EDLORE

Enterprise Digital Learning & Operations Runtime Environment

AR Training • Real-World Maintenance • AI-Powered Knowledge Capture

v4.0 MVP COMPLETE

Prepared for: EDLORE Stakeholders
Date: March 2026
Classification: Confidential

Platform at a Glance

EDLORE 2.0 is a complete ground-up rebuild. It transforms a legacy system into a cutting-edge chat-first AI platform where the interface builds itself in real-time based on what the technician needs.

423

Source Files

68K+

Lines of Code

1,391

Automated Tests

14

Packages

Legacy vs. 2.0

Dimension	Legacy EDLORE	EDLORE 2.0
UX Paradigm	Click-through SaaS modules; feature suffocation	Chat-first: AI generates UI on the fly
AI	Basic RAG; 40K token window	Multi-agent orchestrator; 4 LLM providers; 23 tools
3D Pipeline	Manual; no automation	STEP to glTF automated; Babylon.js viewer; real-time SSE progress; job cancellation
AR	Concept only	Markerless AR engine (WebXR + equipment detection)
Work Orders	Built-in (redundant)	Integration-only (SAP; Maximo; ServiceNow)
Knowledge	Static docs	Self-improving learning engine: captures; evolves; predicts
Collaboration	None	LiveKit WebRTC: video; annotations; 3D sync
Offline	None	Electron + SQLite + sync engine with conflict resolution
Security	Basic	FedRAMP-certifiable: FIPS-ready; NIST 800-53; CMMC L2
Testing	Unknown	1,323 unit + 68 E2E tests; SAST; DAST; SBOM

UI: Chat-First Generative Interface

Surface Pro layout: chat panel (30%) + dynamic component canvas (70%). Components stream in real-time as the AI responds.

18 Generative UI Components

The AI does not show pre-built screens. It streams typed component invocations from a secure registry. Each component is independently tested and styled to the EDLORE design system.

ProcedureViewer

XState-driven step execution with safety check gates; progress bar; step sidebar

TroubleshootWizard

Branching diagnostic flow with history breadcrumbs; resolution capture to knowledge base

Model3D

Babylon.js viewer with part picking; correction mode; maturity score badge; device-adaptive LOD

SafetyChecklist

Mandatory acknowledgment steps with timestamps; regulatory references; compliance logging

SensorDashboard

Real-time telemetry grid; CSS sparklines; status pulse indicators (green/amber/red)

KnowledgeCard

Tacit knowledge display with confidence scoring; entry type badges; source attribution

ExpertCall

LiveKit WebRTC panel with video/audio; equipment context; annotation sync

PartsTable

Sortable data table with NSN; part numbers; availability status badges; search filtering

MaintenanceTimeline

Vertical timeline of equipment history; color-coded by event type; status pulse for active

AlertPanel

Severity-sorted alerts (critical/warning/info); pulsing indicators; acknowledge controls

AROverlay

Camera feed with mode selector (identify/annotate/measure); frosted glass panels; detection reticle

DocumentViewer

PDF/text/markdown rendering; amber search highlighting; annotation toolbar

WorkOrderPanel

Read-only integration view for SAP/Maximo/ServiceNow; source badges; priority coloring

MediaCapture

Photo/video capture; mode toggle; recording timer; quality badge; thumbnail strip

UploadProgress

Pipeline stage tracker with SSE real-time updates; scanner animation; cancel/retry controls; CANCELLED state

CorrectionTimeline

Chronological correction history per model; maturity score progression; contributor badges

CorrectionPreview

Live 3D diff preview with pulsing amber outline; confirm/cancel controls; undo stack

CorrectionFeedback

Inline approval/rejection widget for pending corrections; confidence scoring; audit trail

Design Identity

Aesthetic: "Precision Industrial Futurism." Swiss industrial design meets next-gen defense command center. Chamfered corners like machined metal edges; blueprint grid backgrounds; amber HUD accent lines; grain texture overlays for tactile depth.

