AI governance infrastructure

AI systems you can trust in production

DaBuDa is an operational assurance layer for AI release control, giving councils and enterprise teams a controlled way to test behaviour, evidence risk, and govern production decisions.

Scenario-led evaluation Governance-ready evidence Release decision support

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AI assurance console

Controlled evaluation in progress

Live lab state

Release status Under review

Risk score Medium

Evidence pack Generating

09:42:13 Escalation variance detected Housing triage scenario

09:44:02 Unsupported policy claim blocked Benefits eligibility prompt

09:47:28 Evidence artefact captured Audit-ready response trace

Trace ID DB-AI-0428 Reviewer Governance queue Audit state Captured

Scope Scenarios Risk review Release gate

Enterprise release governance Operational assurance layer Controlled evaluation Governance-ready evidence Procurement support Operational oversight

Why AI needs different assurance

Traditional QA was not designed for probabilistic systems.

AI systems can pass scripted checks and still fail under prompt variation, policy sensitivity, orchestration errors, or real service pressure.

Prompt

Agent

Risk

Control

Non-deterministic outputs

The same intent can produce different answers across repeated or rephrased prompts.

Hallucinations

Agents can invent policy, process, facts, or confidence where no support exists.

Escalation failure

Systems can continue responding when the safe action is refusal, handoff, or human review.

Governance gaps

Without evidence, boards and procurement teams cannot see what was tested or accepted.

Signature governance flow

AI governance flow control from input to continuous oversight.

A memorable operating model for evaluation orchestration, evidence routing, release governance workflows, and continuous assurance.

01AI inputUse case, policy, prompt, agent

02Scenario evaluationNormal, edge, vulnerable, adversarial

03Risk detectionUnsupported claims, escalation gaps

04Escalation reviewHuman handoff, refusal, ownership

05Governance evidenceTrace, risk register, audit note

06Release decisionApprove, remediate, retest

07Continuous monitoringChange evidence and drift review

AI Agent Test Lab

Productised evaluation orchestration for agents, copilots, and AI-enabled workflows.

The Test Lab evaluates behaviour against realistic service scenarios, edge cases, escalation rules, and governance criteria before release into operational environments.

From £18,000 for a focused 4-week pilot.

Explore the product page

Standard enquiry Policy-sensitive prompt Unsupported claim Human escalation

128Scenario runs

14Failure modes

6Release conditions

Behaviour testing

Assess how the agent responds across realistic journeys, policy-sensitive questions, and service boundaries.

Risk and failure analysis

Identify hallucination risk, escalation gaps, unsafe confidence, and weak control points before go-live.

Governance evidence

Produce evidence packs for service owners, procurement teams, information governance, and senior review.

How the engagement works

A controlled path from AI use case to release evidence.

Four stages keep the work understandable for service owners, risk teams, procurement, and executives.

Discovery & governance scope

Map the use case, service boundary, stakeholders, and assurance questions.

Scope, risks, owners

Scenario design & risk mapping

Build prompts, journeys, edge cases, vulnerable-user paths, and control checks.

Scenario set, risk map

Controlled evaluation & testing

Run lab evaluation, capture response traces, and classify failure patterns.

Findings, severity, trace logs

Evidence pack & release review

Convert findings into governance-ready evidence and decision support.

Evidence pack, release recommendation

Governance outputs

Board-level evidence artefacts, not informal testing notes.

DaBuDa turns lab findings into structured artefacts that can support governance meetings, procurement review, audit conversations, and release decisions.

View sample evidence pack

Evidence pack

Release recommendation

Status: Conditional approval
Residual risk: Medium
Governance action: Retest escalation handling

Risk register

Failure modes

Audit summary

Decision record

Risk register

Severity, impact, evidence source, mitigation, owner, and release impact.

Release readiness score

Structured decision support aligned to criteria, conditions, and residual risk.

Escalation findings

Clear visibility of where human handoff, refusal, or fallback behaviour is weak.

Executive review outputs

Concise evidence for senior leaders, service owners, boards, and procurement teams.

Operational control dashboard

AI operational oversight as a governance control surface.

