Evaluation Infrastructure
for Frontier AI
We operate confidential evaluation systems for advanced models where internal benchmarks break down.
Model
Agent
Eval
Signal
The Bottleneck Isn't Training. It's Trust.
Long-horizon drift
Silent regressions
Agent coordination failure
Reward hacking
Task brittleness
Alignment decay
These failures don't show up in static benchmarks, but they show up in production.
We Are
- Evaluation infrastructure
- Human-signal operators
- System-level testers
- Confidential partners
We Are Not
- A SaaS dashboard
- A label marketplace
- A benchmarking leaderboard
- A training vendor
eval.qa is the operating system beneath our engagements
Not exposed publicly. Not generalized. Deployed selectively.
eval.qa
Task definitions
Human signal calibration
Scoring primitives
Drift detection
Audit trails
Partnership Operating Model
Scope
Confidential problem framing with senior operators
Eval
Isolated runs using human + system testing
Signal
Structured findings, failure modes, confidence intervals
Expand
Only if value is proven
OptionalDesigned to Reduce Partner Risk
No production data required initially
Isolated evaluation pipelines
No cross-client data leakage
Optional on-prem / VPC deployment
No attribution without consent
Our default posture is restraint.
Engagement Philosophy
We work with a limited number of frontier teams at a time.
We prioritize depth, confidentiality, and system-level rigor over scale.
This is not a sales funnel.
It's an operating partnership.
Request Private Evaluation Engagement
[email protected]Introductions reviewed by senior operators only.