ClearGlass · Web Engineering Studio

We design websites as growth infrastructure.

A website should not just look premium — it should work like a business system. We architect, build, and maintain fast, secure, accessible sites engineered for clarity, performance, and long-term maintainability.

<1s
Target first-contentful paint on modern connections
95+
Lighthouse performance & accessibility budget
100%
Responsive, mobile-first across breakpoints
0
Build servers to babysit — static-first deploys
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 / linkCore propertiesOperational behavior
Entityentity_id, type, aliases, confidence, valid_time, system_timePerson, organization, asset, location or digital identity with merge/split lineage.
Observationsource_id, payload_hash, observed_at, classification, reliabilityImmutable evidence; corrections create superseding records rather than erasure.
Assessmentclaim, confidence, rationale, evidence_refs, analystSeparates evidence from interpretation and preserves dissenting assessments.
MissionContextobjective, ROE profile, coalition, compartments, time windowScopes retrieval, tools, model route, latency budget and approval authority.
Case / Alertstate, severity, owner, SLA, disposition, outcomeDrives human queues and generates labeled evaluation examples.
ActionPackageproposal, evidence, risk, reversibility, approval_stateState machine: draft → reviewed → approved/rejected → executed → verified.
RELATES_TOvalid_from/to, confidence, provenance, hypothesis_idTemporal 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.

03:17:00
Live event enters Foundry.A signed source adapter emits a schema-valid observation. Duplicate detection, classification and lineage checks pass; raw evidence is sealed.
03:17:02
Gotham resolves context.The Ontology links the observation to two assets and an open case. One relationship remains a hypothesis, visibly labeled at 0.64 confidence.
03:17:05
AIP triages and enriches.Specialists query only coalition-authorized sources, test competing hypotheses and produce a citation-complete summary. A low-confidence claim is excluded.
03:17:11
Recommendation is drafted.The commander copilot prepares alternatives, expected effects, risks, reversibility and evidence. Policy marks the package “human approval required.”
03:18:24
Operator revises and approves.The operator corrects an entity link, narrows the action scope and signs with a distinct approver. Only then does the execution adapter receive a single-use capability.
04:02:10
Outcome closes the loop.The alert disposition, correction and mission result become quarantined evaluation candidates. Privacy and quality review accepts them into the next offline replay set.
D+2
Artemis proposes—not deploys—an upgrade.A routing change improves precision and lowers p95 latency in replay. Reviewers approve a 5% canary; Apollo monitors policy, quality and trust thresholds before staged promotion.
D+3
Evidence decides.The canary meets every gate and is promoted with an immutable audit record. Any threshold breach would have automatically returned traffic to the signed last-known-good release.
Crafted like glass

Every surface is engineered to catch the light.

Our namesake is precision. The same discipline that etches a flawless emblem into crystal is the one we bring to a front-end: nothing decorative that isn't also structural, nothing shipped that hasn't been measured.

  • Media that respects the budget. The clip on the left is transcoded to WebM + H.264, muted, looped, and lazy-loaded — under 1 MB, no layout shift.
  • Motion with a fallback. A poster frame renders instantly; the loop pauses for prefers-reduced-motion; captions describe it for assistive tech.
  • Neon, not noise. The glow is pure CSS — conic gradients and blur, zero image weight, GPU-composited.
ClearGlassInc Artemis · Reference architecture

A self-evolving intelligence platform, governed by humans.

A production blueprint for secure, coalition-aware, latency-sensitive operations. Artemis fuses live and historical data, reasons over a permissioned ontology, and proposes evidence-backed improvements without ever manufacturing its own authority.

01 · Interface

Mission workbench

Analyst, commander, ModelOps, approvals, timelines, maps and evidence-linked briefs.

02 · Control

Zero-trust gateway

mTLS, workload identity, typed APIs, rate limits, purpose binding and OPA policy checks.

03 · Intelligence

AIP orchestration

Copilots, bounded agents, model routing, approved tools, retrieval and evaluation harnesses.

04 · Knowledge

Foundry + Gotham

Pipelines, ontology, lineage, investigations, entity resolution and operational cases.

05 · Runtime

Apollo delivery

Signed releases, enclave-aware configuration, canary rings, health gates and rollback.

Kafka / PulsarLakehouseHybrid searchOpenTelemetryImmutable auditPolicy as code

Data and Ontology

Mission, Event, Alert, Case, Entity, Device, Location, Evidence, Intel Product, Operator Feedback, Approval and Version objects carry confidence, source lineage, classification, coalition caveats, valid time and transaction time.

