Use case
Quote faster and close the loop.
Price with your shop’s DNA.
Today’s quoting still relies on people to predict production costs. With Werk24 + Saphirion, you can measure actual costs, feed them back into pricing, and make the loop self‑correcting. Ideal for shops that repeatedly produce a handful of part families.
The bottleneck in quoting
Manual reading of 2D drawings and gut‑feel costing make quotes slow and inconsistent. Estimators must predict cycle time, setups, scrap, and risk from memory. Under pressure or with mixed experience levels, you see quote variability, margin slippage, and long cycle times.
Closing the loop with measured costs
In most shops today, quoting is still based on estimates and intuition. Actual costs are only reviewed much later by cost control, meaning valuable insights arrive too late to improve the next quote.
A closed loop changes that. Every finished job delivers hard data — machine time, tooling, QA, even scrap — which flows directly back into your pricing model. The system self‑calibrates continuously, so each new part makes the next quote more accurate.
The result: quotes that reflect reality, less reliance on guesswork, and a pricing engine that improves with every order. The same signals also highlight process bottlenecks, helping you refine operations as you refine your prices.
- Measure actual production cost at job close (time tickets, machine data, tooling, QA, scrap).
- Feed back automatically into the next quote for similar parts — your model self‑calibrates.
- Reduce reliance on guesswork: the system learns from real costs instead of only expert intuition.
- Continuously improve pricing and operations: the same signals that tune price reveal process bottlenecks.
Is this for you?
Best fit
- Manufacturers producing a small set of part families (e.g., turned shafts, milled plates, common subassemblies).
- Recurring RFQs where features repeat and learning compounds.
- Desire to codify tribal knowledge and standardize quoting.
Not ideal
- Every part looks different (pure job‑shop variety with no clustering).
- No access to actual production costs, or unwillingness to measure them.
How it works
Start with ~100 representative drawings and their prices/costs. Werk24 structures the data; Saphirion fits an interpretable pricing formula you can audit, iterate, and run in production.
Provide a sample pack You
Provide ~100 PDFs/scans and the matching historical prices or actual production costs (per part). Use stable, comparable SKUs; avoid bundled/one‑off specials. If variants exist, include quantity and finish.
Extract & normalize Werk24
Werk24 parses title blocks, GD&T, materials, tolerances, hole tables, finishes, and notes — then returns clean JSON with units and confidence. Includes: dimensions, tolerances, GD&T, materials/specs, operations hints, drawing metadata, surface roughness, and hole/slot tables.
Fit the pricing formula Saphirion
Train an interpretable model to produce a human‑readable formula with error bands and driver importance.
Detect outliers & gaps Saphirion & You
Saphirion highlights inconsistent historical prices, missing drivers, extraction anomalies, or under/over‑predictions. Prevents brittle formulas and reveals hidden process drivers that meaningfully shift cost. Requires discussions with your cost engineers.
Review & iterate Saphirion & Werk24 & You
Fix mappings, add drivers, or drop poor records; refit until KPIs are met.
Integrate & go live You
Integrate the automated price/cost calculation into your existing processes and tools (e.g., Salesforce).
Close the loop
Feed actual production cost after job close; the model self‑calibrates and improves the next quote (automated through your existing tools).
Outcomes
Price faster, keep margins disciplined, and explain every number.
Automated quoting proposals
Generate automated quoting proposals for standard parts. Reviewers focus on exceptions and complex parts.
Self‑calibrating prices
Measured production costs feed back automatically to tune the formula.
Higher RFQ throughput
Automated processing absorbs spikes; low‑confidence cases get triaged.
Consistent margin discipline
Guardrails across customers, teams, and sites — no silent discount creep.
Traceable rationale
Every price is explainable — driver weights and an audit trail.
Cost‑structure insights
Understand what drives manufacturing costs.
Metrics that matter
Track the signals that show quoting is not only faster, but also more predictable, competitive, and profitable.
- Speed: Time‑to‑quote (P50/P90) to prove cycle‑time improvements.
- Competitiveness: Win rate and price realization vs. target margins.
- Discipline: Gross‑margin variance across estimators, sites, or teams.
- Accuracy: Quote‑to‑actual cost delta — should steadily trend toward zero.
- Model health: Forecast accuracy (MAPE) and human override rate.