Website Design & Development — Growth Infrastructure

ClearGlassInc Artemis

Reference architecture · governed by design

Self-evolving intelligence platform / blueprint 01

Intelligence that learns under command.

A secure, coalition-aware full-stack design that fuses live and historical data, reasons through bounded agents, and proposes measurable upgrades without manufacturing its own authority.

Target-state specification. This blueprint describes an implementable architecture—not evidence of a deployed Palantir environment. Every operationally significant action remains draft-only until an authorized human approves it.

GOTHAMOperational intelligence, investigations, entity tracking and mission context.

FOUNDRYData integration, lineage-aware pipelines, Ontology and application logic.

AIPCopilots, bounded agents, model routing, evaluation and workflow automation.

APOLLOPolicy-controlled delivery, environment promotion, health checks and rollback.

01 / System

Seven planes. One governed decision path.

The architecture isolates presentation, intelligence, execution, policy and audit concerns so a partial failure cannot silently become an unauthorized state.

Experience plane

React/TypeScript analyst workspace, commander cockpit, case graph, timeline, geospatial layers, provenance drawer and an approval inbox. Optimistic UI is prohibited for consequential actions.

API & orchestration

Python FastAPI gateway, typed mission APIs, Temporal-style durable workflows and idempotent event handlers. Every call carries actor, tenant, compartment, purpose and correlation context.

Data & Ontology

Foundry pipelines normalize streams into versioned datasets; the Ontology exposes governed objects, links, actions and functions as the shared operational contract.

Intelligence plane

AIP copilots and agents retrieve only policy-filtered evidence. A deterministic router selects models by classification, latency, quality and cost bounds.

Policy & audit

Default-deny ABAC/ReBAC decisions sit beside protected actions. Append-only audit events record input hashes, evidence, model/prompt versions, tool calls and approvals.

Runtime & delivery

Apollo promotes signed, evaluated releases across disconnected or cloud environments with canaries, health gates, immutable identity and last-known-good rollback.

02 / Data

Ontology as the operational contract.

Objects are temporal, lineage-bearing and permission-scoped. Agents receive the same constrained object view as the operator, preventing retrieval from becoming an authorization bypass.

Object / link	Core properties	Operational behavior
`Entity`	entity_id, type, aliases, confidence, valid_time, system_time	Person, organization, asset, location or digital identity with merge/split lineage.
`Observation`	source_id, payload_hash, observed_at, classification, reliability	Immutable evidence; corrections create superseding records rather than erasure.
`Assessment`	claim, confidence, rationale, evidence_refs, analyst	Separates evidence from interpretation and preserves dissenting assessments.
`MissionContext`	objective, ROE profile, coalition, compartments, time window	Scopes retrieval, tools, model route, latency budget and approval authority.
`Case / Alert`	state, severity, owner, SLA, disposition, outcome	Drives human queues and generates labeled evaluation examples.
`ActionPackage`	proposal, evidence, risk, reversibility, approval_state	State machine: draft → reviewed → approved/rejected → executed → verified.
`RELATES_TO`	valid_from/to, confidence, provenance, hypothesis_id	Temporal graph edge; asserted, inferred and disputed links remain distinguishable.

Ingest contract

Schema registry rejects ambiguous, oversized or duplicate events.
Watermarks and idempotency keys support replay.
Raw evidence remains immutable; normalized views are reproducible.
Every transformation emits dataset, code and source lineage.

Permission contract

Tenant + coalition + compartment + purpose-of-use checks.
Row, column, object, link and action-level controls.
Field redaction occurs before search indexing and model context.
Derived objects inherit the strictest contributing classification.

03 / AI

Bounded agents, typed tools, explicit authority.

Models propose; deterministic services authorize. Every tool is schema-bound, allowlisted, time-limited and independently audited.

Triage signal and set urgency

Enrich from authorized sources

Correlate entities and hypotheses

Summarize with citations

Draft recommendation package

Human approve, reject or revise

Analyst copilot

Builds provenance-linked timelines, compares hypotheses, identifies evidence gaps and drafts intelligence products. It abstains when source coverage or confidence is below mission thresholds.

Commander copilot

Surfaces decision points, risk, alternatives and confidence without hiding dissent. It can prepare—but never self-approve—an action package.

Specialist swarm

Triage, enrichment, graph correlation, geospatial, summarization and red-team agents exchange typed artifacts, not free-form authority. A governor enforces budgets and terminal states.

04 / Loop

Gets better through evidence—not autonomy.

Operator corrections and outcomes become governed evaluation material. The platform may propose changes to prompts, routing, heuristics or workflows; only an authorized release owner can promote them.

Capture feedback and outcomes

Curate privacy-safe eval cases

Generate bounded proposal

Offline replay + red-team

Human approval + canary

Monitor, promote or rollback

Promotion gates

Required: no policy regression, provenance coverage at target, precision/recall non-inferiority, bounded p95 latency/cost, adversarial suite pass, two-person approval for high-risk routes, signed release and rollback pointer.

