The compound-learning moat.
Every engagement contributes back. Proven techniques reinforce. Failed ones decay out. Engagement #100 is measurably sharper than engagement #1. Every number on this page is queryable against our public endpoint.
11 specialist agents.
Each agent runs its own playbook, keeps its own belief store, and carries its own model assignment. Orchestrator coordinates. Specialists execute.
Expected solve rates, by stack.
Before dispatching, the orchestrator loads the framework profile for your stack. Playbook, preferred agent order, known gotchas, and expected solve rate — all calibrated from prior engagements.
Framework data temporarily unavailable.
Bayesian confidence,
temporal decay.
Every belief lives in a feedback loop. Confirmation raises confidence asymptotically. Failure takes a 25% haircut. Beliefs not confirmed in 90 days decay toward irrelevance.
The top 30 beliefs — ranked by confidence × recency × framework match — get injected into every agent prompt. Not all 500. The 30 most relevant.
Discovered
An agent surfaces a novel pattern during an engagement. Confidence starts at 0.5. Status: untested.
Validated
The pattern is tested on a new target. If it leads to a finding, confidence climbs: conf + (1 - conf) × 0.15. If it fails, 25% haircut.
Production
After ≥3 confirmations with confidence > 0.8, the technique gets promoted to production. It becomes a load-bearing part of the playbook.
Retired
If a production technique starts failing, confidence decays. After a threshold, it's retired. No manual curation — the system self-corrects.
Your engagement adds to the brain.
When we test your stack, we don’t just find what’s there. We add what we find back into the system for the next engagement. Every customer raises the ceiling.
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