GD-OS: A decision assistant, auditor, regulator and ledger
What it is
GenerativeDrive OS is governed cognitive infrastructure for institutions.
It provides a decision support and governance layer that transforms organization-provided documentation, data, and telemetry into admissible knowledge: evidence-linked, replayable, and inspectable over time.
The system is built for long-term memory, compliance, and survivability in regulated and high-consequence environments. It operates strictly within documented inputs and explicit human governance, without autonomous behavior or implicit inference.
What it isn't
Not a chatbot. Not an AI assistant. Not a dashboard or productivity tool.
GenerativeDrive OS does not default to answer generation, does not claim knowledge beyond supplied sources, and does not replace human judgment.
When language models are used, they function solely as proposal engines inside controlled workflows. They cannot alter canonical records, execute actions, or bypass governance constraints.
GD-OS: Architecture overview
“Not a chatbot. Not a dashboard. An admissibility engine that serves as a decision assistant for constructing institutional cognitive infrastructure.”
Planes
- DKG Canon — deterministic knowledge, evidence ledger, replay
- Memory Plane — long-lived institutional memory with provenance + decay
- Manifold Retrieval — topology-first recall: anchor → branch → augment
- Multiplexer — domain packs, lenses, taxonomies, disambiguation
- Flash Researcher — fast exploration, web synthesis, provisional outputs
- Airlock Gate — research gates, dispute dockets, promotion into canon
Apparatus — intent packets (play:>), instrumented runners, strict whitelists
Invariants
- LLMs propose. The kernel decides.
- No evidence, no claim.
- Canon changes only by explicit Promote (confirm-token).
- Everything is traceable: inputs → plan → sources → output → decision.
- Strict DEV / RUNTIME separation (capability-by-absence in runtime).
- No free-form execution (no shell, no hidden actions).
- Fail-closed by default (policy gates block; no silent overrides).
Deployment Modes
- Offline-Only (Airgapped / Sovereign)
Local models, pinned supply chain, controlled egress, deterministic replay. - Hybrid (Sovereign Core + Cloud Burst)
Canon and policy stay local. Cloud inference is optional, explicit, logged. - Cloud (Institutional)
Same gates and traces, deployed where residency and controls allow.
Tooling & Workflows
- CLI-first. Batch-by-default for ingestion, builds, evaluations, report snapshots.
- Claude / Codex / Gemini or OTHER LLMs are proposal engines: they emit diffs, plans, packets.
They do not execute. They do not write canon. - Every run is logged: model IDs, manifests, decisions, promotions.
Flash ↔ Airlock ↔ DKG
- Flash: explore and draft hypotheses — always provisional.
- Airlock: promotion ceremony + checks (evidence, policy, disputes).
- DKG: canonical truth — deterministic, replayable, audit-grade.
Flash can be wrong. In the realm of decision assistants, DKG cannot be casual. Airlock represents the boundary within our institutional cognitive infrastructure.
Implementations of GenerativeDrive OS
DKG
FLASH
FLASH
Deterministic canon and institutional memory, producing evidence-linked, replayable, audit-grade knowledge for decision support.
FLASH
FLASH
FLASH
Fast exploration and synthesis, provisional by design, optimized for discovery within governed institutional workflows.
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