A NOTE FOR WOVEN
Every retailer drowns in signals — inventory shifts, pricing pressure, customer behavior, supply chain disruptions. Woven OS turns that noise into a structured decision pipeline: detect what matters, calculate the best response, act automatically, and learn from every outcome.
See Woven OS as a standalone product platform
View the Woven OS Homepage →THE RETAIL PROBLEM
A regional grocery chain makes 10,000+ pricing and inventory decisions per week. A fashion retailer recalculates markdown timing across hundreds of SKUs every cycle. A convenience store operator juggles supplier lead times, weather-driven demand, and perishable waste — simultaneously.
Most of these decisions are made by humans staring at spreadsheets, or by rigid rules that can't adapt. The ones that get made well — the ones that account for context, history, and downstream effects — are the ones that separate thriving retailers from struggling ones.
Woven's thesis: retail decisions shouldn't be guesses. They should be structured, context-aware, and continuously improving.
That requires a system that ingests every relevant signal, classifies what matters, calculates the best action, executes it, and feeds the outcome back into the next decision. Not a dashboard. Not a report. A decision pipeline.
WHY IT'S HARD
The average mid-market retailer runs 15-30 disconnected systems: POS, inventory management, CRM, loyalty, supply chain, workforce scheduling, e-commerce, competitive pricing feeds, weather APIs, foot traffic sensors. Each one generates signals. None of them talk to each other in a way that supports real-time decision-making.
Siloed Signals
POS data lives in one system, inventory in another, customer behavior in a third. By the time a human synthesizes them, the window for action has closed.
Rigid Rules
Most automation in retail is if-then rules written years ago. They can't weigh competing signals, account for context, or learn from outcomes.
No Memory
Decisions happen, but outcomes aren't tracked against the context that produced them. There's no feedback loop. The same mistakes repeat every cycle.
Dashboard Fatigue
More dashboards don't solve the problem. Retailers don't need more data visibility — they need systems that act on the data with judgment.
This is the gap Woven OS fills: a single architecture that unifies signals from every source, applies structured decision logic, takes action, and improves with every cycle. Not another integration layer. A decision intelligence platform.
THE PROOF
The Woven OS decision pipeline — detect, calculate, orchestrate, record — isn't theoretical. Lab 36 built and operates this exact architecture 24/7 for a different domain: knowledge organization across a multi-agent fleet. Different signals. Different actions. Same pipeline.
That means the hardest engineering problems — multi-source signal ingestion, real-time classification, autonomous orchestration, persistent state with feedback loops — are already solved. What's left is domain adaptation: swapping knowledge categories for retail categories.
WOVEN OS PIPELINE
LAB 36 PIPELINE
ARCHITECTURE MAPPING
Every layer of the Woven OS framework has a running analog in Lab 36. This isn't theoretical. These components are deployed, battle-tested, and processing real signals every day.
| Woven OS | Lab 36 | What It Does | Status |
|---|---|---|---|
| Detection Layer | Conduit | Classifies inbound signals (RSS, scrape, voice, agent output) into domain-appropriate categories. Deterministic rules + ML classification. | Deployed |
| Calculation Layer | Inspector | Synthesizes classifications + context into "is this worth acting on?" decisions. Probabilistic scoring. The analog of value-of-intervention calculation. | Deployed |
| Orchestration Layer | Lab Agent + Forge | Routes decisions into actions: ingest to vault, queue for synthesis, escalate to the operator, trigger content pipeline. Cross-domain atomic workflows. | Deployed |
| State Ledger | Vault (QMD) | Persistent knowledge store with entity states, relationship maps, ingestion history, TTL tracking. Feeds outcomes back into next-cycle classification. | Deployed |
| Context Model | Synthesis Engine | Maintains unified picture of the organization's knowledge: what's known, what stage it's in, what's expiring, what's underweighted. Real-time dashboard via Vault Pulse. | Deployed |
| Signal Rules | UIL Routes | Classification rules that define how signals get categorized and routed. "If source=YouTube AND pillar=Digital, route to Carbon Pipeline." | Deployed |
| Action Catalog | MCP Tool Registry | ~10 MCP servers, each with 6-20 tools. Every tool is an action the system can invoke. Governance via permission levels (readonly, default, bypass). | Deployed |
| Domain Configs | Pillar Definitions | Six life pillars (Physical, Mental, Spiritual, Digital, Financial, Culture) define the domain taxonomy. Swap pillars for retail categories — pipeline stays the same. | Deployed |
WHY THIS MATTERS FOR WOVEN
Building a decision intelligence pipeline from scratch takes years. This one is already running.
