About UpCheckAI
AI Systems Are Getting More Capable, and Harder to Validate
UpCheckAI exists to answer one question for every AI system we touch: can it reliably do the job it was built for?
Why We Exist
AI systems are shipping faster than they are being tested.
A response can sound correct while the system used the wrong tool, violated a policy, or failed the workflow entirely.
What We Do
- Agent evaluation
- RAG & knowledge evaluation
- Red-teaming & adversarial testing
- Human evaluation & verification
- Multilingual & cultural evaluation
- Regression testing
What Makes Us Different
Automated evaluation handles scale. Human reviewers handle ambiguity. Every important failure is preserved as a permanent regression test, including evaluation for language and cultural context, not just English-language behavior.
Founder
“A passing demo isn't a working system.”
UpCheckAI was founded by Clement Ondimu after seeing AI agents and applications ship on the strength of a demo rather than a test suite, and fail in ways a simple response-quality score would never catch.
Through experience working within international AI-related delivery environments, Clement recognized that rigorous evaluation, combining automated checks with expert human judgment, including deep multilingual and cultural review, was missing from how most teams ship AI.
UpCheckAI is being developed as a long-term platform for AI reliability engineering: evaluation, human verification, and continuous regression protection for production AI systems.
Why This Matters Now
AI agents and applications are shipping faster than they can be reliably tested.
Evaluation increasingly requires testing full system behavior, tools, actions, and policies, not just model responses.
AI systems deployed across markets need evaluation for language and cultural context, not just translation accuracy.
UpCheckAI closes that gap by combining automated evaluation, human judgment, and multilingual expertise into one evaluation practice.
Initial target markets: Europe and North America, reflecting existing vendor relationships and strong, growing demand for multilingual AI evaluation services in both regions.
Evaluation Rigor & Data Security
Every engagement is governed by the same two principles: systematic evaluation and rigorous data governance.
Evaluation Rigor
Systematic, not incidental
- Every evaluation passes through defined verification gates before delivery
- Independent human reviewers, separate from automated scoring
- No single point of failure in judgment or evidence handling
- Explicit rubrics defined before a single test case runs
- Final approval check before every client delivery
Data Security
Planned enterprise operating framework
- Client-controlled environments for all evaluation work
- Azure Virtual Desktop (AVD) / secure cloud workspace approach
- No local downloads of client systems or data
- Evaluator NDAs on every engagement
- Security and privacy training for all evaluators handling client data
- Role-based access, scoped per project
- Regional data residency for EU and North American engagements
This reflects how UpCheckAI is designing its security posture as it formalizes enterprise engagements.
Evaluation Is Systematic, Not Incidental
Every evaluation passes through defined gates before delivery. No single point of failure.
Client
Evaluation scoping
Evaluation Gate
Test suite design & rubric definition
Automated + Human Evaluation
Execution across the full test suite
Independent Review
Separate from automated scoring
Final Report Approval
Pre-delivery sign-off
Client Delivery
Evidence report + regression suite
Every evaluation enters through scoping and rubric design before a single test case runs, passes through independent review separate from automated scoring, and receives a final approval check before delivery.
Native to East Africa, Built for Global AI
Native English and Swahili evaluators
Native speakers evaluating language understanding, not just checking translation.
Cultural & contextual review
Does the AI respond appropriately for local terminology and norms, not just grammatically correctly?
Regional safety judgment
What counts as unsafe or inappropriate varies by market. Our evaluators catch what a generic filter misses.
Deep domain familiarity
Evaluators who understand the real workflows an AI system is meant to serve, not just the language.
Growing evaluation ecosystem
Increasing technical capability in evaluation and quality operations across East Africa.
Enterprise quality, efficient delivery
Rigorous evaluation standards without the overhead of larger, generalist vendors.
Enterprise Data Security
Client-controlled environments, Azure Virtual Desktop, no local downloads, evaluator NDAs, and regional data residency. Full security framework on our security page.