No baseline.
The system was deployed without capturing the performance baseline. Without a baseline, there is nothing to compare against.
AI Assurance
Independent AI Assurance for Regulated Enterprises
If your regulator or board asked tomorrow for evidence that your AI systems are safe, accurate, and compliant, could you provide it today?
Most Kenyan enterprises cannot. They have deployed AI into customer service, credit decisions, fraud detection, underwriting, and operations. The technology works. The evidence base does not exist.
Enterprise AI Africa delivers independent AI Assurance: testing, evaluation, validation, and verification for AI systems in production and pre-deployment, combined with the governance and evidence-workflow layer that turns findings into executive decisions.
Independent testingDecision-ready evidenceKenya & East Africa
The evidence gap
The regulator will ask. The board will ask. And most organisations have no answer.
AI systems are now making decisions that affect customers, employees, patients, and citizens. Credit approvals. Claims decisions. Fraud flags. Clinical triage. Hiring shortlists. Public service eligibility.
When one of those decisions goes wrong, or when a regulator, auditor, or board asks for evidence that the system is safe, most organisations can describe the system. They cannot prove the decision.
Three failure modes we see repeatedly:
No owner.
The AI decision was never assigned to a named person. Accountability sits with a team, a committee, or a vendor.
No evidence trail.
The decision cannot be reconstructed. What data was used? What threshold applied? Who authorised the outcome?
“A policy document is not evidence. A vendor claim is not evidence. Independent testing is evidence.”
What we test
What AI Assurance Covers
We test AI systems we did not build, under conditions that reflect how they will actually be used.
Accuracy and Reliability
Does the system perform reliably for the intended user workflow, under realistic input conditions, not just ideal demonstrations?
Robustness and Failure Modes
How does the system behave when information is incomplete, ambiguous, corrupted, adversarial, or outside its expected context?
Bias and Fairness
Are bias, unfairness, and differential outcomes across claimant or customer groups being tested rather than assumed away?
Security and Data Boundaries
Are access controls, credential isolation, data boundaries, and logging appropriate for the sensitivity of the data the system handles?
Human Oversight
Where is human review mandatory? What are the approval thresholds? Can a person intervene before a failure scales?
Auditability and Evidence
Can every AI-driven decision be reconstructed? What data was used, what policy applied, who authorised the outcome?
AI Types We Cover
Assurance testing is configured for the specific type of AI system in scope. Generic benchmarks are not enough.
Language and Conversational AI
Chatbots, RAG systems, customer service assistants, and copilots answering customer or employee questions.
Vision and Document Intelligence
Image recognition, document processing, medical imaging, and identity verification systems.
Agentic and Tool-Using AI
Agents that call APIs, execute workflows, modify systems, or take actions with real operational consequences.
Predictive and Decision Models
Credit scoring, fraud detection, underwriting, eligibility, and risk classification models.
Aligned With the Standards That Matter
AI Assurance is delivered against the frameworks your regulator, board, and auditors already recognise.
KEBS KS 3007:2025
Kenya's national AI governance standard. Assurance findings map directly to the controls required for compliance.
Kenya Data Protection Act 2019
Assurance testing includes data boundary enforcement, consent verification, and personal data protection checks.
ISO/IEC 42001 and NIST AI RMF
International alignment for organisations operating across multiple jurisdictions or serving global clients.
Assurance is delivered in the context of the regulatory environment you operate in, not as a generic framework applied from outside.
How it works
From Decision Threshold to Deployment Decision
AI Assurance is a structured process, not a report. It produces evidence that leaders can act on.
- 01
Define
We convert the business need into clear acceptance criteria: intended outcome, required quality level, risk tolerance, and accountable owner.
- 02
Challenge
We test the system under relevant, difficult, and underrepresented scenarios, not just a static test set or a vendor demonstration.
- 03
Test
We apply fit-for-purpose assurance testing for the AI type and use case: language, vision, agents, or autonomy. The output is evidence relevant to the procurement or deployment decision.
- 04
Evidence
We give decision-makers a defensible fact base. Findings, remediation conditions, accountable owners, and the route to re-test are made explicit.
The customer retains ownership of data, approvals, residual-risk acceptance, and the production deployment decision. The decision is go, remediate, constrained deployment, or no-go.
What you receive
The Executive Decision Pack
Not a slide-based maturity assessment. Application-level evidence and a defensible decision framework for leadership.
- Current-state assessment of the AI system in its intended operating context
- Independent testing findings across accuracy, robustness, bias, security, and oversight
- Identified failure modes and residual risks
- Remediation recommendations with priority and effort
- Governance and evidence-workflow design for continuous assurance
- Board-ready executive decision pack with recommendation: go, remediate, constrained deployment, or no-go
- Roadmap for monitoring, re-testing, and material change management
“Leadership should not have to choose between speed and certainty. AI Assurance delivers both.”
Who this is for
Built for Regulated Enterprises
AI Assurance is most valuable where AI decisions carry real consequences for customers, patients, citizens, or employees.
Financial Services
Banks, insurers, fintechs, SACCOs, and payment providers deploying AI for credit, fraud, claims, customer service, or AML.
Healthcare
Hospitals, clinics, and health systems adopting AI for clinical decision support, triage, patient services, and operational workflows.
Public Sector
Government ministries, county governments, and public agencies using AI for citizen services, eligibility decisions, and document intelligence.
Energy and Infrastructure
Utilities, energy providers, and infrastructure operators deploying AI into operational, maintenance, and safety-critical systems.
Telecommunications
Mobile network operators and service providers using AI for customer operations, network management, and fraud detection.
Professional Services and Education
Firms, universities, and training institutions adopting AI for client work, student services, research, and knowledge workflows.
Investment
Engagement Investment
AI Assurance engagements start from KSh 350,000, depending on the number of AI systems in scope, the complexity of the use case, and the evidence requirements of the regulator or board.
Final pricing is confirmed against scope and requirements after a scoping call.
Single System Assurance
From KSh 350,000
One AI system, one use case. Full assessment, testing, and executive decision pack.
Portfolio Assurance
Custom quote
Multiple AI systems across one business function or subsidiary. Prioritised testing, shared evidence framework, consolidated decision pack.
Enterprise Assurance Programme
Custom quote
Organisation-wide AI assurance capability, continuous monitoring, and annual re-verification.
Annual engagement
If You Cannot Prove It, You Cannot Scale It.
AI adoption in regulated sectors will be governed by evidence, not ambition. The organisations that build the assurance layer now will be the ones that move fastest, defend their decisions with confidence, and earn the trust of regulators, boards, and customers.
Enterprise AI Africa is selecting its first AI Assurance engagements now.
Need enforceable controls alongside independent evidence? Explore AI Governance Guardrails.