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Artificial Intelligence

We did not bolt AI onto the product; we put it inside the decision. In Jovi ERP, AI is not a separate module — it is a recommendation layer standing next to the calls made every day in inventory, production and finance.

In most ERPs, AI arrives last: the system goes live, data accumulates, and a forecasting screen is added on top. That screen looks good, but nobody opens a purchase order by staring at it.

We did the opposite. AI sits next to the decision, not next to the report. The moment somebody on the purchasing screen asks “should we order this item today?”, the recommendation is already there — with its reasoning, and open to refusal.

The practical consequence: getting value out of AI does not require starting a separate data science project, hiring a new team, or reshaping your processes. The data that accumulates as you use the modules is the input the recommendation runs on.

Three principles

Decisions, hourly

Each module’s AI engine lives inside the hourly decision loop. It joins the operation, not the dashboard: it is there when an order is opened, a batch starts, a payment is scheduled.

A model you can see into

You can see which model suggested what, and why. The inputs a recommendation rests on come with it; AI recommends, the operator has the last word.

Sector models, ready to go

Models for 5+ sectors come ready and are tuned on your own data — live by end of day. There is no from-scratch modelling project to start.

How it works

Four steps to a recommendation. None of them asks you to build a new dataset.

  1. Data

    The input is the record your ERP already produces: a stock movement, a work order, an account, a quality check. There is no separate warehouse to stand up.

  2. Context

    The model sees the number framed by your business rules — lead time, shift capacity, shelf life, minimum stock — not the raw figure.

  3. Recommendation

    The output is a schema-bound structure rather than free text: quantity, date, urgency and reasoning. The same question comes back in the same shape every time.

  4. Decision

    The recommendation sits on screen as a button. Accept, revise or reject — whichever you choose is recorded.

AI, module by module

Each module's AI engine works on that module's own data. The list below is the full set of capabilities published on the module pages.

Production

  • Optimal production sequence from the order pool and capacity
  • Early bottleneck detection
  • Scrap trend forecasting
  • Performance analysis per operator and machine

Inventory

  • Reorder suggestions that weigh seasonality and movement velocity together
  • Early dead-stock warning
  • Batches flagged by remaining shelf life
  • Warehouse slotting suggestions

Procurement

  • Purchase timing suggestions from consumption and lead time
  • Supplier risk scoring
  • Price deviation detection
  • Anomaly alerts in quote comparison

Finance

  • 30/60/90-day cash flow projection
  • Late-collection risk scoring
  • Anomalous expense detection
  • Product profitability deviation alerts

CRM

  • Win-probability prediction
  • Early warning on customers whose order frequency is falling
  • Cross-sell suggestions
  • Deviation alerts on quoted prices

Human Resources

  • Shift plan suggestions driven by production load
  • Absence trend alerts
  • Attrition risk scoring
  • Skills gap detection

Quality

  • Root-cause suggestions from past non-conformities
  • High-risk batch prediction
  • Supplier quality scoring
  • Early drift alerts in SPC data

Maintenance

  • Failure probability from downtime history and running hours
  • Usage-based intervals instead of fixed periods
  • Critical spare stock level calculation
  • Recurring failure pattern detection

What it does

  • Finds patterns in history and turns them into a numeric recommendation.
  • States which inputs its recommendation came from.
  • Flags records outside the expected range — anomalies, drift, early warnings.
  • Answers the same question in the same structure every time.

What it does not do

  • It does not decide for you; nothing is applied without approval.
  • It does not invent a forecast where there is no data.
  • It does not give legal, financial or medical advice.
  • It does not start transactions or change records on its own.

Transparency and responsibility

Reasoning is mandatory

Every recommendation carries the inputs and the logic it came from. A number without a reason is not shown.

The operator has the last word

AI recommends, a person decides. The system applies nothing by itself.

Output is validated

The model reply is checked against the expected schema before you see it. A reply that does not fit is not displayed — it returns as an error.

The limits are stated

The playground and assistant on this site are demonstrations; their output can be wrong or incomplete. Do not enter personal data or confidential business information.

Details: Terms of Use · Privacy Policy

Playground

The three scenarios below run on the same model and the same schema constraint as the ERP. Swap the sample numbers for your own and run it again.

Sales Forecast

Forecast the next 3 months from last 6 months of data.

What comes back A 3-month forecast, a confidence level and the reasoning

Inventory Optimization

Get reorder recommendations from a product list.

What comes back Order quantity and urgency per SKU, with the reasoning

Production Suggestion

Generate a 14-day plan from pending orders.

What comes back A day-by-day plan, capacity usage and the reasoning

Sales Forecast

Swap the numbers for your own and run it again.

To prevent abuse, each scenario can be run 5 times per hour.

Frequently asked

Where does what I type in the playground go?

To produce an answer, your input is passed to the model provider we use. That is why we ask you not to enter personal data or confidential business information here. The details are in the Privacy Policy.

What if the model is wrong?

It can be. That is why no recommendation is applied automatically and every one is shown with its reasoning — an operator can read the reasoning and reject it.

Can it be tuned on our own data?

Yes. The sector models are a starting point; they are tuned on your own history and then put into service.

Can we turn the AI off?

Yes. AI is a recommendation layer; with it off, the modules keep working on classic rules and formulas.