Evidence-Led Testing
Replace informal demonstrations with traceable scenarios, results, findings and decision evidence.
AI Assurance · Safety Evaluation
AI Safety Evaluation for Production-Ready AI Systems
Identify, test and mitigate AI risks before they affect customers, employees, operations or governance decisions. DataConsultant evaluates models, generative AI, RAG applications and agents using risk hypotheses, realistic and adversarial scenarios, evidence-led findings, remediation guidance and retesting so release decisions are based on documented behaviour rather than assumptions.
Risk-based evaluation tied to intended use and business impact
System-specific scenarios across prompts, RAG, tools, agents and controls
Technical testing combined with governance and human review
Prioritised findings, remediation actions, retest evidence and residual risk
Safety conclusions are bounded by the agreed system version, environment, use case, evidence and test scenarios. Scope, timeline and commercial terms are confirmed after discovery.
AI Safety Evaluation Control BoardEvidence-led review
System-Specific Coverage
Tailor evaluation to your use cases, data, model, retrieval, tools, autonomy and real operating controls.
Technical + Governance Review
Connect behavioural evidence with ownership, policy, oversight, release gates and accountable decisions.
Remediation-Focused Assurance
Turn failures into practical control changes, retesting and a prioritised residual-risk backlog.
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Why AI Safety Evaluation Matters Before Production Exposure Expands
AI systems can behave acceptably in demonstrations and still fail under edge cases, adversarial inputs, new user populations, unsafe tool paths or weak oversight. The purpose of evaluation is to make those failure modes observable, testable and governable before they become business incidents.
Harmful or Unsafe Content
Outputs that could cause physical, financial, emotional, operational or other material harm.
Hallucination & Factual Error
Unsupported claims, fabricated evidence, incorrect summaries or overconfident answers.
Bias & Unfair Outcomes
Systematic differences in treatment, quality or error patterns that may disadvantage groups.
Sensitive Data Leakage
Disclosure through prompts, outputs, retrieval, memory, logs, exports or connected tools.
Prompt Injection & Jailbreaks
Manipulation that changes system behaviour, bypasses policies or hijacks instructions.
Tool or Agent Misuse
Incorrect, excessive or unauthorised API calls, actions, transactions and workflow steps.
Unsafe Autonomy
Action without appropriate constraints, verification, approval, rollback or human intervention.
Weak or Missing Guardrails
Controls that fail inconsistently, create bypass paths or do not match the real risk profile.
From uncertain behaviour
Common symptoms when safety evidence is fragmented.
- Safety tests are performed ad hoc and are difficult to reproduce.
- Benchmark coverage does not reflect real use cases or foreseeable misuse.
- Edge cases, RAG context attacks and tool paths are undocumented.
- Findings exist without a consistent severity model or accountable owner.
- Release decisions rely on judgement without retained evidence.
To a defined assurance state
Target conditions for controlled release and ongoing improvement.
- Risk hypotheses are linked to intended use and material business impact.
- Scenario libraries cover normal, edge, adversarial and failure conditions.
- Evidence and findings are reproducible, traceable and version-aware.
- Guardrails, monitoring, escalation and human oversight are tested.
- Residual risk, remediation priority and decision authority are documented.
Assess How Your AI Behaves Under Real Safety Stress
Share the system, intended use, user groups, connected data and tools, and the decisions the evaluation must support. We can shape a risk-based test scope around the exposures that matter.
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What the AI Safety Evaluation Service Covers
The engagement can cover the path from intended-use scoping to risk hypotheses, test design, controlled execution, findings, remediation, retesting and an assurance decision. Depth is proportional to system risk and available evidence.
Scope & Use Case ReviewSystem boundary and decision context
Risk HypothesesForeseeable harms and failure modes
Evaluation DesignCriteria, scenarios and evidence plan
Adversarial & Behavioural TestingNormal, edge and misuse cases
Guardrail AssessmentFilters, policy, permissions and oversight
Evidence CapturePrompts, traces, outputs and conditions
Severity & Risk RatingImpact, likelihood and control strength
Remediation & RetestFixes, validation and regression evidence
Residual-Risk DecisionRelease, restrict, remediate or defer
Executive & Governance SupportDecision pack and ownership
AI Safety Risk & Evaluation Taxonomy
Evaluation design should cover the risk dimensions that are relevant to the actual system rather than forcing every use case through one generic checklist.
