Documentation
Technical reference for the Industrial Genome Platform.
Product Overview
What Genome is, who it's for, the six fingerprint dimensions, seven archetypes, and the D1–X3 constraint taxonomy.
Math & Methodology
How Genome works: the six system-ID algorithms (latency, gain, damping, oscillation, saturation, volatility transmission), archetype classification rules, constraint taxonomy signal mappings, and judgment engine logic.
Component Architecture
Full stack from signal ingestion through fingerprinting, classification, Studio UI, and commercial outreach engine. Includes file structure, algorithm details, and database schema.
Commercial Engine
Status and plan for the autonomous outreach pipeline: targeting → genome report → email generation → response classification → strategy learning. Includes CLI reference and build roadmap.
Potential Extensions & Integrations
Genome's public signal layer is the floor, not the ceiling. Each integration below replaces inferred constraints with observed ones — increasing fingerprint accuracy and reducing the time to insight.
Ops Maturity
Ready to wireField-observed constraints replace inferred ones
Ops Maturity captures ground truth inside a facility: bottleneck photos, AI-scored maintenance backlog, assessor notes on supplier issues, labor turnover, process map cycle times. Today, Genome infers these constraints probabilistically from public signals. Ops Maturity field data collapses that uncertainty to a known answer.
Assessment save → extract constraint signals → POST to /v1/judgment/evaluate with anchor_node → Genome wraps field evidence with macro context, peer comparison, trajectory
Genome report generated automatically from every Ops Maturity assessment. PE firm gets a field-anchored fingerprint with public signal context — no extra work for the assessor.
CDI / LNS Research
Signal layer readyIndustry intelligence feeds macro context
CDI's signal intelligence layer tracks 10+ industrial sectors with IPI scores, market shaping events, and causal models per industry. This is exactly the industry context layer Genome needs for cohort benchmarking and trajectory calibration.
CDI sector signals → Genome sector_outputs() → fingerprint context. IPI scores per company → Genome peer comparison calibration. CDI sprint structure → Genome archetype narrative framing.
Every CDI sector briefing gets a Genome overlay: which companies in this sector are constrained, oscillatory, saturated — and which are moving toward fragility.
ERP / MES Integration
Architectural path definedInternal operational data replaces all public proxies
For operating companies (not just PE diligence), connecting to ERP/MES gives Genome direct access to revenue by product line, inventory levels, cycle times, OEE, and labor metrics. This replaces EDGAR + FRED proxies with actual operating data — orders of magnitude more accurate fingerprints.
ERP extract (weekly/monthly) → signal normalizer → Genome TimeSeries → fingerprint. No EDGAR, no FRED needed — direct observable inputs and outputs.
Operating Command becomes a real-time COO tool — not quarterly, not EDGAR-lagged. Fingerprint refreshes weekly. Constraint drift detected before it becomes a crisis.
Private Equity Deal Flow
Commercial targetGenome as diligence infrastructure
PE firms run 50–200 targets per deal cycle. Genome can fingerprint every company in a sector universe overnight using public signals, rank by constraint type and fragility score, and flag the most interesting situations — before any analyst time is spent.
CRM deal list → batch_fingerprint → ranked output by archetype + constraint → automated outreach to highest-signal targets.
Compress deal sourcing from weeks to hours. Replace gut-feel sector screening with system-level behavioral signal.
Supply Chain Risk
Signal gap existsAIS, trade data, and port signals into R-node detection
Genome's Risk nodes (R1–R4) currently infer from FRED financial stress and commodity signals. Real supply chain risk signals — AIS vessel tracking, Panjiva trade flow data, port congestion, Freightos freight rates — would make R1 and R4 quantitative rather than inferred.
AIS vessel positions → shipping delay index → R4 Geopolitical / F4 Logistics signals. Trade flow shifts → R1 Supply Fragility early warning. Port congestion → F3 Lead Time pressure.
R1 Supply Fragility becomes a leading indicator, not a lagging inference. Companies in the tail of a disruption show up in Genome before the 10-Q does.
Environmental / ESG Signals
Emerging opportunityRegulatory and physical climate risk as constraint nodes
EPA enforcement actions, carbon pricing trajectories, physical climate risk (flood zones, drought indices) all map directly to Genome constraint nodes. R2 Regulatory Risk and X2 Regulatory Cap are currently underweighted — ESG data would make them quantitative.
EPA enforcement database → R2 Regulatory Risk score. Carbon credit pricing → C1 Input Cost modifier. Physical climate risk indices → R4 Geopolitical proxy for physical exposure.
Genome fingerprints the transition risk exposure of every industrial company — which ones face a constraint cascade from decarbonization, and how fast.