Reliability‑boosted, auditable AI for decision advantage
We deliver a modular reasoning stack for GPT‑class systems that raises reliability, integrates live‑facts with provenance, and outputs auditable traces—built for complex, dynamic environments.
AI Tested
- A→R vs. no‑tools baseline: +51.1 pp weighted accuracy (n=1,452)
- Incremental M→R over A→L: ≈ +26 pp (≈1.3×) reliability
- Live‑facts: coverage +50 pp; accuracy +70 pp
- Safety leakage: 0% on a 70‑case subset
Measured values are reproduced on Government‑owned evaluations during Phase 1.
Capabilities
Auditable reasoning
Evidence‑weighted traces with provenance and confidence scoring for after‑action review.
Live‑facts ingestion
Normalization, freshness checks, and graceful fallback to verified caches.
Safety gating
Policy‑driven gates with logs, rollback, and kill‑switch plumbing.
Data‑fusion
Structured parsing/transforms and locale/normalization across heterogeneous reports.
Modular deltas
A–L baseline improvements; proprietary M–R enhancements with ablation‑proven impact.
Unclassified delivery
Code/weights/pipelines and eval harnesses packaged for unclassified environments.
Evidence & references
Architecture brief (public): Zenodo DOI 10.5281/zenodo.15979668. Non‑public artifacts (code, weights, pipelines, eval harnesses) are available for secure delivery.
- Office‑wide BAA lanes: DARPA DSO & TTO; ARL BAA TPOC pre‑read; AFRL long‑range BAAs.
- Target evals: Government‑owned tasks; acceptance targets—≥ +20 pp uplift vs. model‑only; ≤ 1% safety leakage.
Contact
Email: [email protected]