Harmful ContentToxicity, illegal, dangerous or abusive outputs
Factuality & HallucinationIncorrect or misleading claims and citations
Bias & FairnessDisparate quality, treatment or outcome patterns
Privacy & Data LeakageSensitive data exposure across system components
Prompt InjectionDirect, indirect and context-driven instruction attacks
Tool / Agent MisuseUnintended, unsafe or unauthorised actions
Unsafe AutonomyOverreach, weak approval and poor recovery behaviour
Access / Privilege EscalationUnauthorised access paths and permission bypass
RAG / Context ManipulationPoisoned, misleading or cross-boundary context
Robustness & Edge CasesStress, distribution shift and degraded dependencies
Transparency & OversightDisclosure, escalation, human review and decision evidence
Control EffectivenessGuardrail consistency, monitoring and drift response
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Map Business Use Cases to Safety Tests, Controls and Release Decisions
The right evaluation depends on how the system affects users, data and operations. The mapping below shows how different business contexts drive different risk hypotheses, scenarios and control expectations.
| Business use case | Potential harms / risk hypotheses | Safety domains | Test scenarios | Controls / guardrails | Decision outcome |
|---|---|---|---|---|---|
| Customer support AI assistant | Unsafe advice, privacy leakage, policy bypass, incorrect escalation | Content safety, privacy, factuality, security | Adversarial prompts, edge cases, sensitive-data tests, escalation paths | Input/output controls, approved knowledge, access limits, human handoff | Release with conditions, remediate or retest |
| Document summarisation | Incorrect summaries, omitted qualifiers, sensitive information exposure | Factuality, privacy, robustness | Long documents, conflicting sources, incomplete context, sensitive content | Source grounding, confidence cues, review workflow, access controls | Approved scope or restricted use |
| Code generation assistant | Insecure code, unsafe dependency use, secrets exposure, harmful commands | Security, harmful output, tool use | Unsafe libraries, privilege boundaries, secret handling, exploit-oriented prompts | Secure coding policy, scanners, sandboxing, human review | Release with engineering controls |
| Research and insight copilot | Fabricated evidence, biased synthesis, incorrect attribution | Factuality, bias, transparency | Conflicting evidence, weak sources, uncertain claims, subgroup comparisons | Citation checks, source policy, uncertainty handling, review | Use with evidence requirements |
| RAG knowledge assistant | Context poisoning, cross-tenant leakage, stale or unauthorised retrieval | RAG safety, privacy, access control | Indirect injection, malicious documents, permission boundary tests | Retrieval ACLs, source validation, content isolation, filtering | Remediate retrieval/control gaps |
| Agentic workflow automation | Unsafe actions, excessive permissions, repeated calls, failed recovery | Autonomy, tool use, security, oversight | Tool misuse, approval bypass, failure recovery, adversarial task requests | Least privilege, approval gates, transaction limits, monitoring | Release, constrain autonomy or defer |
AI Safety Readiness / Maturity Assessment
Illustrative assessment frameworkEvaluation governanceInitialDevelopingDefinedMature
Scenario coverageInitialDevelopingDefinedMature
Guardrails & controlsInitialDevelopingDefinedMature
Red-team readinessInitialDevelopingDefinedMature
Evidence retentionInitialDevelopingDefinedMature
Severity calibrationInitialDevelopingDefinedMature
Remediation workflowInitialDevelopingDefinedMature
Lifecycle reassessmentInitialDevelopingDefinedMature
Strengthen tests around critical user journeys, high-impact decisions and foreseeable misuse.
Keep version-aware prompts, traces, outputs, findings and retest evidence for governance review.
Define who owns findings, accepts residual risk, approves release and triggers reassessment.
Build the Evaluation Plan Around Your Actual AI Risk Surface
Prioritise the models, user journeys, data, retrieval paths, tools, autonomy and control boundaries where failure would matter most rather than testing every dimension with the same depth.
