# ClearGlassInc Artemis — Quantum-Neural Smart Glass

> **Status: target-state research and pilot specification (August 2026).** This
> document does not claim that a unified SPD/PDLC/BCI/quantum product, a Palantir
> deployment, regulatory clearance, supply agreement, pilot contract, or measured
> building-energy result exists. Vendor figures and roadmap thresholds below are
> validation targets supplied in the project brief; procurement must verify them
> against current primary documentation before they become acceptance criteria.

## Visionary Company Manifesto (300 words)

ClearGlassInc Artemis begins with a disciplined belief: architecture should respond to people without taking authority from them. We envision transparent surfaces that manage light, privacy, heat, and information while preserving human choice, safety, dignity, and control.

Our Quantum-Neural Smart Glass project is not a promise disguised as a product. It is a research mission built around evidence. We will test hybrid optical layers, digital twins, optimization methods, accessible interfaces, and governed neural input as separate capabilities before claiming they belong together. Every performance target must survive independent measurement. Every model must beat a credible baseline. Every deployment must be reversible.

The pane will sense, but collect only what is necessary. Intelligence will recommend, but never approve itself. Operators will see sources, confidence, alternatives, and consequences before consequential action. Neural signals will remain sensitive human data, never a shortcut around consent. Clinical, privacy, safety, electromagnetic, accessibility, and security review will define the boundary of progress, not follow it.

ClearGlassInc Artemis will learn from corrections and outcomes through controlled evaluation, versioned proposals, human review, bounded canaries, and known-good rollback. It will not rewrite its mission, widen its permissions, add its own tools, or convert feedback directly into production behavior. Improvement without governance is drift; speed without evidence is theater.

We are building a membrane between people and place that is calm, legible, efficient, and inclusive. In enterprise spaces, it should reduce distraction and wasted energy. In care environments, it should protect privacy and restore agency. At home, it should make comfort accessible without surveillance or complexity.

Our standard is not novelty. It is trustworthy capability: measured, permissioned, auditable, resilient, and useful. The glass remains transparent. So must every decision behind it. We will publish failures, challenge assumptions, protect operators, and earn deployment one validated gate at a time, together, with care, always.

## Technical Validation Matrix

| Capability | Target-state design | Project-brief hypothesis to verify | Pilot decision gate |
|---|---|---|---|
| Multi-zone optical control | Independently bounded SPD and PDLC zones | sub-second SPD response, millisecond PDLC response, optical and lifetime targets | accredited bench results for the final laminate |
| Light and thermal optimization | Classical baseline with optional quantum research adapter | quantum method improves the multi-objective frontier | repeatable quality, cost and latency advantage over baseline |
| Accessible intent input | explicit commands through approved assistive adapters | BCI-class intent can safely map to a small command vocabulary | consent, clinical, privacy and human-factors approval |
| Sustainability twin | measured manufacturing and operating telemetry with uncertainty | hybrid twin predicts energy and carbon outcomes | independently calibrated error bounds and lifecycle assessment |
| Unified assembly | isolated low-voltage control and replaceable compute/antenna modules | layers coexist without unacceptable optical, thermal or EMC effects | safety, durability, repairability and EMC qualification |

## System Architecture

ClearGlassInc Artemis is the governed intelligence and control plane around a
future hybrid smart-glass assembly. The trusted path is deliberately split:

```text
pane telemetry / BMS / weather / operator feedback / approved BCI adapter
     │
     ▼
Foundry ingestion → quarantine → schema/quality policy → governed datasets
     │                    │
     │                    └── lineage + classification + retention
     ▼
Foundry Ontology ↔ Gotham investigations, entity graph, cases and timelines
     │
     ▼
AIP triage → enrichment → optimization proposal → evidence-grounded action draft
     │
     ▼
Policy decision point → two-person approval → signed, digest-bound command
     │
     ▼
Hardware adapter (future) → SPD/PDLC zone controller → outcome telemetry

Apollo: signed release rings, environment policy, canary, health gate and rollback
Audit plane: append-only decision, approval, provenance and execution records
```

The browser application shows pane state, thermal/daylight overlays, evidence,
case context and approval queues. It never acts as an authorization boundary.
Python mission services own command validation, workflow transitions and audit.
Gotham supports operational investigation and entity tracking; Foundry supplies
data integration, Ontology and application logic; AIP hosts governed copilots,
agents and evaluations; Apollo controls deployment and rollback. These are
target-state integration roles, not evidence that Palantir services are provisioned.

