Fibre OS

Three layers. Three modes. One integrated system.
Invisible specialization, with visible intelligence.
v1.1 — February 16, 2026
System Architecture
Three Layers
The user talks to Fibre. Fibre handles it. They never see the layers — they just see correct answers and intelligent actions.
Layer 1 Channel Layer How things get in and out
Email
Receive, classify, draft, send. Learn voice and style.
Chat
Conversational interface. User's primary interaction.
Notifications
Agent communications. Decisions requiring input.
Tasks
Smart prioritization. Follow-ups. Scheduling.
Documents
Contracts, invoices, receipts. Intake and generation.
Integrations
Bank feeds, payment processors, external APIs.
events flow down → intelligence processes → routes to domain
Layer 2 Intelligence Layer The brain — sees everything, remembers everything, learns
Event Stream
Immutable record of every state change. Source of truth.
Entity Graph
Vendors, clients, contacts. Relationships and learned facts.
Rule Engine
Learned behaviors. Event → condition → action. No LLM cost.
Pattern Detection
Connects dots across domains. Anomalies and trends.
Goal Orchestration
Cross-domain goal tracking. Injected as context to all domains.
Prior Decisions
Memory of every user decision. Never asks the same question twice.
Context Composer
Assembles focused context per situation. Right info, right time.
Domain Router
Classifies events. Routes to the right specialist. Invisible.
Tool Factory
Self-building. Agent proposes tools. User approves. System grows.
Autonomous Agent
Wakes when rules say so. Investigates. Acts or asks. Learns.
This is the moat. The intelligence layer accumulates knowledge unique to each business. Rules, learned facts, entity relationships, patterns, prior decisions. Every interaction makes it smarter. Every week the system handles more autonomously. The switching cost is the accumulated intelligence, not the features.
routed to specialist with focused context + tools
Layer 3 Domain Layer Specialized reasoning — called by the brain when expertise is needed
Money
Financial Intelligence
Accounting, cash management, AP/AR, invoicing, budgets, cash flow projection, P&L, balance sheet, asset management, depreciation, tax optimization, margin analysis, financial anomaly detection.
AVAILABLE: BUSINESS TIER
Revenue
Commercial Intelligence
Leads, pipeline, proposals, deals, client communication, follow-ups, pricing strategy, win/loss analysis, churn detection, upsell identification, relationship management.
AVAILABLE: BUSINESS TIER
Legal / Admin
Compliance Intelligence
Contracts, agreements, renewals, regulatory filings, business entity maintenance, state licensures, corporate documents, insurance tracking, compliance deadlines.
AVAILABLE: BUSINESS TIER
Supply
Logistics Intelligence
Inventory, vendor management, procurement, receiving, shipping, fulfillment, reorder logic, lead time tracking, vendor performance scoring, warehouse management.
AVAILABLE: PRO TIER
Production
Manufacturing Intelligence
Work orders, production scheduling, yield tracking, quality control, machine utilization, maintenance scheduling, BOM management, capacity planning.
AVAILABLE: PRO TIER
R&D
Product Intelligence
Product development, DFM analysis, prototyping cycles, material selection, cost estimation for new products, regulatory compliance, concept-to-production pipeline.
AVAILABLE: PRO TIER
Operating Modes
Three Modes
How the intelligence layer operates. Each mode has different cost characteristics and value delivery.
Mode 1
Passive
Always running. Code only. No LLM calls. Processes every event, evaluates rules, tracks balances, monitors deadlines. The backbone.
Trigger: Every event, automatically
What runs: Rule engine, balance tracker, projection updates, pattern detection, event classification, entity resolution, deadline monitoring
Value: Real-time state of the business, always current, never forgets
Mode 2
Reactive
Wakes when triggered. A rule fires, the user asks, or something unusual happens. Investigates, reasons, acts or asks. Learns.
Trigger: Rule match, user message, anomaly
What runs: Autonomous agent with domain-specialized prompt and tools. Context composed per situation. Learning tools (entity facts, rules, observations).
Value: Expert-level decisions on demand. Gets smarter with every interaction.
Mode 3
Proactive
Works unprompted. Scheduled sweeps. Projects forward. Identifies problems before they happen. Pursues goals. The system working while you sleep.
Trigger: Schedule (daily/weekly sweeps), goal deadlines
What runs: Goal-driven agent reasoning across domains. Solves problems end-to-end. Executes multi-step actions autonomously.
Value: Predictive operations. No human employee does this consistently.
The system gets quieter as it gets smarter. In week one, Fibre asks you questions. By month two, it handles routine decisions automatically — the same way you would. By month six, it's catching problems you wouldn't have seen until they cost you money. The longer it runs, the less you have to do and the more it does right.
Pricing
Three Tiers
Each tier unlocks more of the system. The intelligence layer learns at every tier — accumulated knowledge compounds and increases switching cost.
Fibre Core
Everyone. The wedge product.
  • ● Intelligent email (learns voice, drafts, knows context)
  • ● Smart task management and prioritization
  • ● Contact and relationship graph
  • ● Basic automation rules
  • ● Intelligence layer learning (always on)
  • ○ No domain specialization
  • ○ No proactive operations
    • Passive Reactive
    Fibre Business
    Any business. Full operational intelligence.
  • ● Everything in Core
  • ● Money domain (financial intelligence)
  • ● Revenue domain (commercial intelligence)
  • ● Legal/Admin domain (compliance intelligence)
  • ● Goal tracking and cross-domain orchestration
  • ● Tool Factory (system builds its own tools)
  • ○ No proactive operations
    • Passive Reactive
    Fibre Pro
    Growth businesses. Predictive operations.