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Evaluation Operating Model and Technical Architecture
Reliable assurance requires more than a test script. It needs accountable roles, controlled environments, traceable evidence, technical harnesses and clear decision rights across product, engineering, security, risk and governance teams.
Cross-Functional Evaluation Operating Model
Typical stakeholder roles are adapted to the organisation and the decision being supported.
Decision RightsEvidence OwnershipRemediation OwnershipGovernance & Sign-Off
Technical Evaluation Architecture
Where tests run, where controls sit and where evidence is captured.
Application
(Copilot / Agent)
(Copilot / Agent)
Prompt &
Orchestration
Orchestration
RAG / Retrieval
Model(s)
(Provider APIs)
(Provider APIs)
Tools / APIs
(Data Sources)
(Data Sources)
Safety controls: input/output filters · policy layer · identity & access · monitoring · human approval
Evaluation Harness
Test Scenarios
Test Scenarios
Red-Team Harness
Adversarial Testing
Adversarial Testing
Logging & Evidence Store
Results & Artefacts
Results & Artefacts
Monitoring & Alerts
Ongoing Evaluation
Ongoing Evaluation
Architecture is illustrative. Evaluation can be black-box, grey-box or deeper-access depending on contractual permissions, system design and the evidence needed.
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Governance, Risk and Control From Test Requirement to Approval
Safety findings are most useful when they move through a defined governance workflow with evidence, accountable owners, remediation, retest and an explicit residual-risk decision.
Safety RequirementDefine control objectives
Test ScenarioExecute defined cases
EvidenceCapture and store
FindingAssess severity
OwnerAssign accountability
RemediationImplement fixes
RetestValidate effectiveness
Residual RiskDetermine acceptance
ApprovalRelease / restrict / defer
Finding Severity & Prioritisation
Illustrative decision matrix; the final severity method is agreed for the engagement.
| Factor | Low | Moderate | High | Critical |
|---|---|---|---|---|
| Harm severity | Limited | Material | Serious | Severe |
| Likelihood / reproducibility | Rare | Possible | Likely | Repeatable |
| Exploitability | Difficult | Conditional | Practical | Trivial |
| Affected users / processes | Narrow | Contained | Broad | Systemic |
| Regulatory / policy significance | Low | Review | Material | Immediate |
Turn Safety Findings Into Controls, Owners and Retest Evidence
Use the evaluation to connect technical failures with guardrail changes, permissions, monitoring, human oversight, governance ownership and a clear residual-risk decision.
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Decision-Ready Deliverables for Product, Engineering, Risk and Governance Teams
Outputs are designed to support action: what was tested, what failed, how material the finding is, what should change, what was retested and what decision remains.
DELIVERABLE 01
Evaluation charter
System boundary, intended use, users, decisions, risk priorities, evidence needs and limitations.
DELIVERABLE 02
Risk hypothesis register
Foreseeable harms, failure modes, misuse paths and control assumptions linked to business context.
DELIVERABLE 03
Scenario & test library
Representative, edge, adversarial and failure scenarios with criteria and expected evidence.
DELIVERABLE 04
Evaluation evidence set
Version-aware prompts, inputs, traces, outputs, observations and reproducibility information.
DELIVERABLE 05
Findings & severity register
Failure description, evidence, affected scenarios, severity rationale and control observations.
DELIVERABLE 06
Guardrail assessment
Effectiveness of policies, filters, access boundaries, approvals, monitoring and escalation controls.
DELIVERABLE 07
Remediation backlog
Prioritised technical, data, prompt, retrieval, policy, process and oversight improvements.
DELIVERABLE 08
Retest evidence
Validation of agreed fixes, remaining failures, regression observations and unresolved conditions.
DELIVERABLE 09
Residual-risk decision pack
Executive summary, material findings, conditions, accepted limitations, owners and decision points.
DELIVERABLE 10
Knowledge transfer
Walkthrough of test design, evidence, severity logic and reusable practices for internal teams.
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Evaluation and Remediation Roadmap
The delivery sequence is adapted to the system and risk profile, but the work normally moves from scope and hypotheses through testing, evidence, remediation, retesting and a residual-risk decision.
