SourceOptima reads your engineering drawings and purchase history, classifies every part by manufacturing profile, then algorithmically identifies negotiation leverage, sole-source risks, consolidation wins, and quick-switch opportunities - ranked by dollar impact.
The result: redundant suppliers, unexploited volume leverage, and no way to answer “who in our supply base can actually make this?”
A 5-axis titanium component bought in Oregon at $420. A near-identical part bought in Czechia at $1,260. Different suppliers. No one knows.
Self-reported capability sheets say one thing. Delivery records tell another. Suppliers who broker work they can't fulfill in-house are a quality and IP risk.
M&A leaves you with PLM systems that don't talk, part numbers that overlap, and category lines drawn by who acquired whom - not by what gets made.
Purchase history tells you what you bought. Drawings tell you what you actually needed. The combination is where the leverage lives.
In one recent engagement, 545 parts and $21.4M of spend went in - and 80 ranked savings opportunities came out. Here's the shape of what happens in between.
30 minutes. We'll show it running on data like yours - or on a sample of your own.
Every view is driven by AI-extracted engineering data crossed with your purchase history. Here's what each reveals.
Savings ranked by dollar impact. Each opportunity includes the data pack you need for the supplier conversation.
Your entire supply base as an interactive graph, built to navigate 50,000-part portfolios. Fragmentation, concentration, and dependency - visible at a glance.
Parts grouped by what they actually need - process, material, tolerance, size. Manufacturing taxonomy replaces org-chart categories.
Capability profiles built from what each vendor has actually delivered - not what their brochure claims.
Most savings tools compare list prices. SourceOptima builds every target from the engineering of the part and the prices you have actually paid - and refuses to show a number it can't defend.
Every part is classified by manufacturing method, material, size, tolerance band, and complexity - the five attributes that drive cost. Parts that could run on the same machines land in the same family.
Within each family, a regression model relates price to volume, complexity, tightest tolerance, setup count, and material utilization - trained on what your suppliers actually charged, not textbook cost tables.
A target is never more aggressive than the best price a comparable part has actually achieved. Families with too few members - or too wide a price spread - are excluded from negotiation-grade benchmarks.
Every opportunity carries an achievability score and the evidence behind it - and generates a negotiation brief or supplier-facing RFQ package on demand, ready for the conversation.
Considering leaving a vendor? The engine redistributes every part to evidence-ranked alternatives - same-part transactions first, then family peers - nets out qualification costs, and states the payback period. Sole-sourced parts are flagged, not hidden.
Family consolidation savings are assembled from four named components - price alignment, volume-tier leverage, administrative reduction, and tooling amortization - each one defensible line by line in the business case.
From process clusters to part families to individual parts: semantic zoom built for 50,000-part portfolios. Fragmentation and concentration are visible at every level, not buried in a hairball.
Five steps. No IT project. No organizational change. The output is the intelligence layer above - running on your data.
Drop in your drawing archive as-is - ZIP, RAR, nested folders. We handle 100K+ files without IT involvement.
AI reads each drawing - process, material, tolerance, geometry, GD&T, threads - into structured engineering data.
Every part lands on a manufacturing taxonomy: process → complexity → size → material → tolerance band.
Structured profiles matched against purchase history. Price benchmarks, sole-source flags, and consolidation candidates surface automatically.
An interactive procurement intelligence layer plus per-stakeholder workbooks ranked by dollar impact.
Anonymized - global manufacturer, mechanical / machined engineered components category.
Multi-hundred-GB drawing repository · tens of thousands of part folders. No human reviewed any of it. The platform did.
Cross-referenced against ERP line items. ~50% matched on first pass; coverage grows as namespace gaps close.
An entire site's engineering drawings had zero procurement records anywhere in the company. Hundreds of unique part numbers, invisible without cross-reference.
Hundreds of missing items for the drawings team to locate. Dozens of orphan parts for the procurement SME to add. One workbook per person, ranked by impact.
The first round of value comes from data your team already exports every month - no IT project, no organizational restructuring, no new suppliers to qualify. ERP, PLM, and supplier-system integrations are a path to deeper value once the platform is producing, but they're never a prerequisite to getting started.
Where integrations unlock more value (later, on your timeline):
What made it possible: AI-powered drawing extraction at scale. No human reviewed 31,000 folders - the platform did. The subject-matter experts only touched the exceptions.- Reference engagement, Phase 1 retrospective
Supply chain intelligence is the foundation. Next: automated RFQ generation and a vendor-facing collaboration portal.
Savings identification, network visualization, manufacturing taxonomy, supplier profiling. Live now with global manufacturers.
AI-generated RFQ packages with full engineering context, supplier bid leveling, and an agentic negotiation assistant - built on the same intelligence layer.
Supplier-facing collaboration layer. Quote responses, capacity signaling, and delivery tracking - integrated back into your intelligence layer.