[ Drawing and Spend Intelligence ]

VectorLedger
Make your drawings searchable and your part prices comparable

VectorLedger joins two datasets manufacturers already own: the engineering drawing archive and the ERP purchase ledger. It resolves the same part across drawing, item master and invoice, groups similar parts into families, normalises commercial scope, and produces price-variance findings that can be traced to the exact drawing region and purchase-order line.

11 Dimensions
Commercial scope normalised per finding
5 Stages
Variance to realised saving, never merged
3 Clicks
From any figure to drawing or PO row
Deterministic
Integer-paise arithmetic, no guessed numbers

What VectorLedger Solves

Two pre-existing datasets, one decisive join

Drawings sit in scanned PDFs, PLM and network drives where nobody can search them. The ERP ledger is searchable but blind to what a part actually is. The same part carries different identifiers across drawing, item master, invoice and second plant, so near-identical parts bought at different prices can go unnoticed.

The archive is the productValue comes from reading your own drawings and ledger, not from a generic benchmark database.
Provenance or silenceA number without a source is not shown. Every figure links to a drawing region or purchase-order row.
Refusal over guessworkWhere evidence is thin the finding is withheld with a stated reason, not inflated.
VectorLedger isometric cube logo

Capability Flow

From scanned drawing to evidenced finding

Six stages convert unreadable archives and raw ledgers into findings a sourcing professional can defend.

Read the archive

Scan clean-up, deskew, OCR and title-block extraction using templates learned from about twenty labelled examples.

Normalise purchases

PO and GRN lines, vendor identity and price series. Every excluded line keeps a reason.

Resolve identity

A seven-strategy cascade matches drawing, part and PO, biased against false merges.

Cluster families

Text, visual, dimensional and specification similarity group parts into families.

Normalise scope

Eleven commercial dimensions, each marked known, inferred or unknown, with provenance.

Compute findings

Variance, an adjustment ledger, addressable exposure, a confidence grade and mandatory limitations.

Pipeline stages as implemented in the VectorLedger engine.

Value Chain

Five stages that are never summed or conflated

VectorLedger claims identification only. Each stage after that is owned by a named person.

Observed variance

The raw price difference across vendors for the same or equivalent part.

Addressable exposure

Observed variance minus an evidenced adjustment ledger for scope differences.

Validated opportunity

A sourcing professional agrees the finding is real.

Negotiated saving

A named owner agrees a new price with the vendor.

Realised saving

Confirmed by the customer's finance team after the price takes effect.

Product Map

From the opportunity board to the source document

Every screen exists to make a number inspectable.

Opportunity BoardFilter findings by stage, confidence, minimum exposure and part, cluster or kind
Finding detailItemised deduction ledger with limitations and actionability
Basis panelClick any figure to see exactly how it was derived
Drawing terminalMulti-sheet viewer with bounding-box anchors on the source drawing
ERP row viewThe exact purchase-order line behind a price
Withheld findingsWhat was not shown and why
Annotations and saved viewsTeam notes and reusable filters
ExportsMarkdown or HTML variance reports with a digest and withheld count
AdminUsers, invites, runs and audit viewer

Capabilities

What VectorLedger does for sourcing and engineering

Each capability is a module in the engine.

ARCHIVEOCR

Searchable drawings

Ingests, scans for malware, hashes, deduplicates and renders originals, then reads title blocks with calibrated confidence and a per-customer template.

  • Includes: Works on scanned PDFs, not only clean CAD exports.
  • Includes: Extraction confidence is reported, not hidden.
IDENTITYMATCHING

One part, one identity

Resolves the same part across drawing numbers, ERP codes, invoice text and second-plant IDs, with a bias against false merges.

  • Includes: Variants such as AX-00123-R4, 00000000123 and AX123 are designed to resolve together.
  • Includes: Unresolved identities stay visible instead of being forced.
SPENDPRICE SERIES

Comparable vendor prices

Imports PO and GRN lines, resolves vendors and builds price series, keeping a reason for every excluded line.

  • Includes: Same-part multi-vendor and single-source findings.
  • Includes: Unexplained price increases and equivalent price bands.
SIMILARITYFAMILIES

Find equivalent parts

Combines text, visual, dimensional and specification similarity to cluster near-identical parts into families.

  • Includes: Surface duplicates that carry different part numbers.
  • Includes: Cluster explorer is on the product roadmap.
SCOPENORMALISATION

Compare like with like

Eleven dimensions: material supply, treatment, freight, packaging, tooling, inspection, certification, payment terms, MOQ, lead time and programme context.

  • Includes: Each dimension is known, inferred or unknown.
  • Includes: Adjustments are itemised in a ledger.
FINDINGSCONFIDENCE

Findings with confidence grades

Each finding carries a confidence grade and mandatory limitations. Findings below the evidence threshold are withheld.

  • Includes: Opportunity board has no sort by raw variance on purpose.
  • Includes: Annotations, saved views and exports for the team.

Trust and Governance

Principles built into the product

The product principles are enforced in the arithmetic and the interface.

Deterministic arithmetic

Money is computed with integer paise arithmetic. Models help read drawings and find similarity, not produce prices.

Provenance everywhere

Every number resolves to a source drawing region or an ERP row within three clicks.

Human owns the claim

The product identifies exposure. Validation, negotiation and realisation belong to named people.

No cross-tenant pooling

A customer's drawings and prices are never pooled with another customer's data.

Who Uses It

Built for sourcing, engineering and finance together

Designed for machinery manufacturers with large drawing archives and bought-out spend.

💼

Sourcing managers and CPOs

Review vendor and part findings with evidence and scope adjustments.

⚙

Design and cost engineers

Find existing parts and tooling before releasing a new design.

🗃

Data stewards

Resolve unmatched identities and maintain title-block templates.

💰

Finance

Confirm realised savings against agreed prices.

Frequently Asked Questions

VectorLedger: common questions

What does VectorLedger do?

VectorLedger makes engineering drawings searchable and part prices comparable. It joins the drawing archive to the ERP purchase ledger and produces auditable price-variance findings adjusted for commercial scope.

Does VectorLedger use AI?

VectorLedger uses OCR and similarity models to read drawings and group parts. All money arithmetic is deterministic, and findings without enough evidence are withheld instead of estimated.

Which ERP systems does it work with?

VectorLedger starts from purchase-ledger and PO/GRN exports such as CSV from SAP, Oracle, Infor or Tally. Live ERP connectors are on the roadmap.

Does VectorLedger promise savings?

No. It identifies addressable exposure and shows its working. Validation, negotiation and realisation are tracked as separate stages owned by named people.

How does an engagement start?

A time-boxed Spend Discovery pilot runs the pipeline on a customer's own archive and ledger to test whether defensible exposure exists.

Talk To Mativyx

Find out what your drawings and ledger already know

Start with a Spend Discovery pilot on your own drawing archive and purchase ledger.

Request Spend Discovery