ShadowsTechnologies
Sector experience

Different industries, the same physics.

Our team has spent 10+ years modelling, building and repairing high-performing applications across a wide range of industries. The domain language changes; the statistics and the discipline don't.

Finance

modelled and measured order paths

Logistics

solvers and telemetry at scale

Manufacturing

robotics and deep learning vision

Accounting

automated ledgers and matching

Constraints

What each sector demands.

Budget · trade-critical

Financial sector

A slow tick is a lost trade and a regulator question.

  • Order entry gateways and pre-trade risk checks
  • Market-data decoding and normalisation
  • Tick storage and replay for research
6.4×gateway throughput after rework
Budget · seconds, at scale

Logistics

Route quality degrades the moment the solver misses its window.

  • Dispatch and routing solvers under time pressure
  • Vehicle telemetry ingest and ETA engines
  • Warehouse control-plane throughput
7.8×dispatch solver throughput gain
Budget · line rate, no exceptions

Manufacturing

A model that hesitates stops the line; a model that guesses ships defects.

  • Robotic processing and pick-and-place control software
  • Deep learning vision for defect detection and classification
  • Model training, drift monitoring and on-line inference at line rate
99.4%defect recall at full line rate
Budget · month-end close

Accounting

A ledger that doesn't balance at midnight is a decision made on the wrong number.

  • Automated accounting systems that classify ledgers and extract document information
  • Payment matching and counterparty resolution at scale
  • Statistical reconciliation and anomaly detection in close processes
94%of document-to-ledger matching automated
Accounting as a statistical problem

Month-end close, solved in code.

We treat accounting not as bookkeeping but as a sequence of inference problems: what is this document, which ledger does it belong to, who is the counterparty, and does the payment match? For a recent large-scale engagement we built an automated accounting platform that answers those questions in code, from document ingestion to a balanced close.

accounting pipelineautomated
Ingest documents
reduce
Classify ledgers
reduce
Match payments
reduce
Close with confidence
  1. 01
    Ingest documentsOCR + structure extraction

    Bank statements, invoices and receipts are read into structured candidates: amounts, dates, identifiers and counterparties.

  2. 02
    Classify ledgersmulticlass classifier

    Each document is routed to the correct ledger and cost centre using features from text, amounts and historical patterns.

  3. 03
    Match paymentsbipartite matching + record linkage

    Open items are paired to bank lines by probability, not just exact reference, so near-misses resolve automatically.

  4. 04
    Close with confidencereconciliation + anomaly test

    Residuals are tested statistically; anything that fails the threshold is surfaced for a human, not buried in a spreadsheet.

98.2%
Field extraction
91%
Auto-matched payments
4×
Close time reduction

Your sector isn't listed?

Performance work transfers. Tell us the constraint and we'll tell you whether we've solved its shape before.

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