Evidence ─supports→ Event ─triggers→ Alert
Mission ─scopes→ Case ─contains→ Entity
Feedback ─corrects→ Decision ─proposes→ Upgrade

AI and Agent Design

Analyst and commander copilots coordinate bounded specialists for triage, enrichment, correlation, summarization and recommendation. Every output cites evidence; every tool receives the operator's filtered authorization context.

  • Read-only ontology search and evidence retrieval
  • Case and intel-product drafts through governed Foundry Actions
  • Operational action packages stop at explicit human approval

Self-Improvement Loop

Artemis learns from corrections, query traces, alert dispositions and mission results—but can only propose versioned changes to prompts, workflows, heuristics or routing. Deterministic policy, offline regression gates and accountable reviewers remain outside the model.

1 · CAPTURERedact and normalize feedback, outcomes and traces.
2 · EVALUATEBuild eval cases; score quality, safety, cost and latency.
3 · PROPOSECreate a signed, reviewable diff with evidence and rollback.
4 · APPROVEHuman owner accepts scope, risk and canary criteria.
5 · RELEASEApollo canary, drift watch, promote or auto-rollback.
View Python approval-gate skeleton
def promote(candidate: Upgrade, actor: Identity) -> Release:
    policy.require(actor, "upgrade.approve", candidate.mission_scope)
    assert candidate.evals.safety_regressions == 0
    assert candidate.evals.precision >= candidate.baseline.precision
    approval = approvals.consume(candidate.id, actor.id)
    audit.append("upgrade.approved", candidate.digest, approval.id)
    return apollo.canary(candidate.artifact, ring="shadow", rollback=True)

Security and Governance

Need-to-know ABAC enforces row, column, entity, relationship and Action-level access. Coalition markings propagate into retrieval, prompts, exports and audit. Short-lived identities, default-deny egress and tamper-evident logs protect every trust boundary.

Observability and Evals

OpenTelemetry joins request, retrieval, model and tool spans. Dashboards track precision, recall, citation validity, time-to-decision, p95 latency, operator override rate, trust, drift, policy denials and mission impact—without placing sensitive content in telemetry.

Scenario Walkthrough · signal to safe learning

Live eventA signed sensor event enters the stream and is hashed, classified and deduplicated.
Machine triageAgents correlate bitemporal entities and retrieve only policy-visible evidence.
RecommendationAIP drafts a cited response package with confidence and alternatives.
Human gateThe commander edits, approves or rejects; no significant action bypasses this step.
Safe learningThe outcome becomes an eval. A proposed prompt diff must beat baseline before canary release.
Why most websites underperform

Most sites are decorated, not engineered.

They look fine in a mockup and fail in production — slow on real devices, fragile to maintain, invisible to search, and inaccessible to a meaningful share of visitors.

SLOW
Heavy, unoptimized front-ends.Megabytes of unused script and render-blocking assets cost you ranking and conversions on every visit.
FRAGILE
No architecture, no maintainability.Page-builder sprawl that nobody can safely change six months later without breaking something else.
INVISIBLE
Weak semantic structure & SEO.Non-semantic markup and missing metadata leave search engines guessing what the page is about.
EXCLUDING
Inaccessible by default.Poor contrast, no keyboard support, missing labels — locking out users and inviting legal risk.
The build approach

Engineered for clarity, performance, and long-term maintainability.

We start from architecture, not aesthetics — then layer a precise, high-contrast interface on a foundation that stays fast, indexable, and easy to change.

Architecture first

A clear component model and content structure that maps to how your business actually works — so the site scales without rewrites.

information architecture

Performance budget

Every page ships against a measurable budget: minimal payload, lazy media, no render-blocking waste. Speed is a feature, not an afterthought.

core web vitals

Accessible by construction

Semantic HTML, keyboard paths, visible focus, and AA+ contrast built in from the first commit — not patched on at the end.

WCAG-aligned
Capabilities

A complete web engineering practice.

One partner across the full lifecycle — architecture, front-end, performance, search, security, and the automation that keeps it shipping.

Website architecture

Content models, routing, and reusable components designed for clarity and change — a system, not a stack of pages.

⟨⟩

Front-end development

Hand-built, standards-based front-ends with clean, reviewable code and no framework lock-in where it isn't earned.

Responsive design

Mobile-first layouts that hold their hierarchy and touch targets from phone to ultrawide.

Performance optimization

Asset budgets, caching strategy, image pipelines, and Core Web Vitals tuned to real-world devices.

🔎

SEO-ready structure

Semantic headings, metadata, sitemaps, and structured data so search engines understand and rank the page.

Accessibility

Keyboard-first interaction, ARIA where needed, contrast and motion preferences respected by default.

🛡

Security-minded implementation

Least-privilege automation, hardened headers, no secrets in the client, dependency hygiene, and a documented threat surface.

