Human trust is the real risk in AI.
Ikwe.ai measures where AI systems break human judgment — before those failures scale into crisis.
Enterprise risk & trust evaluations for AI systems deployed in the real world.
As AI becomes more accurate, risk shifts away from model errors and toward human over-trust, miscalibration, and downstream harm. Ikwe.ai is the infrastructure to measure and mitigate that risk before it scales.
Enterprise evaluation of human-trust risk in AI systems
Ikwe.ai evaluates how people respond to AI under real conditions — not just whether the model is correct.
Human trust & risk evaluations
Behavioral evaluation of AI responses under emotional load, pressure, and vulnerability.
Failure modes accuracy misses
Over-reliance, distress reinforcement, escalation misses, and failure-to-repair after harm.
Enterprise-ready reporting
Actionable findings tied to deployment decisions, policy, and mitigation strategy.
Built for teams deploying AI in high-trust contexts
If your AI system influences human decisions, Ikwe.ai is relevant.
AI product leaders
User-facing assistants, copilots, agents, and conversational systems.
Trust & Safety / Risk
Organizations accountable for downstream harm, escalation, and user impact.
Regulated deployers
Health, education, public sector, finance, and other high-stakes environments.
Platforms launching multiple apps
Common evaluation standards across builders, products, and releases.
Paid pilots that convert to annual enterprise contracts
Structured, bounded engagements with predictable scope and compute.
Scoped Pilot (60–90 days)
We define the AI behaviors, use cases, and risk surface to evaluate.
- Bounded scope & timelines
- Secure evaluation workflow
- Typical pilot range: $10k–$50k
Evaluation & Benchmarking
Ikwe.ai runs proprietary trust benchmarks under realistic conditions.
- Safety Gate + longitudinal behavior
- Pattern and dimension scoring
- Compute scoped and capped
Risk & Trust Report
Enterprise-ready report with findings, implications, and recommended actions.
- Failure modes + mitigation roadmap
- Interpretation session with your team
- Optional continuation: ongoing evaluations
Proprietary benchmarks that compound across contexts
Evaluation data compounds across deployments and use cases, improving the benchmark’s practical signal over time.
Measures risks accuracy metrics miss — increasingly relevant as regulation, liability, and enterprise governance mature.
Ready to evaluate trust risk before it scales?
Engagements begin with paid enterprise pilots and convert to annual contracts for teams deploying or scaling AI systems.