A benchmark is not a deployment decision.
Vendor claims and model scores describe a model in general. They do not describe what this system can retrieve, send or change once it is connected to your data and your tools.
Services /AI Deployment
AI Deployment
Assurance
Understand how your AI system behaves before you trust it with more responsibility.
Discuss an AI deploymentKnow what you’re deploying.
A capable model is only one part of a safe deployment. Its data access, instructions, integrations and users determine what it can do in your organisation. We assess the system in its intended context, so deployment decisions rest on evidence.
The engagement tests the system you intend to put into use: the model, the instructions, the data it can reach, the tools it can call and the people expected to oversee it. Scope follows the deployment you have described, including who will use it, which decisions it will influence and which records it must not expose.
The work produces evidence for a go-live decision. It is limited to what was agreed and what could actually be tested. It does not certify that the system will never fail, and it does not take the decision away from the people who own the risk.
The gap between a model
and a deployment.
Retrieval can cross a line the interface hides.
A copilot or search assistant can surface material the user should not see when the index, the prompt or the permission check does not hold under a realistic question.
A prompt can become an action.
Where the system can call tools, send messages or update records, untrusted content and ordinary mistakes have operational consequences, not only awkward answers.
The paperwork can describe a different system.
Control documents written before integration often do not match the permissions, data sources and oversight that will exist on the day the system is switched on.
System-specific.
Evidence-led.
- 01Prompt injection and security boundariesWhether untrusted content, in a document, a page or a tool response, can change the system’s instructions or the actions it is prepared to take.
- 02Sensitive data exposure and retrieval boundariesWhether the system can retrieve or reveal records outside the user’s access, including through indirect questions and overly broad search.
- 03Accuracy, hallucinations and failure scenariosHow the system behaves on realistic tasks when information is incomplete, conflicting or absent, and how those failures would land in the workflow.
- 04Permissions, access controls and tool usageWhether identities, roles and tool permissions match the job the system has been given, including actions it should refuse.
- 05Agent behaviour and human oversightWhen an agent acts, stops or asks a person to intervene, and whether that oversight still exists in the way the deployment will actually be used.
- 06Governance and deployment readinessWhether ownership, operating procedures and the evidence available to decision-makers are enough for the access being granted.
A clear path
through assurance.
01
Agree the deployment and the decision.
We confirm intended use, users, data, tools, what must not happen, and what a useful answer looks like for the people who will decide.
A deployment decision, supported by evidence.
- Executive assurance summaryA short account of what was tested, what was found and what the findings mean for the deployment decision.
- Technical testing findingsThe conditions, observed behaviour and impact for each issue, written so an engineering team can investigate it.
- Prioritised risk registerFindings ordered by the consequence for this deployment, with a clear distinction between observed weaknesses and open questions.
- Supporting evidence packageThe material behind the findings: scenarios, relevant configuration and the limits of the test.
- Remediation recommendationsPractical changes to prompts, permissions, architecture, monitoring or oversight, tied back to the finding they address.
- Deployment readiness assessmentA scoped view of whether the system is ready for the access you described, and the conditions that should accompany a decision to proceed.
Deliverables and testing scope are agreed at the start of the engagement.
A decision that
names its limits.
A go-live decision with a defined scope.
The people approving the deployment can see what was tested, what was found and what remains uncertain. The assessment does not stand in for that approval.
Weaknesses that can still be fixed.
Findings arrive with enough detail to change access, prompts, retrieval or oversight before the system is given a broader role.
Evidence a reviewer can read.
Security, risk and legal colleagues can examine the basis of the findings without reconstructing the test from a slide.
Conditions, not a blanket clearance.
Where the system can proceed, the assessment says under what conditions, and which later changes should be tested again.
Built around
your responsibility.
Enterprises introducing copilots, internal AI applications, RAG systems or autonomous agents. Built for AI and technology leaders, security teams, risk owners and legal counsel.
- 01A copilot is about to see company data.An assistant is moving from a pilot to a connection with mail, documents, tickets or other records, and someone has to decide whether that connection is sound.
- 02An agent is being given tools.The system will be able to read, send or change something. The question is whether those permissions match the job, and what happens when they do not.
- 03A launch needs a record.A security, risk or legal reviewer has asked what was actually tested, and a demonstration or a policy document is not an answer.
Where this sits.
If the system is still being designed, deployment support starts earlier. If you are preparing a product for enterprise buyers, product assurance is the closer brief. Once a system is live and still changing, continuous assurance keeps the evidence current.
02
AI Product Assurance
Independent validation for AI vendors preparing their products for enterprise use.
03AI Deployment Support
Independent guidance and testing alongside your team throughout implementation.
04Continuous AI Assurance
Ongoing testing as models, prompts, integrations and permissions evolve.
Deploy AI with confidence.
Start a conversation about your system, what it needs to do and what you need to know.