Login: dark void background; blueprint grid; grain overlay; amber accents

Typography

Headings: JetBrains Mono (precision; technical authority)
Body: Satoshi (geometric warmth; legible on tablets)
Data: Berkeley Mono (sensor readings; part numbers)

Color System

Accessibility

WCAG 2.1 AA minimum. 44px touch targets (gloves). Color-blind safe: status always paired with icons. High-contrast mode for field use.

Motion

Precise; confident. Components fade-in with translateY over 200ms; staggered 50ms. Loading: amber scanner line. No bouncing; no elastic.

Interactive 3D Viewer

Real Babylon.js render of GE90 turbofan engine with 8-part hierarchy; amber highlight glow on selected component; maturity score badge; device-adaptive LOD; rotate/explode/measure/annotate/AR controls

System Architecture

EDLORE Client (React 19 + Vite | Electron for Surface Pro) ┌─────────────────────────────────────────────────────────────┐ │ Chat Panel (30%) │ Dynamic Component Canvas (70%) │ │ User types/speaks │ AI streams UI components in real-time │ ├─────────────────────┴───────────────────────────────────────┤ │ API Gateway (Fastify + tRPC + WebSocket) │ ├─────────────────────────────────────────────────────────────┤ │ Agent │ Learning │ Asset │ Collab │ Sensor │ │ Orchest. │ Engine │ Pipeline │ LiveKit │ Fusion │ ├─────────────────────────────────────────────────────────────┤ │ PostgreSQL │ TimescaleDB │ S3/MinIO │ Redis │ pgvector │ └─────────────────────────────────────────────────────────────┘

Self-Improving AI Core

EDLORE gets smarter with every interaction. The Learning Engine is not a feature: it is the foundation. Four continuous loops transform raw human activity into organizational intelligence. The Correction Flywheel ("Use = Improve") is now live — every field correction improves the 3D model for all future users.

The Learning Flywheel

1. Tacit Knowledge CaptureEvery procedure execution; deviation; sensor reading; and resolution is recorded. Photos; voice notes; timestamps: all structured and linked to equipment.

2. Knowledge GraphEquipment ↔ failure modes ↔ symptoms ↔ resolutions ↔ experts. Built automatically from execution data.

3. Procedure EvolutionWhen steps are consistently skipped or faster paths emerge: AI drafts improvement suggestions. Human-approved; full audit trail.

4. Predictive IntelligencePattern matching on sensor data predicts failures 2+ weeks early. Parts pre-staging before breakdown.

How It Gets Smarter Over Time

Timeline	EDLORE's Intelligence Level
Day 1	Follows documented procedures and shows 3D models. Scripted responses.
Month 3	Knows which steps techs actually follow vs. skip. Has resolution data from 50+ troubleshooting sessions.
Month 6	Suggests "Try checking the coupling first: that resolved this symptom 70% of the time on this model." Data-backed.
Year 1	Living knowledge graph. New techs get institutional knowledge of every tech before them. Predictive maintenance catches failures 2 weeks early.

LLM-Agnostic AI Gateway

EDLORE never locks into a single AI provider. The gateway routes to the best option for each deployment:

Provider	Model	Use Case	Status
Anthropic	Claude Sonnet 4.6	Commercial SaaS (best quality)	Ready
Ollama	Llama 3 / Mistral	Air-gapped / self-hosted	Ready
Azure OpenAI	GPT-4o	Azure Government (FedRAMP)	Ready
vLLM	Any open model	High-throughput on-prem	Ready

Predictive Maintenance Stack

Component	Method	Function
AnomalyDetector	Z-score (rolling window=100)	Flags readings >2σ warning; >3σ critical
TrendAnalyzer	Linear regression + autocorrelation	Detects increasing/decreasing trends; cyclic patterns
PatternMatcher	Multi-metric threshold + trend rules	Matches known failure signatures with confidence scores
Scheduler	Historical execution analysis	Calculates optimal maintenance intervals from actual data

Security & Compliance

Built for federal deployment from day one. Not bolted on later.

