Nextrion connects multi-location operations into one ontology: what exists, how it relates, and what can be done.A decision leaves it with the evidence behind it and an owner. The next run measures whether it landed.
The platform runs on our ontology, the technology that
turns how an operation runs into decisions.
From Small Business
Operations run in pieces.
POS, kiosks, delivery apps, access systems, and staffing ledgers never connect. Every location makes the same calls from gut feel, one screen at a time.
POS, delivery channels, and staffing never reconcile, so tonight's prep and tomorrow's schedule still come down to gut feel.
Decisions, not dashboards.
BI shows what happened. A model can produce a plausible reason, but nothing traces back to it, and an answer you cannot show an approver is no use where approval is required. A consultant gives you this month's answer, and you buy it again next month.
Decision Intelligence works the other way. It holds the question, the options, the evidence, and who chose, as one record that the next run can check.
Context, not columns.
Ontology based AI Architecture
How one decision closes.
A trigger fires, the model recommends, and something happens. What changes between those steps is how much of it you hand over. What does not change is the last column.
- BI stops here
- Recommendation
- Raise safety stock from 12 to 18 days
- Confidence
- 91%
- Impact
- ₩42M
- Alternatives
18 days stockout 6% recommended 15 days stockout 14% keep 12 stockout 31% - Premortem
- If demand lands below 80% of forecast,
₩9M of excess inventoryTemplate · model output inserted
Nothing is marked resolved because someone said so. The next run measures the same subject with the same model version, and the decision closes only when the result is confirmed.
The recommendation shows what moved it.
Not a score with an explanation bolted on afterwards. Every recommendation carries the factors that pushed it, the ones that pushed back, and what would change the answer.
What pushed it
- Demand forecast, revised up — pushed toward the recommendation
- Lead-time variance, widening — pushed toward the recommendation
- Seasonal index — pushed toward the recommendation
What pushed back
- Current cover, 9.4 days — pushed back against the recommendation
What would change it
If lead-time variance returns to baseline,
the recommendation drops from 18 days to 15 days.
The model card travels with it: training window, retraining cadence, known limits, risk tier, and the date it was last validated.
A decision carries what it expected.
A decision is not finished when someone picks an option. It carries what that option was supposed to produce, and the next run is what fills that in.
- at decision time
Decision record
- Decided
- recorded
- Owner
- recorded
- Expected
- recorded
- Measured by
- recorded
- Actual
- not yet recorded
awaiting next run - after the next run
Decision record
- Decided
- recorded
- Owner
- recorded
- Expected
- recorded
- Measured by
- recorded
- Actual
- recorded
confirmed - if it falls short
Decision record
- Decided
- recorded
- Owner
- recorded
- Expected
- recorded
- Measured by
- recorded
- Actual
- recorded
→ New decision
The decision outlives the cycle that produced it. When the same trigger fires next month, what was decided, and what it was worth, is still on the record.
The industry is configuration, not code.
The screen reads the ontology. It does not know what you sell. Change the object types and the record keys, and the same screen reads a different business.
Distribution is the configuration that exists today. Life insurance is shown as what configuration would carry.
- POS SALE closes into STORE CLOSE
- POS SALE consumes SKU
- POS SALE supplied by VENDOR
- POS SALE reconciles with SETTLEMENT
Nowhere in the screen's code is there a name for a product, a claim, or a contract.
The limits are in the product.
Autonomy is something you configure, not something the product assumes. And the boundaries are enforced in code, not written in a policy document.
Agents call models, not systems.
An agent can only invoke a decision model that has been versioned, backtested, and approved. There is no free-text execution path.
Automatic runs have a ceiling.
Impact thresholds, action budgets, and guardrails are part of the decision table. Anything outside them lands in the approval queue instead of executing.
The model never invents a number.
Every generated sentence is labelled with how it was made. Figures come from the ledger. The model phrases them and cites what it read.
Run every location
on one model.
Restaurants, lobbies, retail floors.
If your operation runs on disconnected systems, the ontology is built for you.