Every day, the same detect-calculate-orchestrate-record pipeline processes real signals, makes real decisions, and ships real outcomes — across content, finance, operations, and culture domains. The infrastructure Woven needs isn't a roadmap item. It's deployed.
Signal Processing
Conduit classifies incoming signals into domain-appropriate buckets every time content arrives. Same deterministic routing a retail system needs for event classification.
Decision History
Inspector has months of real decision history — which signals the operator acted on, which were deprioritized. This is the training data for improving value-of-intervention scoring over time.
Feedback Loops
The synthesis engine feeds outcomes back into the next classification cycle. Same closed-loop architecture that Woven needs for continuous model improvement.
Multi-Agent Fleet
12 agents across 5+ AI providers, running on owned hardware and cloud APIs. Provider diversity, role specialization, and graceful degradation — production-tested.
FIRST WORKFLOWS
A design partnership isn't a research project. Here's what the first phase delivers — concrete workflows running on real retail data, built on the proven pipeline.
Markdown Timing Engine
Ingest sell-through velocity, inventory age, and competitive pricing signals. The pipeline calculates optimal markdown timing per SKU — not a static schedule, but a dynamic response to real conditions. Ships in weeks, not months.
Stock Rebalancing Alerts
Detect inventory imbalances across locations before they become stockouts or dead stock. The orchestration layer routes rebalancing recommendations to the right decision-maker — or executes them automatically within defined thresholds.
Promotion Impact Tracker
Close the feedback loop on promotional decisions. Track which promotions drove incremental margin vs. cannibalized full-price sales. Feed outcomes back into the next promotion cycle. This is the "memory" most retailers lack.
Decision Audit Trail
Every decision the system makes — and every decision a human overrides — gets recorded with full context. Over time, this becomes the training data that makes the pipeline smarter. The ledger that turns tribal knowledge into institutional intelligence.
These aren't hypothetical features. Each workflow maps directly to a running Lab 36 analog — signal ingestion, classification, action routing, and outcome tracking are all deployed today. The retail domain config is the only new variable.
THE ECOSYSTEM
Van's Framework
Cross-domain orchestration with autonomous decision-making. The framework concept that emerged from networking patents and years of thinking about how to make the physical world as reliable as the digital one.
Jackson's Simulator
Real event data from airline operations — the domain-specific signal source that the decision intelligence layer needs for a working demonstration.
Lab 36's Infrastructure
The battle-tested intelligence pipeline — detection, calculation, orchestration, state management — running 24/7 across a multi-agent fleet. Not a prototype. Not a whiteboard. Production infrastructure.
Van described a pattern he'd been working on for years. Lab 36's creator realized: "We already built that." Three pieces fit together: Van's framework concept + Jackson's event data + Lab 36's infrastructure = working prototype, demo-ready.
PLATFORM VISION
| Layer | Lab 36 (Knowledge) | Woven (Retail) | Future (Airlines) |
|---|---|---|---|
| Signals | RSS, scrape, voice | POS, inventory, loyalty | Flight, crew, weather |
| Detection | Conduit classifies pillars | Metric deviation scoring | Delay cascade detection |
| Calculation | Inspector scores relevance | Incremental value likelihood | Rebooking ROI analysis |
| Action | Ingest, synthesize, broadcast | Reprice, restock, reroute | Rebook, reroute, compensate |
| State | Vault (5,500+ docs) | Customer + inventory graph | Flight + passenger graph |
The pipeline doesn't change. The domain config does. Lab 36 proves the core architecture is sound. Woven applies it to retail. The same framework can extend to airlines, logistics, financial services — any domain with events, entities, and decisions.
DESIGN PARTNER PROGRAM
Lab 36 isn't pitching a concept. We're offering a running system, a proven architecture, and the engineering capacity to stand up the first Woven OS vertical — together. A design partnership means we build it with you, not for you.
01
Working Prototype
The detection-calculation-orchestration-ledger pipeline is deployed and processing signals today. We bring production infrastructure, not a slide deck.
02
Domain Adaptation
Swap life-pillar taxonomies for retail categories. Plug in Jackson's airline event simulator. The architecture stays the same — the domain config changes.
03
Joint Development
Shared roadmap, shared codebase access, direct collaboration with the engineer who built and operates this system every day. No handoff. No black box.
Design Partners get direct access to the running Lab 36 infrastructure, weekly working sessions, and a dedicated integration pathway from prototype to production.
Retail. Airlines. Financial services. The architecture doesn't care about the domain — it cares about events, entities, and decisions. Lab 36 proved it works. Now let's prove it scales.
LAB 36 × WOVEN