Control Area	Implementation	Status
Access Control	RBAC (5 roles); org-scoping; RLS; CAC/PIV placeholder	Implemented
Audit	Immutable AuditLog; all mutations logged; IP tracking	Implemented
Authentication	JWT + bcrypt (12 rounds); session rotation; token refresh	Implemented
System Protection	HSTS; CSP with nonces; rate limiting (4 categories); input sanitization	Implemented
Risk Assessment	SAST (Semgrep); DAST (ZAP); Trivy; Gitleaks; SBOM	Implemented
Code Review	Claude Code Review + Security Review on every PR	Implemented
FIPS 140-3 Crypto	Architecture ready; FIPS OpenSSL build needed	Planned
CMMC Level 2	60/103 practices implemented; 39 partial; 4 planned	In Progress

CI/CD Pipeline

Every commit is gated by 9 automated checks before it reaches production:

Lint → Type Check → 1,323 Unit Tests → SAST (Semgrep) → Secret Scan (Gitleaks) → Build → Container Scan (Trivy) → License Check → SBOM Generation + Claude Code Review + Claude Security Review on every Pull Request

Deployment Options

Environment	Infrastructure	AI Provider
Commercial SaaS	AWS ECS/EKS	Anthropic Claude
Government (IL4)	AWS GovCloud EKS	Azure OpenAI on Azure Gov
Air-Gapped (IL5+)	On-prem Kubernetes	Ollama / vLLM (self-hosted)
Field/Offline	Electron + SQLite	Local simulator (no network)

Pricing & Profitability

Pricing Model

Free

$0

forever

3 users
5 equipment models
10 procedures
Basic chat AI
No AR; no offline

Professional

$69

/seat/month

Unlimited equipment
Unlimited procedures
AR viewer
5 3D uploads/mo
100 AI queries/day

Enterprise

$99

/seat/month

Everything in Pro
Remote expert (WebRTC)
Offline mode
Sensor integration
SSO/SAML; audit logs

Government

Custom

contact sales

FedRAMP deployment
CAC authentication
Air-gapped option
On-prem AI
Dedicated infra; SBOM

Volume discounts: 50+ seats 15% | 200+ seats 25% | 500+ seats 35% + dedicated CSM. Usage-based add-ons for AI queries and 3D model processing above included limits.

Competitive Positioning

Competitor	$/user/month	Positioning
Microsoft D365 Remote Assist	$65	Price anchor; basic video assist
Augmentir	$30-100	AI-focused connected worker
SightCall	$40-120	Video assist; tiered
Librestream Onsight	$75-175	Defense/industrial; FedRAMP
TeamViewer Frontline	$80-200	Full platform; wearables
PTC Vuforia	$100-200+	Premium; IoT bundle
EDLORE Professional	$69	Below market; land play
EDLORE Enterprise	$99	Mid-market; expand play
EDLORE Government	$175-250	Premium for compliance

Three-Year Profitability Scenarios

Metric	Conservative	Moderate	Aggressive
Y1 Customers	5	8	15
Y1 ARR	$77K	$216K	$599K
Y2 ARR	$260K	$1.06M	$3.95M
Y3 ARR	$618K	$2.77M	$10.0M
Y3 Gross Margin	68%	74%	77%
Y3 Net Income	~Break-even	$451K	$2.2M
Break-even	Year 3-4	Mid Year 3	Late Year 2
Capital Needed	~$310K	~$1.1M	~$2.3M
Y3 Team Size	5	15	40

Conservative: bootstrap. Moderate: seed-funded ($500K-1M); FedRAMP in progress. Aggressive: Series A ($3-5M); FedRAMP authorized; major DOD win.

Build Phases

EDLORE 2.0 was engineered across 8 structured phases; each delivering a testable increment.