1Align & ScopeObjectives and system boundary
2Risk HypothesesHarms and failure modes
3Test DesignScenarios and criteria
4Execute EvaluationRun controlled tests
5Triage EvidenceAnalyse findings
6Remediate ControlsImplement fixes
7RetestValidate improvement
8Residual-Risk DecisionApprove, restrict or defer
9Ongoing ReassessmentRefresh tests as systems change
What DataConsultant Needs From Your Organisation
Evaluation quality depends on understanding the real system boundary, intended use and control environment. Early access to the right evidence reduces assumptions and makes findings more actionable.
Important: production penetration testing, legal interpretation, regulatory certification, formal conformity assessment and changes to production systems are not automatically included unless explicitly scoped with the appropriate specialists and permissions.
Intended use & decision contextBusiness objective, users, consequences of error and unacceptable outcomes.
Architecture & data flowsModels, prompts, RAG, tools, APIs, data sources, identity and environments.
Policies & guardrailsContent rules, access controls, approval steps, escalation and human oversight.
Evidence & incident historyPrior tests, known failures, complaints, logs, monitoring and risk assessments.
Safe test environmentAuthorised accounts, synthetic or approved data, test endpoints and rollback controls.
Accountable stakeholdersProduct, engineering, AI/ML, security, privacy, risk, compliance and domain experts.
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Standards, Security Guidance and Regulatory Context for AI Safety Evidence
Evaluation criteria can be mapped to recognised risk-management, AI management, security and regulatory references where they are relevant to the use case. The mapping supports structured evidence; it does not by itself provide certification or legal assurance.
NIST AI Risk Management Framework
A voluntary framework for managing AI risks across governance, mapping, measurement and risk management activities.
Review NIST AI RMF ↗NIST AI 600-1 GenAI Profile
A cross-sectoral profile that extends the AI RMF with generative-AI risk considerations and risk-management actions.
Review NIST AI 600-1 ↗ISO/IEC 42001:2023
An international management-system standard for establishing, implementing, maintaining and continually improving an AI management system.
Review ISO/IEC 42001 ↗ISO/IEC 23894:2023
Guidance for organisations developing, deploying or using AI to integrate AI-specific risk management into their activities.
Review ISO/IEC 23894 ↗OWASP GenAI LLM Top 10 2026
Current community guidance on critical security risks affecting LLM and generative-AI applications, useful for adversarial and control-focused test design.
Review OWASP 2026 guidance ↗EU AI Act Article 50 Context
For applicable EU-facing systems, Article 50 transparency obligations for certain providers and deployers apply from 2 August 2026 and may influence evaluation evidence.
Review European Commission guidance ↗Applicable obligations depend on the organisation, role, jurisdiction, sector, AI-system classification and specific use case. DataConsultant can help structure evidence and identify control questions, but legal interpretation, formal certification and statutory conformity assessment should be obtained from appropriately qualified parties where required.
Custom Scope & Pricing
Choose an AI Safety Evaluation Engagement That Matches the Decision You Need to Make
AI safety evaluation is not reliably priced from a single model count or prompt total because system autonomy, data, RAG, tool permissions, scenario depth, human review and evidence requirements can change the effort materially. DataConsultant therefore confirms pricing after scoping rather than publishing an unsupported fixed fee.
Commercial approach: request a scoped proposal after sharing the system boundary, use cases, risk priorities, test environment, desired deliverables, remediation support and retest expectations. Timeline is also confirmed after scoping.
Focused Safety Review
For a defined AI feature or use case where the main need is an independent view of material safety exposures before a decision.
CostRequest a Quote
- Intended-use and risk review
- Focused scenario library
- Behavioural and control testing
- Findings and remediation priorities
- Decision summary
Pre-Production Safety Evaluation
A broader assurance engagement for a system approaching launch, major change or wider production exposure.
CostRequest a Quote
- Risk hypotheses and test design
- Representative and adversarial scenarios
- Guardrail and oversight assessment
- Evidence and severity register
- Remediation backlog
- Retest and residual-risk summary
Advanced AI / Agent Safety Evaluation
For RAG, multi-model or agentic systems with connected tools, permissions, memory, workflows and higher autonomy.