🚀

Automation-friendly deployment

CI-validated, reproducible static-first deploys with a clean rollback path — ship confidently, revert instantly.

Conversion-focused UX

Every interaction reduces friction and increases confidence — copy, hierarchy, and CTAs aligned to the decision the visitor is making.

Live proof, not promises

The same site is a running system.

This page reads its own operations feeds at load — CI, deploys, pipeline, and fleet telemetry — straight from the JSON we publish. If it's on the screen, it's real.

LOADING Fetching live operations signal…

Recent automation activity

  • Loading feed…

Growth pipeline

Open alerts

    Reading run history…

    Datasets wired: control-surface (health · metrics · pipeline · activity · alerts · runs) · store catalog · Ontario OSINT intel — fetched client-side, cached-safe, fail-quiet.

    Process

    Disciplined from discovery to optimization.

    A predictable engagement with clear deliverables at each stage — no black boxes, no surprises.

    01

    Discovery

    Goals, audience, constraints, and the metrics that define success.

    02

    Design

    Architecture, hierarchy, and a precise interface system mapped to your brand.

    03

    Build

    Semantic, performant, accessible front-end with reviewable, documented code.

    04

    Launch

    CI-validated deploy, security headers, analytics, and a tested rollback path.

    05

    Optimize

    Measure, tune Core Web Vitals, iterate on conversion, and maintain.

    Technical stack

    Modern, durable, and boring in the right places.

    We choose technology for longevity and performance, not novelty — standards-first, with build tooling only where it earns its keep.

    Semantic HTML5 Modern CSS · custom properties Progressive-enhancement JS Design tokens Static-first / JAMstack GitHub Actions CI/CD Lighthouse budgets Core Web Vitals Structured data · schema.org WCAG 2.x Hardened security headers Edge CDN delivery
    The difference

    A typical build vs. an engineered system.

    Where most projects cut corners that surface in production, we hold a measurable line.

    DimensionTypical buildClearGlass
    PerformanceHeavy page-builder output; slow on real devicesMeasured against a payload + Core Web Vitals budget, verified in CI
    MaintainabilitySprawl nobody can safely change laterClean component model and documented code you own outright
    AccessibilityPatched on at the end, if at allSemantic, keyboard-first, AA contrast — built in from commit one
    SEO structureNon-semantic markup, missing metadataSemantic headings, metadata, sitemaps, and structured data
    SecuritySecrets in the client; unmanaged surfaceNo client secrets, hardened posture, least-privilege automation
    Deploy & rollbackManual, fragile, hard to revertCI-validated, reproducible deploys with a tested rollback path
    Why ClearGlass

    An architecture partner — not a hobbyist freelancer.

    We build digital systems for organizations that treat their website as infrastructure: measured, documented, secure, and engineered to keep performing long after launch.

    • Measurable performance & accessibility budgets, verified in CI
    • Clean, reviewable, documented codebase you own outright
    • Security-minded by default — no client secrets, hardened headers
    • Reproducible deploys with a tested rollback path
    • Long-term maintainability over short-term shortcuts
    Questions, answered

    What serious clients actually ask.

    Direct answers — the kind a technical decision-maker needs before an engagement.

    Do I own the code?

    Yes — outright. You receive a clean, documented, framework-honest codebase in your own repository. No proprietary page-builder lock-in, no licensing leash, no dependency on us to make a change.

    How fast will it actually be?

    We build to a measurable budget — minimal payload, lazy media, no render-blocking waste — and verify Core Web Vitals in CI. The target is sub-second first-contentful paint on modern connections and a 95+ Lighthouse score for performance and accessibility.

    Will it hold up on mobile?

    Mobile-first is the starting point, not an afterthought. Layouts keep their hierarchy and touch-target sizing from phone to ultrawide, and every interaction is tested with keyboard and reduced-motion preferences.

    Is it secure?

    No secrets ship in client code. Automation runs least-privilege, dependencies are kept current, and the threat surface is documented. Where the host can't set HTTP security headers (e.g. static hosting), we say so and mitigate at the layers we control rather than pretend otherwise.

    Can you maintain it after launch?

    Yes. We offer ongoing optimization — Core Web Vitals tuning, conversion iteration, content and security updates — on a clear cadence. Because the code is documented and yours, you're never locked in to that arrangement.

    How is it deployed?

    CI-validated, reproducible, static-first deploys with a tested rollback path. A bad change is reverted in one step and the previous state redeploys automatically — no servers to babysit.

    Engagement

    Built for speed, trust, and conversion.

    Tell us what you're building. We'll respond with a clear architecture, a realistic plan, and a fixed view of what success looks like.