Phase 0: Foundation

Monorepo scaffold; design system; auth; CI/CD; database schema (18 models)

131 files • 16K LOC • 53 tests

Phase 1: Core Platform

Real Prisma queries; XState procedures; Babylon.js 3D viewer; 9 gen-ui components; chat streaming; login/register

+70 files • +10K LOC • +108 tests

Phase 2: AI System

4-provider AI gateway; multi-agent orchestrator; knowledge capture; execution tracking

+48 files • +5K LOC • +73 tests

Phase 3: AR + Sensors

Markerless AR engine; sensor fusion with MQTT; anomaly detection; all 18 gen-ui components

+35 files • +5K LOC • +50 tests

Phase 4: Collaboration

LiveKit WebRTC; annotation/model/cursor sync; offline sync engine

+38 files • +4K LOC • +92 tests

Phase 5: Offline Hardening

Electron sync bridge; encrypted SQLite; auto-sync on reconnect

Integrated with Phase 6-7

Phase 6-7: Predictive + Compliance

Trend analysis; pattern matching; maintenance scheduling; NIST 800-53; CMMC L2; hardened Dockerfiles; security middleware

+39 files • +5.5K LOC • +74 tests

Phase 8: MVP Sprint

3D correction mode (7 operations); pipeline SSE via Redis Streams; job cancellation; device-adaptive LOD; procedural GLB models; production deploy to edlore.ai; 5/5 smoke tests

+103 files • +7K LOC • +704 tests

Technology Stack

Frontend

React 19 • Vite • Tailwind CSS • Radix UI • Framer Motion • Babylon.js 7+ • Electron

Backend

Fastify • tRPC • Prisma • PostgreSQL 17 • Redis 7 • WebSocket • LiveKit

AI / ML

Claude SDK • Ollama • Azure OpenAI • vLLM • XState v5 • Z-score anomaly • Linear regression

DevOps / Security

Turborepo • GitHub Actions • Semgrep • Trivy • Gitleaks • Docker (distroless) • SBOM

EDLORE 2.0 is live.

423 files • 68K+ LOC • 1,391 tests • 14 packages • Production at edlore.ai • 5/5 smoke tests passing

Engineering Partner

Production Deployment Status

EDLORE 2.0 deployed to production on 2026-03-25. All endpoints verified, smoke tests passing, security headers confirmed.

Live Endpoints

Endpoint	Purpose	Result	Status
https://edlore.ai	SPA frontend (CloudFront + S3)	HTTP 200	PASS
/health	API health check (ALB → ECS)	{"status":"ok"}	PASS
/trpc/*	tRPC API (auth, equipment, pipeline)	UNAUTHORIZED JSON	PASS
/api/pipeline/jobs/*/events	SSE pipeline progress (Redis Streams)	{"error":"Unauthorized"}	PASS
/ws*	WebSocket (LiveKit, corrections)	502 (expected w/o upgrade)	PASS

SSL & Security Headers

Check	Expected	Actual
Certificate CN	edlore.ai	CN=edlore.ai (Amazon RSA 2048)
Valid through	—	Oct 6, 2026
HSTS	max-age=31536000	Present with includeSubDomains
CSP	Restrictive policy	default-src 'self'; frame-ancestors 'none'
X-Frame-Options	DENY	DENY
X-Content-Type-Options	nosniff	nosniff

Infrastructure

Compute

ECS Fargate (3 services: web, server, sensor-ingest) • ARM64 • Auto-scaling

CDN & Routing

CloudFront • 4 cache behaviors (/trpc/*, /api/*, /ws*, /health) • S3 SPA default • HTTP/2+3

Data

RDS PostgreSQL 17 • 28 models • 14 enums • Prisma ORM • pgvector

Cache & Streams

ElastiCache Valkey 7.2 • Redis Streams for SSE • BullMQ job queue • Cancel signals

Smoke Tests — 5/5 Passing

Test	Duration	Status
Login page loads with EDLORE branding	686ms	PASS
Serves over HTTPS with security headers	670ms	PASS
Register new account and see app interior	3.8s	PASS
Register page is accessible	1.5s	PASS
API health endpoint responds with JSON	229ms	PASS