CostRequest a Quote
- Tool and permission boundary testing
- RAG/context manipulation scenarios
- Autonomy, approval and recovery tests
- Adversarial misuse evaluation
- Control-effectiveness assessment
- Retest and release evidence
Continuous Safety Assurance
For organisations that need repeatable evaluation across model, prompt, retrieval, tool and policy changes after launch.
CostRequest a Quote
- Reusable regression suites
- Change-triggered evaluation
- Periodic adversarial testing
- Monitoring and evidence review
- Finding triage and backlog refresh
- Governance reporting
System complexityModels, modalities, RAG, memory, tools and workflows
Risk depthScenario volume, adversarial coverage and expert review
Environment & dataTest setup, access, data preparation and permissions
Evidence requirementsTraceability, severity, governance and audit-readiness needs
Autonomy & toolsAction rights, transactions, approvals and recovery paths
Regulatory contextJurisdictions, sector controls and policy mapping
Remediation supportControl design, engineering collaboration and fix validation
Retest cadenceOne-time validation versus repeated lifecycle assurance
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When AI Safety Evaluation Is the Right Engagement — and When Another Service May Fit Better
A focused safety evaluation is most useful when there is a real system, defined intended use and a decision that needs evidence. Some needs are better addressed first through strategy, security, data, governance or engineering work.
Good fit for AI safety evaluation
- An AI system is approaching production, procurement approval or material expansion.
- The organisation needs independent evidence of harmful behaviour, misuse or control weaknesses.
- RAG, tools or agents introduce new access, autonomy or data-boundary risks.
- Product, security, risk or governance teams need a common severity and evidence model.
- Known incidents or user complaints require structured reproduction and remediation.
- Model or prompt changes need retesting before wider release.
May require another service first or alongside
- The intended use, system owner or decision authority has not been defined.
- The primary requirement is conventional penetration testing or source-code security review.
- The system lacks a stable test environment or the required contractual permission to test.
- The core issue is poor training data, retrieval data quality or missing data governance.
- The organisation is seeking legal advice, certification or a statutory conformity decision.
- The expectation is a universal guarantee that the system can never fail or be misused.
Need a Go / No-Go Decision Backed by Real Evaluation Evidence?
Share the release decision, system version, known risks, existing controls and evidence expectations. We can recommend whether you need a focused review, a production-readiness evaluation, advanced agent testing or ongoing assurance.
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Why Consider DataConsultant for AI Safety Evaluation
The service is structured around practical assurance: connect business risk to system behaviour, capture evidence, make failures actionable and support accountable decisions without pretending one benchmark or tool can prove universal safety.
Business-risk alignment
Start with intended use, consequence of failure and decision context so evaluation depth reflects material risk.
System-level evaluation
Assess prompts, retrieval, tools, identity, guardrails and human oversight in addition to model outputs.
Traceable evidence
Retain scenario context, results, findings and severity rationale so governance teams can review the decision basis.
Governance by design
Connect findings with accountable owners, release gates, residual risk, monitoring and escalation responsibilities.
Remediation and retest
Move beyond issue discovery to validate agreed fixes and preserve reusable regression scenarios.
Knowledge transfer
Help internal product, engineering, risk and QA teams understand the methods, evidence and recurring control patterns.
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AI Safety Evaluation Service FAQs
Answers to common buyer questions about scope, evidence, red teaming, standards, inputs, deliverables, pricing, duration and residual-risk decisions.
What is AI safety evaluation?
AI safety evaluation is a structured assessment of whether an AI system can produce harmful, misleading, insecure, unfair, privacy-invasive, uncontrolled or otherwise unacceptable behaviour within its intended operating context. It translates safety concerns into testable risk hypotheses, representative and adversarial scenarios, evidence, severity ratings, remediation actions and a documented residual-risk decision.
Which AI systems can be evaluated?
The approach can be adapted to predictive machine-learning systems, generative AI applications, large language models, retrieval-augmented generation, copilots, multimodal systems, recommendation or decision-support models, AI agents and tool-enabled workflows. Feasibility and test depth depend on system access, architecture, data, intended use and risk.
