Governed AI assurance for councils and enterprise
AI Systems You Can Trust in Production
Bridge the gap between experimental AI and live-service deployment with governed evaluation, reviewable evidence, and stronger operational control.
Laboratory
AI Agent Test Lab
A structured environment for evaluating AI behaviour, stress testing edge cases, and generating evidence that can stand up in governance and procurement conversations.
Behaviour evaluation
Assess how agents respond across realistic prompts, service pathways, and policy-sensitive scenarios.
Scenario testing
Explore edge cases, escalation points, and service failure modes before AI reaches live users.
Risk measurement
Track reliability, control adherence, and operational risk indicators in a way stakeholders can review.
Evidence generation
Produce structured evidence to support review meetings, approvals, audit trails, and procurement due diligence.
The Trust Gap
Modern AI adoption is moving quickly, but governance and release discipline are not always moving with it.
01
Adoption pressure
Teams are being asked to deploy AI into live services before evaluation and governance structures are mature enough.
02
Evidence gap
Stakeholders need reviewable evidence to support scrutiny, procurement, information security, and governance decisions.
03
Operational risk
Weak observability and unstructured release decisions can turn model issues into live-service problems very quickly.
Core capabilities
What DaBuDa does
Provide the structure needed to evaluate AI systems, generate governance evidence, and make release decisions with stronger control.
01
Evaluate behaviour
Assess outputs against expected service behaviour, exception paths, policy constraints, and real operating conditions.
02
Generate evidence
Create evidence outputs that governance, information security, procurement, and service leadership teams can actually review.
03
Control release
Support go-live decisions with visible thresholds, approval checkpoints, and clearer operational readiness conditions.
The Assurance Workflow
Discovery
Map the service context, governance requirements, and assurance scope.
Integration
Connect prompts, workflows, controls, or agent logic into an evaluation-ready environment.
Evaluation
Run scenario sets, capture findings, and generate evidence against agreed criteria.
Validation
Support release decisions with review checkpoints, approvals, and clear conditions.
Monitoring
Track live performance and maintain assurance visibility after go-live.
Built for high-stakes environments
DaBuDa gives different stakeholders the information they need to make better AI decisions.
Councils
Support citizen-facing AI with stronger controls, accountability, and live-service assurance.
Governance teams
Review evidence, approvals, and control points from one clearer assurance flow.
Service owners
Get visibility on whether AI-enabled services are behaving as expected before release.
Enterprise teams
Apply the same governed evaluation approach in regulated delivery environments.
Governance and procurement
DaBuDa is designed to work with enterprise and public-sector scrutiny rather than asking buyers to suspend it.
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Reviewable criteria
Evaluation methods and outputs built to support real governance review, not just internal testing notes.
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Audit-friendly documentation
Evidence and decision records structured so they can support internal assurance, audit, and procurement due diligence.
Founder
Omoniyi Ajibade-Oke
A senior technology and quality leader focused on AI assurance, digital quality, release governance, and operational confidence in complex delivery environments.
DaBuDa exists to bring discipline, evidence, and controlled delivery into AI adoption conversations that are often moving faster than governance can keep up.
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Contact
Start a secure conversation
If you want to discuss a demo, procurement enquiry, or an AI assurance engagement, DaBuDa can start with a focused email-based conversation.
Headquarters
London, United Kingdom