Gotham
Mission cases, investigations, entity tracking, link analysis and operational timelines.
ClearGlassInc Artemis · New research project
Quantum-Neural Smart Glass
A target-state platform joining hybrid optical control, an ontology-driven digital twin and human-approved AI improvement—designed so untrusted models and neural signals can recommend, but never manufacture authority.
Research status: this is a target-state pilot specification, not a deployed product or clinical claim. Performance figures from the project brief are validation hypotheses until independently reproduced for the intended assembly and operating conditions.
System Architecture
Intelligence around the pane. Safety around the intelligence.
Foundry
Stream and batch integration, data quality, lineage, Ontology objects and governed application logic.
AIP
Typed agent tools, retrieval, copilots, model routing, prompt registry and offline evaluations.
Apollo
Signed releases, isolated rings, health gates, progressive canaries and known-good rollback.
Control plane
Python state machines validate every command, bind approvals to exact digests and keep hardware disconnected by default.
Audit plane
Append-only provenance covers evidence, model and prompt versions, policy decisions, approvals and outcomes.
AI and Agent Design
Propose fast. Decide deliberately.
01 · TriageValidate, deduplicate and classify.
02 · EnrichJoin authorized weather, BMS and maintenance context.
03 · CorrelateTraverse temporal, permission-filtered Ontology links.
04 · RecommendDraft bounded set-points with evidence and uncertainty.
05 · ApproveSeparate operator and safety reviewer bind the command digest.
Self-Improvement Loop
Learning without autonomous goal changes.
Evidence becomes evaluation
Corrections, rejected drafts, outcomes, latency and trust signals become de-identified, leakage-checked regression cases. Candidates are compared with a pinned baseline for precision, recall, calibration, latency, abstention and policy violations.
Promotion remains human-owned
Artemis proposes a versioned diff and rollback target. Model governance and the operational owner approve its immutable digest before an Apollo staging canary. Any policy violation fails closed and rolls back.
observe → curate → evaluate → propose diff → two-person review
→ isolated canary → monitor → approve promotion or rollback
Never: feedback → direct production prompt/model/policy mutation12-Month Pilot Roadmap
Evidence gates before expansion.
| Phase | Timeline | Required evidence | Exit artifact |
|---|---|---|---|
| Foundation | Aug–Sep 2026 | Primary-source verification, threat/privacy model, supplier diligence | Approved research requirements |
| Bench integration | Oct–Dec 2026 | Independent optical, thermal and electrical measurements | Isolated prototype + simulation |
| Governed control | Jan–Mar 2027 | Classical baseline, offline evals and failure tests | Draft-only controller |
| Pilot readiness | Apr–Jun 2027 | Safety, EMC, accessibility, privacy and rollback reviews | Disabled-by-default release |
| Observed pilot | Jul–Aug 2027 | Approved scope and pre-registered metrics | Go/no-go evidence package |
Implementation
Executable governance boundary.
The companion dependency-free Python reference models bounded commands, default-deny policy, separation of duties, digest-bound approvals, tamper-evident records and model promotion gates. It intentionally contains no hardware or Palantir adapter, keeping the project truthful and safe while its contracts are validated.