## Data and Ontology

Every object carries `object_id`, `valid_from`, `valid_to`, `observed_at`,
`confidence`, `classification`, `compartments`, `source_ids`, `lineage_digest`,
`mission_id`, and `policy_tags`.

| Object | Core fields | Governed relationships |
|---|---|---|
| `Pane` / `Zone` | location, controller, rated bounds, lifecycle state | `HAS_ZONE`, `INSTALLED_AT`, `GOVERNED_BY` |
| `Observation` | sensor type, value, unit, quality, calibration | `OBSERVES`, `DERIVED_FROM` |
| `OpticalState` | tint, privacy, transmission, set-point source | `STATE_OF`, `SUPERSEDES` |
| `Mission` / `Case` | objective, owner, allowed compartments, status | `SCOPES`, `CONTAINS` |
| `Evidence` | source, digest, capture time, admissibility | `SUPPORTS`, `CONTRADICTS` |
| `ActionDraft` | target, parameters, risk, command digest | `PROPOSED_FOR`, `CITES` |
| `Approval` | reviewer, role, decision, rationale, expiry | `BINDS_TO` exact digest |
| `AgentRun` | model, prompt, tools, inputs, outputs, cost | `GENERATED`, `USED` |
| `FeedbackSignal` | correction, disposition, outcome, trust score | `EVALUATES` |
| `ImprovementCandidate` | diff, eval set, metrics, rollback version | `PROPOSES_CHANGE_TO` |

Ontology Actions expose typed verbs such as `DraftOpticalState`, `OpenCase` and
`PrepareActionPackage`. `ExecuteOpticalState` remains a separate server-side
action requiring a live authorization decision and digest-bound approval. Entity
merges and cross-compartment relationships are drafts until an authorized human
confirms them; retrieval intersects user, mission, object and source permissions.

## AI and Agent Design

- **Analyst copilot:** retrieves authorized evidence, explains pane anomalies,
  drafts cases and cites every material assertion.
- **Commander/facility copilot:** compares courses of action, uncertainty and
  rollback; it cannot approve its own recommendation.
- **Triage agent:** validates events, deduplicates and assigns confidence.
- **Enrichment agent:** joins weather, BMS, calibration and maintenance context.
- **Correlation agent:** searches temporally valid Ontology relationships.
- **Optimizer agent:** proposes bounded optical set-points; a deterministic
  validator enforces rated limits regardless of model output.
- **Product agent:** prepares an evidence-backed action package and operator brief.
- **Policy agent:** may explain policy, but deterministic policy-as-code makes the
  authorization decision.

Agents receive typed, allowlisted tools; per-run time, token, query and tool-call
budgets; source-level permissions; and an output schema. Retrieved text and BCI
signals are untrusted input. Tool output is provenance-linked. No agent can add a
tool, broaden a compartment, change a goal, deploy itself, or execute a physical,
clinical, security-relevant or production action.

## Self-Improvement Loop

1. **Capture:** append operator corrections, approval/rejection rationale, query
   traces, alert disposition, latency, model/tool versions and mission outcomes.
2. **Curate:** de-identify where required, bind provenance, remove leakage and
   create time-split positive, negative, boundary and adversarial eval cases.
3. **Evaluate:** compare the pinned baseline with a candidate for precision,
   recall, calibration, latency, abstention, policy violations and operator trust.
4. **Propose:** create a reviewable prompt/workflow/router/heuristic diff with
   rationale, affected missions, evidence, risk, migration and rollback version.
5. **Approve:** model governance and the operational owner independently approve
   the immutable candidate digest. Mission goals and permissions cannot be edited.
6. **Release:** Apollo promotes only to an isolated offline/staging ring, then a
   bounded canary. A policy violation, drift alarm or latency breach rolls back.
7. **Observe:** compare canary and control, preserve the result and either promote
   through another approval or pin the known-good version.

No online learning writes directly to production weights, prompts, routing or
policy. A/B tests never split safety rules and never expose an operator to an
unapproved operational behavior. Drift monitors track input distribution,
confidence calibration, retrieval quality, outcome precision and cohort gaps.

## Full-Stack Implementation

```text
apps/web (TypeScript): map/facade view, case workspace, evidence, approval inbox
gateway: workload identity, request limits, schema validation, correlation IDs
services/control (Python): state machine, command digests, policy enforcement
services/evals (Python): immutable datasets, paired scoring, drift and promotion
stream: partition by pane/mission; schema registry; bounded replay and dead letter
Foundry: batch/stream transforms, governed datasets, Ontology and Object Actions
Gotham: investigations, entity graph, alert/case collaboration
AIP: Logic/workflows, model routing, typed tools, prompt registry and evaluations
Apollo: signed artifacts, ring promotion, environment policy and one-click rollback
search: compartment-filtered keyword/vector retrieval with source-level ACLs
telemetry: OpenTelemetry traces, SLOs, eval dashboards and tamper-evident audit
```

The executable, dependency-free reference is in
`quantum_neural_glass/control_plane.py`. It models commands, policy decisions,
separation of duties, digest-bound approvals, an append-only hash chain and
promotion gates without pretending to connect to hardware or Palantir APIs.

