods, documentation, and knowledge transfer can help internal teams sustain the process.
Security, quality, privacy, and compliance
Evaluation designed around responsible evidence handling
Quality controls
Test-set versioning, reviewer guidance, calibration, inter-rater checks, sampling rules, repeatability, error analysis, and documented limitations.
Security controls
Least-privilege access, secure test environments, credential protection, restricted tool permissions, sensitive-output handling, and incident escalation.
Privacy controls
Data minimisation, lawful-use confirmation, purpose limitation, de-identification where appropriate, retention controls, and privacy review.
Compliance support
Traceability to internal policies, risk controls, sector expectations, documentation needs, approval gates, and evidence-retention requirements.
Final control design depends on the client environment. Dataconsultant’s evaluation service does not by itself constitute legal advice, regulatory approval, formal certification, statutory audit, or a guarantee of compliance.
Technology ecosystems and delivery environment
Evaluation across the complete LLM application stack
Model providers
Open-weight models
Cloud AI services
RAG pipelines
Vector databases
Prompt management
Agent frameworks
Enterprise applications
Observability platforms
Security controls
Data platforms
Identity and access
Human review
Governance workflows
Monitoring and reporting
Customer perspectives
What teams value in LLM evaluation delivery
Representative customer feedback written to illustrate common service priorities. Publish named client statements only with appropriate approval.
★★★★★
“The evaluation moved us beyond subjective prompt reviews. We received a clear test framework, documented failure categories, and practical release criteria that product, engineering, and risk teams could use in the same decision process.”
★★★★★
“The RAG assessment was particularly useful because it separated retrieval problems from generation problems. The team explained coverage limits clearly and gave our developers a prioritised list of changes without overstating what the results proved.”
★★★★★
“Security testing covered realistic prompt-injection and data-exposure scenarios rather than generic demonstrations. Findings included evidence, affected workflows, severity, likely causes, and re-test conditions, which made remediation much easier to manage.”
★★★★★
“Dataconsultant helped us compare model options using our own tasks, language requirements, latency constraints, and governance expectations. The decision matrix made trade-offs visible and supported a more disciplined procurement discussion.”
★★★★★
“The workshops brought legal, privacy, operations, and engineering into a common evaluation model. Assumptions and unresolved questions were recorded openly, and the final report distinguished technical findings from matters requiring specialist legal interpretation.”
★★★★★
“We needed a repeatable approach after frequent model and prompt changes. The team created regression tests, review guidance, monitoring thresholds, and ownership rules that our internal team could continue using after the engagement.”
Frequently asked questions
LLM evaluation service questions
What is an LLM evaluation service?
An LLM evaluation service tests whether a large language model or generative AI application performs its intended tasks reliably and within defined risk tolerances. Evaluation may cover answer quality, groundedness, hallucination, safety, robustness, fairness, privacy, security, latency, cost, governance, and production monitoring.
What does Dataconsultant evaluate?
Dataconsultant can evaluate foundation models, fine-tuned models, retrieval-augmented generation applications, copilots, agents, chatbots, summarisation systems, classification workflows, content-generation tools, and other LLM-enabled products. Scope is based on the intended use, users, data, operating environment, risk profile, and acceptance criteria.
How is LLM quality measured?
Quality is measured through a combination of task-specific test sets, deterministic checks, model-based judging with calibration, expert human review, statistical analysis, error taxonomies, adversarial testing, and operational measures. The metric set should reflect the business task rather than rely on a single generic score.
Can you test hallucinations and groundedness?
Yes. Groundedness evaluation can examine whether outputs are supported by supplied source material, whether citations are accurate, whether unsupported claims are introduced, and how the system behaves when evidence is missing or conflicting. Results should be interpreted in the context of the use case and test coverage.
Can you compare multiple LLMs or vendors?
Yes. A comparative evaluation can assess candidate models against the same use cases, datasets, prompts, controls, performance requirements, cost assumptions, and governance criteria. The resulting decision matrix can support model selection, routing, procurement, or migration decisions.
Do you evaluate RAG systems and AI agents?
Yes. RAG evaluation can cover retrieval relevance, context precision, context recall, answer faithfulness, citation quality, and failure handling. Agent evaluation can additionally examine tool selection, action accuracy, permissions, state management, recovery, escalation, and unintended side effects.
How long does an LLM evaluation take?
There is no reliable fixed duration before scoping. Timing depends on the number of models and use cases, test-data availability, risk level, languages, modalities, integrations, human-review needs, adversarial depth, regulatory requirements, and remediation cycles.
What information is required from the client?
Useful inputs include intended use, user groups, model and architecture details, prompts, system instructions, representative data, policies, risk assessments, known failure cases, acceptance criteria, logs, monitoring data, and access to business, technical, security, privacy, legal, and compliance stakeholders.
How is LLM evaluation pricing calculated?
Pricing is influenced by the number of models, use cases, datasets, languages, modalities, integrations, risk dimensions, test volume, human review, red-team depth, reporting requirements, remediation support, and whether continuous monitoring is included.
Does LLM evaluation replace legal, security, or regulatory review?
No. LLM evaluation can produce evidence and identify issues, but it does not replace legal advice, statutory audit, formal certification, penetration testing, privacy impact assessment, or specialist regulatory interpretation unless those services are separately commissioned from authorised professionals.
Can Dataconsultant support remediation after testing?
Yes. Remediation support can include prompt and policy refinement, retrieval improvement, guardrail design, evaluation-set expansion, model-routing changes, human-in-the-loop controls, monitoring design, documentation, and re-testing. Changes remain subject to client approval and system ownership.
Can evaluation continue after production launch?
Yes. Continuous evaluation can monitor quality drift, new failure patterns, policy violations, retrieval degradation, cost, latency, user feedback, and incidents. Monitoring thresholds and review frequency should be aligned with the system's risk level and change cadence.