Use case
Quote a machined part in 60 seconds —
and get more accurate with every job you ship.
Werk24 extracts every dimension, tolerance, and feature from your 2D drawings. Saphirion — a Swiss analytics firm specializing in manufacturing intelligence — turns your historical pricing into an auditable formula. Together they replace estimator gut‑feel with measured cost.
Laserhub: 1,000+ parts/month quoted, 98% accuracy on price‑driving features.Read the case study →
Why manual quoting costs you margin
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.
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.
What the extraction looks like
Werk24 doesn't just OCR text — it understands engineering drawings. Here is a real excerpt of the structured JSON returned for a single part. Every field includes units and a confidence score.
{
"titleBlock": {
"partNumber": "W24-2847-REV-C",
"material": { "standard": "EN 10088-3", "grade": "1.4404 (316L)", "confidence": 0.97 },
"generalTolerance": "ISO 2768-mK"
},
"dimensions": [
{ "type": "diameter", "nominal": 18.0, "tolPlus": 0.018, "tolMinus": 0.0, "unit": "mm", "feature": "H1", "confidence": 0.99 },
{ "type": "linear", "nominal": 45.0, "tolPlus": 0.05, "tolMinus": -0.05, "unit": "mm", "confidence": 0.98 }
],
"gdt": [
{ "characteristic": "position", "value": 0.1, "unit": "mm", "datumRefs": ["A","B"], "confidence": 0.96 },
{ "characteristic": "perpendicularity", "value": 0.02, "unit": "mm", "datumRefs": ["B"], "confidence": 0.95 }
],
"threads": [
{ "designation": "M12x1.75-6H", "type": "metric", "depth": 20.0, "unit": "mm", "confidence": 0.98 }
],
"surfaceFinish": [
{ "parameter": "Ra", "value": 1.6, "unit": "µm", "area": "bore H1", "confidence": 0.94 }
]
}This is what sets Werk24 apart from generic OCR — structured, typed, confidence‑scored data ready for downstream pricing, feasibility, and ERP.
Your formula reflects how you build parts
Two shops can manufacture the same part with different setups, machines, and QA routines. A generic cost model ignores those differences. Your formula — or Saphirion's model — weights the drivers that matter in your environment (setup vs. cycle time vs. tooling vs. QA), so price reflects how you actually build parts.
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.
- 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.
How it works
Three phases. You provide the data, we build the formula, then production keeps it honest.
What you give us You
~100 representative drawings (PDF or scan) and matching historical prices or actual production costs. Use stable, comparable SKUs — avoid bundled or one‑off specials.
What we do Werk24 + Saphirion
Extract every dimension, tolerance, GD&T frame, material, and finish from your drawings → Fit an interpretable pricing formula with error bands and driver importance → Flag outliers, missing drivers, and extraction anomalies → Iterate with your cost engineers until KPIs are met.
After go‑live
Feed actual production costs after each job close. The model self‑calibrates and improves every subsequent quote — automatically, through your existing tools.
Outcomes
Fewer gut‑feel guesses, tighter margins, full traceability.
Automated quoting for standard parts
Generate quotes in seconds for parts that match your trained families. Estimators focus on exceptions and complex geometry — not routine work.
Self‑calibrating prices
Every shipped job feeds measured cost back into the model. The quote‑to‑actual gap shrinks automatically over time.
Traceable rationale for every price
Every quote is explainable: driver weights, confidence scores, and a full audit trail. No more "the senior estimator just knows."
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.