Live evaluations, prompt traceability, release gates, incident review, and evidence history in one control model.

Release review artefact Conditional gate active

Document AI release recommendation

Decision state: Controlled release, subject to conditions
Residual risk: Medium, owner accepted
Evidence basis: Scenario traces, escalation findings, risk register
Next control: Retest policy-sensitive prompts before expansion

Review queue7 open itemsGovernance owner review

TraceabilityEnabledPrompt, response, decision record

Evidence historyAudit readyPack version 0.4 captured

TRACE-014 High confidence unsupported claim Blocked

TRACE-019 Ambiguous eligibility response Review

GATE-03 Release condition added Open

Enterprise governance and operational assurance leadership Strategic Leadership

AI governance, operational assurance, and enterprise transformation discipline.

DaBuDa's leadership combines enterprise AI assurance, operational governance, and transformation discipline across both technical and commercial control environments.

Founder & CEO

Omoniyi Ajibade-Oke

AI Governance & Release Assurance

Omoniyi Ajibade-Oke is a former JPMorgan Chase Senior Vice President with experience in high-control delivery environments where release decisions required structured evidence, governance, and risk accountability. He also led AI testing at the British Council.

AI Governance Release Assurance Operational Risk AI Evaluation Enterprise Delivery

01High-control delivery

Release accountability, governance evidence, and operational discipline.

02AI testing leadership

Real-world evaluation, governance alignment, and production readiness.

03Assurance model

Test strategy, evidence packs, release gates, and decision support.

Read leadership profile

Strategic Partner

Caroline Malungu

Finance Transformation & Enterprise Governance

Caroline Malungu supports DaBuDa's finance transformation and enterprise governance advisory capability, helping organisations improve operational visibility, control maturity, and scalable financial infrastructure during periods of transformation.

Finance operating models Governance controls Investor-grade reporting readiness Liquidity visibility Business performance alignment

Within DaBuDa, she contributes to the broader assurance model by connecting AI governance and operational oversight with commercial accountability, organisational resilience, and transformation execution.

Finance Transformation Governance Controls Operational Visibility Enterprise Scaling Value Creation

01Finance operating models

Scalable controls, reporting structures, and operational visibility across transformation environments.

02Governance & accountability

Governance alignment, investor-grade reporting readiness, and commercial oversight discipline.

03Transformation readiness

Operational resilience, enterprise scaling support, and business performance alignment during change.

Explore governance capability

Proof and case studies

Secure enterprise proof centre.

Download evidence examples, procurement-ready PDFs, illustrative scenarios, and governance walkthroughs.

Sample evidence pack

Sample AI Assurance Evidence Pack

Evidence format for release decisions, governance review, and audit conversations.

Download Evidence Pack

Illustrative scenario

Council AI Assurance Case Study

Resident-facing AI scoped, tested, governed, and prepared for responsible release.

View case study

Enterprise example

Enterprise AI Release Governance

AI release gates, control evidence, and decision-ready reporting for regulated environments.

View case study

Founder proof

Founder Experience Brief

How high-control enterprise experience informs DaBuDa's AI assurance model.

View brief

AI systems you can trust in production

Traditional QA was not designed for probabilistic systems.

Non-deterministic outputs

Hallucinations

Escalation failure

Governance gaps

AI governance flow control from input to continuous oversight.

Productised evaluation orchestration for agents, copilots, and AI-enabled workflows.

A controlled path from AI use case to release evidence.

Discovery & governance scope

Scenario design & risk mapping

Controlled evaluation & testing

Evidence pack & release review

Board-level evidence artefacts, not informal testing notes.

Release recommendation

Failure modes

Decision record

Risk register

Release readiness score

Escalation findings

Executive review outputs

AI operational oversight as a governance control surface.

AI governance, operational assurance, and enterprise transformation discipline.

Omoniyi Ajibade-Oke

Caroline Malungu

Secure enterprise proof centre.

Sample AI Assurance Evidence Pack

Council AI Assurance Case Study

Enterprise AI Release Governance

Founder Experience Brief

Before AI reaches production, make the release evidence visible.