We don’t chase bigger autocomplete. We build agents with a self‑model that can reason about their own state, limits, and uncertainty — then act inside hard safety envelopes.
Models maintain an internal, testable representation of beliefs, uncertainty, and capabilities. Plans must respect known limits. This is engineered — not emergent — via constraints, curricula, and evals.
Safety is a continuous system, not a one‑time audit. Human oversight + automated guardrails + third‑party red‑teams and audits.
We use extreme synthetic datasets and curated human corpora — never mass surveillance scraping. Every dataset is documented and provenance‑tracked.
Two‑country lab plan with off‑grid capable sites. Each lab runs >1,000 agents in recursive loops measured against awareness benchmarks.
Measurable tests for self‑model coherence, calibrated uncertainty, and model honesty under stress.
We release in controlled stages with strict scopes and audit trails. Private nodes let partners retain data sovereignty and minimize risk.