ERP has always recorded the past. We're teaching it to see ahead.
Kriyo Cloud is building AI into the core of Fortress ERP. It isn't a chatbot bolted to the side. It works inside production, planning, quality, maintenance and finance, where it can help a manufacturer find bottlenecks, plan better, waste less and see trouble coming. Here is exactly what we're building, how it works, and the rules it will follow.
Rules someone typed in three years ago.
A classic ERP plans with fixed numbers: a reorder point, a seven-day lead time, a standard cycle time. They were right once. Then a customer grew, a vendor slipped, a festival moved, a mould wore out.
- Planning is static. Seasonal demand arrives before the reorder point notices.
- Quality is reactive. You learn about drift when the inspection fails.
- Insight needs an analyst. Reports answer only the questions someone thought to build.
- Data entry never ends. Every vendor invoice is typed in by hand.
A fixed reorder point treats every week the same. A forecast sees the season coming three weeks early and tells MRP to buy resin before the rush, not during it. (Illustrative data.)
Eight capabilities, each built on something Fortress already does.
AI is only as good as the data under it. Because Fortress already records orders, receipts, inspections, downtime and postings in one structured ledger, the AI starts with clean history, not a data-cleaning project.
Bottleneck & efficiency finder
Work center efficiency (planned vs. actual hours), OEE, scrap and cost-variance reports, read by a person.
Traces every work order through its routing to find where work queues up, which operations run slower than standard, and where material, labour and machine time leak away. It puts a rupee figure on each loss and suggests the fix: rebalance a line, move a job to an idle work center, change the sequence, or schedule maintenance. Then it tracks whether the fix worked.
Demand sensing for MRP
MRP plans from open sales orders and a fixed reorder point per item.
Forecasts per item learn seasonality, festivals and customer patterns from your history and set reorder points and safety stock for you, item by item, every week.
Ask Fortress
A four-step report builder for anyone who knows which tables to pick.
Ask in plain English, or Hindi or Marathi, and get the answer as a report. Fortress shows the query it ran, so you can check it, save it and schedule it.
Predictive quality
Inspections, non-conformances and CAPAs, raised when something fails.
Watches inspection readings drift toward a limit before they cross it, and scores each vendor's quality risk from receipts, NCRs and returns.
Downtime & maintenance insight
Downtime by machine and reason code, with OEE per work center.
Finds the patterns people miss, such as the same breakdown every third Monday or after a mould change, and suggests when a machine should come down for maintenance.
Document reading
Vendor invoices and customer POs typed in by hand, then matched.
Reads PDF and scanned invoices and purchase orders into draft documents. The three-way match and approvals still apply; nobody skips the controls.
Schedule optimisation
A finite scheduler and a planning board the planner arranges.
Proposes job sequences that cut changeovers and late orders, and predicts realistic completion dates from actual cycle times, not standard ones.
Finance watch & cash foresight
A full general ledger, AR and AP ageing, and bank reconciliation.
Flags unusual journals and likely duplicate vendor invoices, and predicts when each receivable will actually be paid, so the cash forecast is about customers, not averages.
Find the slowest step. Price the waste. Suggest the fix.
Every plant has one operation that sets the pace for all the others. Fortress already records each work order's operations, labour, machine time, downtime and scrap. Fortress Intelligence reads that record end to end to find where work piles up, what it costs you, and what to change first.
Moulding runs at 97% while trimming and packing sit below 45%. Jobs wait 6.5 h on average before moulding, and cycles run 18% over standard on IMM-03.
- Move PL-TR-12 jobs to IMM-02 (61% utilised)+9% line output
- Group blue and natural runs to halve colour changeovers−5.5 h / week
- Service IMM-03 heater bands, 3 trips in 30 days−95 min downtime / trip
Identify
Queue time, utilisation and actual-vs-standard cycle time per operation show which work center is the constraint this week, not the one everyone assumes.
Quantify
Each inefficiency gets a rupee figure: idle hours waiting for material, slow cycles, changeovers, rework and scrap, so you fix the most expensive one first.
Improve
Concrete suggestions, such as moving jobs to an under-used machine, resequencing to cut changeovers, splitting a batch or planning maintenance. Accepted fixes are tracked to see if they worked.
From your ledger to a decision you make.
Five stages, and a human at the end of every one of them. The same approval rules, segregation of duties and audit log that govern your team govern the AI.
- 01
Your ledger
Orders, receipts, work orders, inspections, downtime, postings. Already structured, already in Fortress.
- 02
Business context
A semantic layer that knows a lot is not a location, and that a BOM ties items together.
- 03
Models
Forecasting and anomaly models on your history, and language models that use Fortress as a tool.
- 04
Suggestion inbox
Every output is a suggestion with its reasons attached. Nothing posts by itself.
- 05
You decide
Accept, edit or dismiss. Accepted suggestions go through the same approvals and audit log as anything else.
Ask the way you'd ask your plant head.
Owners shouldn't need an analyst to find out which vendor is always late or why scrap went up. Ask Fortress turns a plain question into a report built from your own data, and shows you exactly how it got the answer.
The preview shows the interface we're building. The data in it is illustrative.
Five promises we're building in, not bolting on.
Manufacturers run on trust and tolerances. AI in an ERP has to earn both.
Suggestions, never autopilot
AI proposes. A person accepts, edits or dismisses. Nothing posts to your books on its own.
Shows its working
Every suggestion comes with its reasons: the data it used, the query it ran, and how confident it is.
Your permissions apply
AI sees only what the person asking is allowed to see. A store-keeper's question never reaches payroll.
Your data stays yours
Your ledger is used to serve your company. We will not use it to train models for anyone else.
Measured, not marketed
Each suggestion's outcome is tracked. If a forecast isn't beating your reorder point, you'll see that too.
Rolling out over the coming months.
Early-access partners get each phase first and help us tune it on real plants before general release.
- Phase 1
Early access
- Bottleneck & efficiency finder
- Ask Fortress
- Demand sensing for MRP
- Phase 2
Next
- Predictive quality
- Downtime & maintenance insight
- Document reading
- Phase 3
After
- Schedule optimisation
- Finance watch & cash foresight
Join early access.
We're picking a small group of manufacturers to shape Fortress Intelligence with us. You get every phase first, direct access to the people building it, and a say in what it learns to do.