## Security and Governance

- Workload and operator identity are authenticated at every trust boundary with
  short-lived, audience-bound credentials; authorization defaults to deny.
- Classification and compartments are applied at dataset, row, column, object,
  relationship, search-result, tool and action levels. Coalition release rules
  prevent inference across source compartments.
- Control, data, management and audit planes use separate identities and network
  paths. Browser controls are never treated as security controls.
- Commands bind target, parameters, evidence, expiry and policy version into the
  approved digest. The requester cannot be the safety approver.
- Raw neural data is optional, highly sensitive and minimized; consent, purpose,
  retention, revocation and clinical governance must be established before use.
- Prompt, model, tool and policy versions are recorded with every run. Logs omit
  secrets and unnecessary neural/clinical content and are independently readable.
- Production remains disabled until architecture, privacy, safety, clinical,
  electromagnetic, threat, supply-chain and operational review gates pass.

## Code Examples

```python
from quantum_neural_glass import ActionKind, GlassCommand, GlassControlPlane

plane = GlassControlPlane()
draft = GlassCommand(
    command_id="cmd-203", pane_id="south-17", zone_id="z3",
    action=ActionKind.PROPOSE_OPTICAL_STATE, tint_percent=62,
    source="optimizer", evidence_ids=("weather-91", "bms-771"),
)
# submit() can only return a draft for this action; it cannot actuate hardware.
decision = plane.submit(draft, operator_context)
```

```sql
SELECT o.object_id, o.observed_at, o.value, o.unit, o.confidence
FROM authorized_observation o
WHERE o.mission_id = :mission_id
  AND o.pane_id = :pane_id
  AND o.observed_at >= :window_start
  AND o.compartment = ANY(:authorized_compartments)
ORDER BY o.observed_at DESC
LIMIT :bounded_limit;
```

```python
def promote(candidate, approvals, apollo):
    gate = control_plane.evaluate_improvement(candidate)
    assert gate.reason == "apollo_canary_only"
    assert approvals.bind_exact_digest(candidate)
    release = apollo.deploy(candidate, ring="staging-canary", percent=5)
    return release.observe_or_rollback(
        rollback_on=("policy_violation", "drift", "p95_latency_breach")
    )
```

## Scenario Walkthrough

At 14:07 a south-facade heat-load event arrives with a signed device identity.
Foundry quarantines it, verifies schema and calibration, assigns lineage, and emits
an authorized Ontology `Observation`. Gotham attaches it to the Toronto pilot
case. Triage removes a duplicate; enrichment adds weather and BMS history;
correlation finds a similar verified episode. The optimizer drafts 62% tint with
two evidence IDs and uncertainty. The policy service checks the pane rating,
mission, compartment, operator role and command digest.

The facility operator sees the evidence and expected effect. A separate safety
officer rejects the first draft because maintenance has temporarily isolated one
zone, then approves a bounded replacement. Only the exact approved digest reaches
the future adapter. The outcome stream records optical response and room load.

That rejection becomes a feedback signal—not a production edit. Offline curation
adds a regression case for maintenance isolation. A candidate workflow adds the
missing check and beats the pinned baseline with zero policy violations. Model
governance and the operational owner approve its digest. Apollo deploys a 5%
staging canary; drift, latency and policy monitors remain within bounds. A later
human decision may promote it. The original release remains the rollback target.

## 12-Month Pilot Roadmap

| Phase | Timeline | Evidence gate | Exit artifact |
|---|---|---|---|
| Foundation | Aug–Sep 2026 | Primary-source capability and supplier verification | requirements, threat/privacy model, supply diligence |
| Bench integration | Oct–Dec 2026 | Independently measured optical, electrical and thermal tests | isolated prototype and calibrated simulation |
| Governed control | Jan–Mar 2027 | Classical baseline, offline evals, human factors and failure tests | draft-only multi-zone controller |
| Pilot readiness | Apr–Jun 2027 | Safety, EMC, accessibility, clinical/privacy and rollback reviews | disabled-by-default pilot release |
| Observed pilot | Jul–Aug 2027 | Approved sites, bounded scope and pre-registered metrics | monitored dataset and go/no-go review |

Energy, optical, latency, lifetime and BCI performance figures from the original
brief are hypotheses until reproduced under the intended assembly and operating
conditions. Quantum optimization must beat a credible classical baseline on cost,
quality and latency before it enters even a draft-only operational path.