  • ● Everything in Business
  • ● Proactive mode (scheduled sweeps, projections)
  • ● Supply domain (logistics intelligence)
  • ● Production domain (manufacturing intelligence)
  • ● R&D domain (product intelligence)
  • ● Predictive alerts and opportunity identification
  • ● Goal-driven autonomous operations
    • Passive Reactive Proactive
    How It Works
    Fibre in Action
    Every interaction follows the same path: channel receives → intelligence processes → domain reasons → actions execute. The user sees one Fibre. These are real operational scenarios.
    Equipment purchase — automatic asset classification
    User
    "Record $7K expense from Atlas Fabrication for a lathe"
    Channel
    Chat processes input. Expense recorded. Event emitted.
    Intelligence
    Rule fires: equipment purchase over $1K. Checks prior decisions — user previously approved 7-year depreciation for equipment. No need to ask again.
    Money
    Reclassifies from COGS to capital asset. Creates equipment record. Sets up straight-line depreciation — $1,000/year for 7 years. Adjusts balance sheet.
    Learning
    Learns Atlas Fabrication is an equipment vendor. Strengthens rule confidence. Next time: fully automatic, zero interaction.
    Result: What took a bookkeeper 30 minutes and a CPA consultation happened in 2 seconds. Correctly. With an audit trail.
    Vendor invoice arrives — cross-domain processing
    Email
    Invoice from PolyMold Inc. lands in inbox. PDF attached. $12,400 for injection mold tooling.
    Intelligence
    Entity match: PolyMold — known vendor, Net 30 terms, 97% on-time delivery rating. Cross-references against open POs. Finds match: PO-2847 for mold tooling.
    Money
    Records AP. Matches to PO-2847. Schedules payment for March 15 (Net 30). Checks cash projection — sufficient funds, no conflict.
    Supply
    Updates receiving status. Tooling marked as in-transit. Links to R&D project: new product SKU awaiting this tooling to begin pilot runs.
    Notification
    "PolyMold invoice matched to PO-2847. Payment scheduled Mar 15. Tooling arrival unblocks pilot run for SKU-1847."
    Result: One email triggered 4 updates across 3 domains. The connection between an invoice and a product launch timeline — that's the kind of dot nobody connects manually.
    Proactive sweep — prevents a cash crisis before it happens
    Scheduled Sweep
    Sunday 6am. Weekly financial health check triggers automatically.
    Money
    Projects cash 8 weeks forward. Finds a $34K gap in week 6 — three large vendor payments converge the same week a quarterly tax payment hits.
    Intelligence
    Cross-references AR aging. Two clients are 45+ days overdue totaling $41K. Checks entity history — Client A has been late 3 of last 5 invoices. Client B is usually on time, likely an oversight.
    Action
    Drafts two different collection emails — firm but professional for Client A (includes payment history), gentle reminder for Client B. Drafts request to PolyMold to shift one payment 15 days (they've accommodated before). Queues all three for user approval.
    Goal Check
    Active goal: "Maintain 60-day cash runway." Current: 47 days. If AR collected and payment shifted, runway returns to 71 days.
    Notification
    "Cash gap of $34K in 6 weeks. I've drafted AR collection emails for 2 overdue clients ($41K) and a payment shift request to PolyMold. Approve to send. This resolves the gap and restores your cash runway to 71 days."
    Result: A cash crisis that would have blindsided the owner was identified 6 weeks early, root-caused to specific clients, and solved with three emails — drafted, personalized, and ready to send. The owner wakes up Sunday morning, taps "approve" three times, and it's handled.
    Supply chain disruption — detected and resolved before production stops
    Pattern Detection
    Vendor performance sweep. Pacific Resins has been late on 4 of the last 6 shipments. Average delay increasing: 2 days → 5 days → 9 days. Trend is accelerating.
    Supply
    Next order from Pacific Resins due in 18 days. Current resin inventory covers 14 days of production. If they're late again (likely 9+ days based on trend), production stops for 5+ days.
    Production
    5 days of downtime = ~$28K in lost output. Active orders affected: 3 client shipments totaling $47K would miss delivery windows.
    Money
    Late delivery penalties in 2 of 3 client contracts. Total exposure: $6,200 in penalties + risk of losing Client B's Q3 renewal ($180K annual).
    Action
    Searches vendor database for alternative resin suppliers. Finds 2 qualified alternatives. Compares pricing, lead times, MOQs. Drafts expedited PO to Midwest Polymers (8-day lead time, 12% premium but in-spec). Drafts email to Pacific Resins requesting delivery guarantee or contract review. Queues both for approval.
    Notification
    "Pacific Resins reliability is degrading — 4 late shipments, delays growing. Your next order is at risk. A stockout would halt production for 5 days ($28K lost) and jeopardize 3 client deliveries ($6.2K penalties, $180K renewal at risk). I've sourced Midwest Polymers as backup (12% premium, 8-day lead) and drafted a performance notice to Pacific Resins. Approve to execute."
    Result: A vendor reliability problem that would have become a production crisis was caught by pattern detection across 6 months of delivery data. The system quantified the financial exposure across 3 domains, found an alternative supplier, and prepared the solution — all before the owner knew there was a problem. Total time from detection to ready-to-execute solution: 90 seconds.
    Design Principles
    What Holds It Together
    One system, invisible specialization
    The user talks to Fibre. They never see domains, identities, or routing. The specialization is backend optimization for decision quality, not UI complexity.
    LLM reasons, code enforces
    The LLM handles reasoning and judgment. Code handles guardrails, validation, routing, and constraints. Never ask the LLM to self-regulate.
    Everything is an event
    Every state change starts as an event. Tables are projections. The event stream is the source of truth. Full audit trail from trigger to decision to action.
    The system builds itself
    Rules are data the agent creates. Tools are data the agent proposes. Workflows emerge from event chains. The system gets smarter and quieter over time.