Polymr builds a manufacturing ontology and context graph from fragmented documents (BOMs, routings, POs, PDFs, spreadsheets) and tribal knowledge. We normalize heterogeneous data into a versioned, provenance-aware graph that AI-native reasoning can operate on. This often eliminates the need for consultants.
Long-term, we're building a standardized manufacturing ontology that connects factories—enabling interoperability, supply chain coordination, and network effects across manufacturing operations.
What are you building?
- Manufacturing ontology & normalization layer — extract entities, relationships, and constraints from unstructured sources
- Provenance-aware context graph — every fact cites its source document, version, and lineage
- Versioned graph snapshots — audit trail, rollback, and what-if scenarios
- AI-native reasoning layer — handles uncertainty, explains decisions, explores counterfactuals, and maintains provenance across planning, purchasing, scheduling, and exception handling
What reasoning capabilities does this enable?
- Uncertainty quantification — probabilistic demand forecasting with confidence intervals, propagate uncertainty through BOM explosions and planning decisions
- Explainable decisions — supplier selection with trade-off reasoning, lead time predictions citing historical data, transparent cost rollups linking to source documents
- Counterfactual exploration — "what-if" scenario analysis, bottleneck identification with causal reasoning chains, alternative routing suggestions
- Anomaly detection & recovery — detect deviations from expected patterns, propose substitutions with risk assessments, explain rerouting logic with provenance
- Regulatory interpretation — tariff classification with justification, export control logic that cites regulations, compliance reasoning over changing rules
Why now?
- Explosion of unstructured manufacturing data — spreadsheets, tribal knowledge, legacy systems
- Limits of legacy ERP/MRP systems — rigid schemas, poor interoperability, high implementation cost
- Recent advances in ontology extraction — LLMs, graph databases, and entity resolution now make automated extraction viable
Provenance, security, and auditability?
Every fact in the graph cites its source document, extraction timestamp, and lineage. We maintain full audit trails and versioned snapshots so you can trace any piece of data back to its origin, replay historical states, and incorporate human-in-the-loop corrections without losing provenance.
- Source citations back to original documents
- Full audit trail of graph mutations
- Versioned graph snapshots for rollback and compliance
- Human-in-the-loop corrections with preserved lineage
How do I get involved?
If this resonates — whether you're an investor, engineer, or operator — reach out directly.