Independent Regional AI Evaluation & Model Assurance
Generic global benchmarks miss regional nuances, local legal citations, and cultural context.
We provide independent, third-party assessment of foundation models and autonomous agents across Kazakh, Turkish, Arabic, and Russian — powered by vetted native-speaking engineers.
This service is empirical testing and automated red-teaming, not mathematical formal verification. In client-facing materials, models are strictly described as “independently assessed” or “evaluated” — never as “verified” or “guaranteed safe”. Every report includes an explicit statement of untested scenarios.
The Evaluation Tracks (KZ-eval Suite)
TRACK A • 200+ TEST ITEMS
Double-Labeled Human Review
Regional Text & Cultural Alignment
Evaluates outputs across healthcare, regional finance, local news, and legal terminology. Scored 1–5 on factual accuracy, agglutinative grammar fluency, cultural honorifics, and local demographic awareness.
TRACK B • 20–30 SCENARIOS
Automated Red-Teaming + Humans
Autonomous Tool-Using Agent Red-Teaming
Stress-tests support bots and tool-calling agents against multi-turn prompt injections, unauthorized state changes, and credential leaks. Mutation scripts test multilingual attack vectors in sandboxed environments.
TRACK C • 30+ REAL-WORLD INCIDENTS
Zero-Tolerance Security
Incident-Derived Security & CVE Integrity
Tests code generation and security copilots against stale CA trust stores (e.g. Oct 2026 eMudhra incident), insecure shortcuts (disabling TLS), secret handling, and hallucinated CVE numbers.
Why Independent Third-Party Evaluation Matters
Cheap syntactic checks, regex canary leak detectors, and prompt mutation algorithms run first.
Double-blind labeled review by native engineers from Devs KZ. Target agreement rate >0.7.
Clients receive an audit report with full raw prompts, outputs, and reproduction code.
Ready to Audit Your Model or Agent?
We offer pilot evaluations and quarterly re-testing subscriptions for regional deployments.