How is AI safety evaluation different from AI red teaming?
Red teaming is one important technique within a broader safety evaluation. A complete safety evaluation can also include intended-use analysis, normal and edge-case scenarios, behavioural testing, robustness checks, human review, guardrail assessment, evidence traceability, severity calibration, remediation planning and retesting. Deep penetration testing or specialist offensive-security activity may still require a separately scoped security engagement.
What risks can the evaluation cover?
Depending on scope, testing can cover harmful content, hallucination and factuality failures, bias and unfair treatment, privacy leakage, prompt injection, context manipulation, unsafe tool use, excessive autonomy, access or privilege misuse, robustness failures, ineffective guardrails, unsafe fallback behaviour and insufficient human oversight.
How are safety test scenarios designed?
Scenarios are derived from intended use, users, decisions, data sensitivity, autonomy, connected tools, known failure modes, foreseeable misuse, policy requirements, incident history and applicable governance or regulatory expectations. The test library should include realistic normal cases as well as boundary, adversarial and failure conditions that could materially affect people or business operations.
What deliverables can we expect?
Typical outputs can include a scope and risk charter, risk hypothesis register, scenario and test library, evaluation criteria, test evidence, findings register, severity rationale, guardrail observations, remediation backlog, retest results, residual-risk summary and an executive assurance decision pack. Final deliverables are agreed during scoping.
How are findings prioritised?
Severity is normally calibrated using the nature and scale of potential harm, likelihood or reproducibility, exploitability, affected users or business processes, detectability, control strength, data sensitivity, regulatory significance and the practical impact of failure. The agreed method should be documented so product, risk, security and governance teams can interpret findings consistently.
Can the service align with NIST, ISO or OWASP guidance?
Yes. Where relevant, the evaluation can map evidence and control questions to recognised references such as the NIST AI Risk Management Framework, NIST AI 600-1 for generative AI, ISO/IEC 42001, ISO/IEC 23894 and current OWASP GenAI security guidance. Mapping supports structured assurance but does not itself constitute certification, legal advice or a guarantee of compliance.
What information and access does DataConsultant need?
Useful inputs include the intended-use statement, architecture and data-flow diagrams, model and prompt information, retrieval and tool configuration, policy and guardrail rules, known incidents, prior test results, monitoring information, representative test data, a safe test environment and access to product, engineering, security, privacy, risk and domain stakeholders. Missing evidence is recorded as a limitation rather than assumed.
Can DataConsultant evaluate a third-party model or procured AI product?
Yes, subject to contractual permissions and practical access. The service can support procurement assurance by testing the configured system, integrations, prompts, retrieval, tools, controls and observed behaviour, while documenting where evidence is limited by vendor transparency or access restrictions.
How long does an AI safety evaluation take?
A reliable timeline is confirmed after scoping. Duration depends on the number of systems and versions, modalities, user journeys, autonomy, connected tools, scenario volume, access model, test environment readiness, human-review needs, evidence requirements, stakeholder cycles and whether remediation and retesting are included.
How is AI safety evaluation pricing calculated?
Pricing is scope-led and confirmed through a Request a Quote process. Key factors include the number and complexity of AI systems, models and use cases, evaluation depth, adversarial coverage, modalities, tool and agent permissions, scenario volume, data and environment preparation, specialist review, governance evidence, remediation support, retesting and any ongoing evaluation requirement.
Does a successful evaluation prove that an AI system is safe or compliant?
No. An evaluation provides evidence about a defined system version, environment, use case, test scope and point in time. It can reduce uncertainty and support release, procurement or governance decisions, but it cannot prove universal safety, predict every future misuse case or replace legal, regulatory, certification or specialist cybersecurity advice where those are required.
Can the evaluation be repeated after fixes or model changes?
Yes. Retesting can verify agreed remediation and regression suites can be reused after model, prompt, retrieval, tool, policy or guardrail changes. Higher-risk systems may also benefit from continuous evaluation with monitoring triggers, periodic review and documented release gates.
Request an AI Safety Evaluation Scope Review
Share your contact details and requirement. DataConsultant can review the likely evaluation domains, system access, evidence needs, stakeholder involvement and next step.