Assurance at the end inherits the design.
Permissions, data paths and vendor choices made in the first weeks are expensive to unwind once integration is finished and a launch date is public.
Services /AI Deployment
AI Deployment
Support
Make assurance part of your deployment, from architecture decisions to production readiness.
Discuss your implementationBring assurance into the process.
Finding a design weakness just before launch can be expensive. Earlier assurance brings risks into architecture, control design and testing decisions while there is still room to address them.
Deployment support is independent assurance carried out alongside implementation, rather than as a single test at the end. We work with your delivery team and your vendors while architecture, access and controls are still being decided.
The aim is to find design weaknesses while they are still design weaknesses. A late test can still be useful. It is a poor moment to discover that retrieval, tool permissions or oversight cannot support the use you intended. This is guidance and testing. We do not sell the system, and we do not replace the team building it.
Risks that get
designed in early.
Vendor selection is often a feature comparison.
The questions that matter for risk are what the system can reach, what it can change and how a failure is noticed. Those are easier to ask before the contract shape is fixed.
Acceptance tests miss the boundary.
Functional tests show that a happy path works. They rarely show what the system does with hostile content, an ambiguous request or a permission it should not have.
Readiness stays vague until the launch meeting.
Without an agreed picture of residual risk, the go-live decision becomes a debate about appetite rather than a review of evidence.
System-specific.
Evidence-led.
- 01Architecture and integration designHow data, identities, models and tools will be connected, and where that design creates access or failure modes that are hard to remove later.
- 02Intended use and risk scenariosWhat the system is for, who depends on it, and the specific ways it could cause harm, error or exposure in that use.
- 03Vendor selection and assessmentThe assurance questions worth asking of a product or supplier, and the evidence that should exist before a choice is treated as settled.
- 04Access boundaries and control designHow permissions, segregation and human approval are meant to work, and whether the design can actually enforce them.
- 05Pre-production testing plansWhether the tests planned before launch cover misuse, adversarial input and permission failures, not only functional acceptance.
- 06Governance and readiness criteriaWho will approve go-live, what evidence they will have, and which conditions should block or limit the release.
A clear path
through assurance.
01
Set the use and the risk questions.
Intended users, decisions, data and consequences are agreed before design reviews start, so later testing has something to judge against.
Earlier insight. Better deployment decisions.
- Architecture review findingsSpecific design issues in the proposed system, raised while the architecture can still be changed.
- System risk mapA structured view of intended use, data, tools, users and the failures that would matter in this deployment.
- Assurance and test planWhat should be tested before production, by whom, and which results the launch decision depends on.
- Control recommendationsChanges to access, oversight, logging and vendor requirements, written for the people who can implement them.
- Pre-production testing evidenceResults from agreed tests against the system as built, including what was not possible to test yet.
- Deployment readiness recommendationsWhether the implementation is ready for the access proposed, and which conditions should accompany that judgement.
Deliverables and testing scope are agreed at the start of the engagement.
Fewer surprises at
the launch decision.
Design issues found while they are still design issues.
Access, retrieval and oversight problems are raised before they are buried in integration work and a fixed launch date.
Vendor choices with an assurance question attached.
Selection is informed by what the product can reach and how it fails, alongside capability and cost.
A test plan that matches the risk.
Pre-production testing covers the boundaries that matter for this system, not only the paths that show it working.
A launch discussion with evidence in the room.
The people asked to approve go-live have findings, open risks and conditions, rather than a status update.
Built around
your responsibility.
Organisations implementing enterprise AI, extending an existing AI application or introducing agents into operational workflows. We work alongside your delivery team and vendors.
- 01An implementation has started and assurance has not.Architecture and vendor choices are underway. Waiting for a finished system means testing a design that is already expensive to change.
- 02An existing system is being given new access.A working assistant or workflow is about to gain tools, data or autonomy, and the original design did not assume that access.
- 03Several teams need a shared picture of risk.Delivery, security, risk and the business sponsor are making decisions on different assumptions about what the system will be allowed to do.
Where this sits.
When the system is built and a go-live decision is the immediate question, deployment assurance is the focused test. Product assurance is the brief for a vendor preparing what you might buy. Continuous assurance takes over once the system is in use and keeps changing.
01
AI Deployment Assurance
Independent testing before AI enters your organisation’s workflows, data and decisions.
02AI Product Assurance
Independent validation for AI vendors preparing their products for enterprise use.
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.