We train enterprise AI by connecting every source into one decision context, not by pouring more raw data into a model. Data is ingested where it lives, entities are resolved across systems, relationships become a knowledge graph, each decision retrieves only the grounded slice it needs, forecasting, optimization, rules and LLM reasoning are composed into one decision, and every approval, override and measured outcome flows back as a label that improves the graph, the thresholds and the models.
Why more data and bigger models aren’t enough
Most enterprise AI fails at the seams. The pump in the historian, the asset in the CMMS and the cost center in the ERP are the same thing under three IDs. The SOP that governs it lives in a PDF. The engineer who knows it always fails after a hot week is not in any system at all. A model trained on any one of those sources sees a fragment.
Decision intelligence treats connection as the core training problem: if the context for a decision is complete and correct, even simple models make good recommendations — and large language models stop guessing.
The six steps
- Ingest where it livesConnect ERP, MES, historians, sensors, EHR, TMS, documents, tickets and email with connectors and change-data-capture. No big-bang migration.
- Resolve entitiesMatch the same asset, order, customer, patient or robot across systems, with confidence scores and human review for ambiguous merges.
- Connect a knowledge graphModel relationships — belongs to, feeds, governed by, caused — so the system can follow cause and consequence across silos.
- Ground every decisionRetrieve only the relevant slice of the graph, documents and history for the decision at hand; score context health and ask for what is missing.
- Compose many modelsForecasts, anomaly detection, optimization, simulation, rules and LLM reasoning each contribute evidence; the decision model weighs them against objectives and authority.
- Learn from outcomesApprovals, overrides, reasons and measured results become labeled training data — improving thresholds, rankings and models every cycle.
The feedback loop is the moat
↺ Every outcome feeds the context of the next decision.
Every override is a free, expert-labeled example of what the model missed. Every outcome closes the loop between prediction and reality. Over months this builds a proprietary dataset no foundation model has: how your organization actually decides, and what happened next.
Techniques we combine
| Technique | What it contributes |
|---|---|
| Entity resolution & record linkage | One trusted identity per asset, order, member or robot |
| Knowledge graphs | Relationships and causal paths across systems |
| Retrieval-augmented generation (RAG) | LLM answers grounded in your documents and graph, with citations |
| Time-series forecasting & survival analysis | When something will fail, run out or arrive |
| Optimization & simulation | Best option under constraints; Monte Carlo cost of waiting |
| Rules & statistical process control | Hard limits, thresholds and compliance |
| Learning from human feedback | Approvals and overrides as labels; calibrated confidence |
| Evaluation & guardrails | Offline tests, shadow mode, abstain rules, drift monitoring |
Safe by construction
- Connection, not volume, is what makes enterprise AI accurate.
- Many models composed beat one model stretched.
- Every human decision is training data — the loop compounds.