Manufacturing costing software that calculates in seconds what your spreadsheet takes days to produce.
Custom manufacturing costing software with ML-powered estimation — material costs, machine time, labour rates, overhead, and finishing requirements calculated in real time from your production data. We've already built this. A manufacturer's 3-day spreadsheet estimation process now runs in seconds with confidence-scored output.
Reduced to real-time — cost estimation in production
Reduction in paper approvals — Tejas Networks
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Building enterprise and manufacturing software
Your cost estimation process is a spreadsheet that one person understands.
A sales engineer gets a quote request. They open the spreadsheet. They pull material prices from one system, machine rates from another, check labour availability, look up finishing costs, calculate overhead allocation, and manually factor in tooling setup time. Two to three days later, the quote goes out.
If that estimator is on leave, the quote waits. If material prices changed since the last update, the estimate is wrong. If the quote is for a configuration the estimator hasn't priced before, they guess — and the margin either suffers or the bid is too high and the job goes elsewhere.
The real cost of slow estimation isn't the estimator's time. It's the quotes that never go out because the process is too slow, the jobs won on bad margins because the estimate was stale, and the institutional knowledge locked in one person's spreadsheet.
Still quoting from spreadsheets? Tell us what your estimation process looks like.
Describe your processCost estimation that runs on your production data, not one person's memory.
The system pulls live material costs from your ERP, current machine utilisation rates from the shop floor, historical labour actuals from production records, and overhead allocations from accounting. An ML model trained on your past production data calculates cost estimates for new configurations — in seconds.
Every estimate includes a confidence interval. The model knows when it's uncertain — novel configurations, unusual material combinations, or specifications outside the training data — and flags those for human review instead of producing a false-precision number. That design choice is why estimators trust it.
The model improves continuously. As production runs complete and actual costs are recorded, the system retrains automatically. Accuracy increases over time without manual intervention.
What the system covers.
Manufacturing costing software we've built and deployed.
ML-powered cost estimation: 3-day process replaced with real-time output
Situation: A manufacturer's quoting process required cost estimation from material type, dimensions, tolerances, finishing requirements, production volume, and current material prices. Each quote took 2–3 days using spreadsheets maintained by a single estimator. If material prices moved between estimation and production, margins eroded.
What we built: An ML cost estimator integrated into the manufacturing ERP. The model pulls live material costs, current machine utilisation rates, and historical labour actuals. It produces estimates with confidence intervals — the system tells the estimator when it's uncertain, rather than producing a precise-looking number that's wrong.
What changed: Sales engineers now quote during customer conversations. The estimator reviews flagged cases instead of calculating every quote manually. The model retrains automatically when prediction accuracy drops below threshold.
reduced to real-time cost estimation
Tejas Networks: enterprise platform for a publicly listed manufacturer
Context: Four interconnected enterprise systems built for a publicly listed telecom equipment manufacturer over a multi-year partnership. Procurement, vendor management, reporting, and intelligence — each system consuming data from the previous one. The same integration architecture applies to manufacturing costing: ERP data feeds the cost model, cost estimates feed quoting, actual costs feed retraining.
reduction in paper-based approvals
Custom costing software vs ERP costing modules and spreadsheets.
| Capability | Custom ML costing | ERP costing module | Spreadsheet |
|---|---|---|---|
| Estimation speed | Real-time — seconds per quote | Minutes to hours depending on complexity | 2–3 days per complex quote |
| Material pricing | Live feed from ERP and supplier APIs | Periodic updates — quarterly or monthly | Manual lookup per quote |
| Accuracy | 90–95% within 3 months; improves over time | Dependent on static rate accuracy — typically 15–30% off | Depends on estimator — varies by person and day |
| Confidence scoring | Every estimate shows certainty level | No confidence indicator | No confidence indicator |
| Novel configurations | Flags uncertainty; routes to human | Uses standard rates regardless | Estimator guesses from experience |
| Knowledge retention | Encoded in the model — survives staff changes | In rate tables — requires manual updates | In one person's head |
| Continuous improvement | Automatic retraining on actual costs | Manual rate updates | No systematic improvement |
Three concerns manufacturing companies have about AI costing.
"Our estimators won't trust an AI system."
They shouldn't trust it blindly — and the system is designed so they don't have to. Every estimate includes a confidence score. When the model is uncertain, it says so and routes to the estimator. Estimators use it as a first pass that handles the 80% of routine quotes, freeing them to focus on the 20% that need human judgment. Adoption works because the system admits its limits.
"We don't have enough historical data to train a model."
You need 6–12 months of production cost data with sufficient variety in product configurations. If you have fewer than 200 completed jobs with recorded actual costs, we start with a rule-based estimation engine using your current costing logic, then layer ML on top as data accumulates. The rule-based system is already faster than spreadsheets — the ML improves accuracy over time.
"Our costing variables are too specific for a generic AI tool."
That's why generic AI tools don't work for manufacturing costing. The model is trained on your data, with your cost drivers, your material specifications, your machine capabilities, and your overhead allocation method. Tooling changeover time, operator certifications, finishing requirements, waste factors by material grade — these are features in the model, not afterthoughts.
How we build your manufacturing costing system.
Every manufacturing engagement follows the same structure.
Common questions about manufacturing costing software.
Have a question about manufacturing costing?
Talk to us directly — no forms, no sales reps.
Show us your costing spreadsheet. We'll show you what replaces it.
Walk us through your cost drivers, your estimation process, and the quotes you're losing because estimation takes too long. We'll tell you what a production costing system looks like — and what it costs to build.
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