Drift & trust

Monitor input distribution, label shift, calibration, citation validity, override/rejection rate, operator trust, latency and mission outcome proxies. Threshold breach freezes promotion and returns traffic to the last-known-good version.

05 / Stack

Full-stack implementation blueprint.

Each tier has a narrow responsibility, an observable SLO and a fail-closed degradation mode.

Web UI

React + TypeScript, graph/map/timeline canvases, WebSocket subscriptions, accessible command palette, evidence-first answer cards and mandatory confirmation ceremonies.

Gateway

FastAPI + Pydantic contracts, workload identity, request budgets, idempotency, rate limits and a policy decision point. SSE/WebSockets provide bounded live updates.

Services

Ingest, identity resolution, case, alert, action package, feedback, evaluation, model registry and audit services. Durable workflows isolate retries from business decisions.

Streaming & storage

Kafka-compatible partitioned event log; Foundry datasets/lakehouse for canonical history; transactional store for workflow state; object storage for encrypted evidence.

Retrieval & inference

Permission-aware lexical/vector/graph retrieval with source snapshots. Model router selects only approved endpoints and enforces context, output and latency budgets.

Operations

OpenTelemetry traces, structured privacy-aware logs, eval dashboards, queue depth and policy-denial alerts. Apollo manages progressive delivery, attestation and rollback.

06 / Security

Zero trust from source to action.

Classification, coalition boundary and purpose-of-use policy follow data through ingestion, retrieval, generation, export and audit.

Identity & need-to-know

Phishing-resistant MFA, short-lived workload identities, continuous device posture and default-deny ABAC/ReBAC. Break-glass access is time-bound, witnessed and reviewed.

Model & prompt governance

Immutable registry versions, signed evaluation evidence, approved data-use scopes, prompt-injection isolation and output validation. Models cannot alter policy or tool grants.

Audit & recovery

Hash-chained append-only decisions, independently queryable audit plane, correlation IDs, key rotation and tested restoration. Denied actions produce no partial operational side effect.

Forbidden invariant: no agent can change its mission, expand tools, promote itself, cross a compartment, execute a consequential action or delete material audit history.

07 / Code

Python-first control boundaries.

Representative skeletons show the critical invariants. Palantir SDK calls are intentionally represented behind typed ports until the target environment and generated Ontology SDK are verified.

policy.py · fail-closed action transitionPython 3.12

class ActionState(StrEnum):
    DRAFT = "draft"; REVIEWED = "reviewed"; APPROVED = "approved"
    EXECUTED = "executed"; VERIFIED = "verified"

ALLOWED = {DRAFT: {REVIEWED}, REVIEWED: {APPROVED}, APPROVED: {EXECUTED}, EXECUTED: {VERIFIED}}

async def transition(package, target, actor, policy, audit):
    if target not in ALLOWED.get(package.state, set()):
        raise InvalidTransition(package.state, target)
    decision = await policy.authorize(actor=actor, action=f"package:{target}", resource=package)
    if not decision.allowed:
        await audit.append("action.denied", package.id, actor.id, decision.reason)
        raise PermissionDenied()
    if target is ActionState.APPROVED and not package.has_distinct_human_approver:
        raise HumanApprovalRequired()
    return await package.advance_atomically(target, decision.id)

evaluation.py · promotion gatePython 3.12

@dataclass(frozen=True)
class EvalGate:
    min_precision: float = .92
    min_recall: float = .86
    min_provenance: float = .98
    max_p95_ms: int = 1800

def promotion_decision(candidate: Metrics, baseline: Metrics, gate: EvalGate) -> Decision:
    checks = {
        "policy_regressions": candidate.policy_regressions == 0,
        "quality": candidate.precision >= gate.min_precision and candidate.recall >= gate.min_recall,
        "provenance": candidate.provenance_coverage >= gate.min_provenance,
        "latency": candidate.p95_ms <= gate.max_p95_ms,
        "non_inferior": candidate.mission_score >= baseline.mission_score,
    }
    return Decision(proposable=all(checks.values()), checks=checks, auto_promote=False)

ontology_port.py · policy-filtered retrievalPython 3.12

async def evidence_for_case(case_id: UUID, principal: Principal) -> list[Evidence]:
    scope = await policy.read_scope(principal, object_type="Observation", purpose="mission-analysis")
    if scope.is_empty: raise PermissionDenied()
    rows = await ontology.observations.search(
        case_id=case_id, compartments=scope.compartments, valid_at=clock.now(), limit=200
    )
    return [Evidence.from_object(row).redact(scope.fields) for row in rows]

08 / Scenario

03:17 UTC — a weak signal becomes a governed decision.

A technically credible path from event to outcome demonstrates where machines accelerate work and where human authority remains absolute.