Predictive pricing.
The engine predicts — the formula decides.
Most "predictive pricing" predicts the price itself — a number nobody can decompose. Pricera predicts what no formula and no actuals can supply instead: the yield of a configuration never run, the lead time a line can actually hold, the chance a deal is won. Models learn from the systems you already run; predictions arrive as labelled inputs and advisory signals; the price stays a formula your team owns and can replay.
Predict the inputs, never the price
ML that outputs prices — the decider
- Predicts the number itself; the reasoning is hidden
- Sales can't decompose it; auditors can't replay it
- Optimizes its objective — elasticity, margin targets — not your contract
- When it drifts, nobody can see where or why
ML that feeds governed models — Pricera
- Predicts what no actuals exist for yet: yield on unseen configs, lead time, win probability
- Every prediction lands labelled — which model, which data, how confident
- Proposed values pass the same reference cases and approvals as human edits
- The price stays a readable formula your team owns
The rule from AI-governed pricing holds: AI works inside the model, never above it. Prediction is no exception — a predicted yield is still an input, and the formula still computes the price.
Learns from the systems you already run
No new pipeline to build, no data-science team to hire. Pricera connects to the systems that already record your operations and trains prediction models on your own tables — SKUs, spec text, missing values and all.
ERP & orders
Actual costs, volumes and realized margins per product, customer and region — the ground truth every costing model is judged against.
MES & production
Real yields, scrap and cycle times per configuration, line and material — so the cost of a variant is learned, not guessed.
CRM & quoting
Wins, losses, discounts and deal cycle times per segment — the evidence behind every quote-side signal.
Finance & costing
True product and customer cost via CostCtrl — time-driven and multi-stage ABC that keeps predicted margins honest.
What it predicts — and where it lands
Every prediction has a governed destination. Inputs land inside the model; signals land beside the quote; flags land in a review queue.
Yield & scrap
Expected yield for a configuration never run — inferred from runs that resemble it.
→ labelled input · inside the cost treeLead time
Realistic delivery time per configuration and current plant load.
→ governed input · expedite & capacity logicInput forecasting
Index and tariff trajectories over a contract term — forward inputs for LTA quotes.
→ proposed input · long-term agreementsWin probability
The likelihood a quote converts at its governed price, from deal history.
→ advisory signal · beside the quote lineQuote effort
The estimation work an RFQ will take — and whether similar quotes converted.
→ advisory signal · bid / no-bid triageRenewal risk
Contract customers showing churn signals before the renewal window.
→ trigger · re-quote & retention workflowEvery prediction is governed like every edit
Labelled on the quote
A predicted value inside a calculation is marked as predicted, with the evidence behind it. The waterfall shows predicted_yield 91.4% — never a silent number.
Gated before publish
Proposed inputs land in draft model versions. Reference cases re-run, a pricing owner approves — or nothing reaches sales.
Advisory, never automatic
Signals like win probability inform the seller; they never rewrite the price. The logic that decides is the logic you can read.
billet (LME + prem) €1.18 /m
+ press_rate (22 MN) + €0.74 /m
+ predicted_yield 91.4% · learned · 4 213 similar runs − €0.09 /m
= cost_total €2.65 /m
× margin (+18%) + €0.48 /m
final_price €3.13 /m · win prob 0.62 · advisory
How prediction runs inside Pricera
The result is the insight people want from ML pricing without the accountability gap: predictions that sharpen every quote while the number that goes out the door stays a formula your team can defend.
Predictive pricing, answered
Does machine learning set prices in Pricera?
No. Machine learning predicts what no formula and no actuals can supply — yields on unseen configurations, lead times, deal outcomes. The price itself is always computed by the explicit, versioned model your team owns and can replay.
Is this the same as AI price optimization?
No — it is the complement, not the category. Price optimizers output recommended prices that cannot be decomposed or defended. Pricera's predictions output inputs and signals you can inspect; the number on the quote comes from the governed formula, with its derivation intact.
How much data do we need to start predicting?
Less than most teams expect. Prediction models are trained per tenant on your own tables — a few years of order lines, production runs or quotes is typically enough to be useful, and accuracy improves as new data lands. Sparse areas simply stay formula-driven until evidence accumulates.
Can a predicted value be audited later?
Yes. Predicted inputs are labelled on the quote and pinned with the model version used, so a historical quote replays with the same prediction context it was priced on. Nothing enters the waterfall silently.
Which systems can Pricera connect to?
ERP and order management for actuals and volumes, MES and production systems for yields and cycle times, CRM for quote outcomes, and finance for true cost — including CostCtrl for activity-based product and customer costing. Connection details are scoped per deployment.