SynapsedBuild the capability to deliver trustworthy AI.
We work with your teams to govern, engineer and assess AI systems. Applied research, practical frameworks and purpose-built tools turn trustworthiness objectives into capabilities you can operate and improve.
Start with the decision your team needs to make.
| Your question | A useful starting point |
|---|---|
| Are we ready to govern AI? | Governance & Assessment |
| How do we build trustworthiness into delivery? | Trustworthy AI Engineering |
| What does the application’s behaviour tell us? | Testing & Continuous Assurance |
| Which skills does our team need? | Education & Training |
Consulting that builds
lasting capability.
We bring advisory work, engineering methods and tooling into the same engagement. The aim is to help your team make better decisions, apply controls and review evidence as its AI systems evolve.
Scope, responsibilities and outputs are agreed around your context. Methods, reusable artefacts and knowledge transfer support adoption within your organisation.
AI Governance & Maturity Assessment
Understand your starting point and make AI governance operational. We help leaders connect AI objectives, organisational responsibilities and day-to-day decisions.
What we work on
- Assess AI Team, Processes, Guidelines, Standards and Guardrails through the Synapsed 5D approach.
- Define ownership, policies and review points across the AI lifecycle.
- Translate assessment findings into priorities, accountable actions and an improvement roadmap.
What you take forward
A shared view of current capabilities, a practical governance structure and a prioritised plan your organisation can act on.
A useful starting point for: Organisations starting an AI programme, scaling adoption or reviewing existing governance.
Trustworthy AI Engineering
Bring trustworthiness into the way an AI product is designed, developed and maintained. We work with product and technical teams to turn expectations into engineering decisions and verifiable controls.
What we work on
- Review intended use, requirements, architecture and the boundaries of the AI system.
- Analyse AI-specific threats and failure modes; design human oversight, guardrails and evaluation criteria.
- Connect lifecycle activities with responsibilities, review gates and evidence, informed by AI-T-SDLC research.
What you take forward
An actionable engineering plan, prioritised controls and agreed evidence for design reviews, release decisions and ongoing maintenance.
A useful starting point for: Teams designing a new AI product or strengthening an existing LLM, RAG or agentic application.
AI Testing & Continuous Assurance
Examine how an AI application behaves and what changes after an update. We help teams make testing repeatable and findings useful for remediation and assurance decisions.
What we work on
- Define assessment criteria and test scenarios for privacy, robustness, reliability and intended AI behaviour.
- Investigate prompt manipulation, information exposure, unsafe outputs and unintended actions, where relevant to the agreed scope.
- Record evidence, prioritise findings and repeat tests after changes to models, prompts, retrieval sources or tools.
What you take forward
A scoped assessment with supporting evidence, remediation priorities and a repeatable test plan for future changes. Ongoing campaigns can be agreed as part of the engagement.
A useful starting point for: Teams preparing for release, assessing a deployed application or introducing a regular assurance process.
AI Education & Practical Training
Give decision-makers and practitioners a shared language and the skills to apply it. Training connects trustworthy AI principles with the work participants need to perform.
What we work on
- Executive sessions on AI governance, accountability and adoption decisions.
- Practitioner workshops on AI lifecycle methods, assessment and control design.
- Hands-on AI testing exercises and evidence review, adapted to the audience and agreed learning objectives.
What you take forward
Shared understanding, practical methods and reusable exercises that help participants apply what they learn in their own roles.
A useful starting point for: Leadership teams, AI product owners, engineers and governance or assurance practitioners.
Methods and tools behind the work.
Our research informs the frameworks and testing tools we bring into engagements. We select and adapt them to the question your team needs to answer.
Synapsed 5D Assessment
A structured view of organisational readiness and improvement priorities.
Explore the assessment
AI Governance Framework
A method for connecting responsibilities, lifecycle activities, controls and evidence.
Explore the framework
TrustInspect
An AI testing initiative that connects observations with reviewable evidence.
Explore the projectProduct presentations are illustrative. Visit each project page for its current scope and availability.
Start focused. Apply the work. Keep improving.
Scope the question
Agree the AI use case, organisational context and decisions to support. Define responsibilities, access requirements and expected outputs.
Work with your team
Apply the relevant methods, review findings together and turn them into practical actions, reusable artefacts and working knowledge.
Support adoption
Agree ownership and follow-up. Where useful, establish recurring reviews, test campaigns or further training as your needs evolve.
What does your team need to move forward?
Tell us about your AI initiative and the question you need to answer. We will help define a focused starting point.