Hours of daily data entry
Invoices and POs re-typed into spreadsheets and accounting — slow, mind-numbing, and the single largest use of back-office time.
Case StudyTrading & Logistics
A UAE Trading Company: Teaching Software to Read the Paperwork
A UAE trading and distribution company moved thousands of documents a month — supplier invoices, purchase orders, packing lists, shipping papers — through human hands and keyboards. We built its document AI pipeline and the order-and-inventory system behind it, shifting staff from typing documents to reviewing exceptions.
At a Glance
- Client
- Trading & distribution — UAE
- Industry
- Trading & Logistics
- Engagement
- Document AI + operations system
- Volume
- Thousands of documents monthly
- Core target
- Typing replaced by review
Background
The Context That Made This Worth Building
Trading businesses run on documents the way engines run on fuel — and this company burned staff-hours processing them: reading supplier invoices, keying them into spreadsheets, matching packing lists to purchase orders, reconciling landed costs per shipment. The work was essential, error-prone, and endless; the people doing it were too capable to be spending their days as human OCR.
The Challenge
The Pain Points That Started It
Paper velocity had outgrown human processing — with familiar consequences.
Errors surfacing weeks later
A transposed quantity or missed line item discovered at reconciliation — after decisions had already been made on wrong numbers.
Stock and margin known approximately
Multi-warehouse inventory in drift, landed costs computed occasionally — pricing decisions made on estimates.
Discovery
Understanding Before Building
We collected a real month of documents — every supplier’s invoice format, every courier’s paperwork — and measured the flow: volumes, formats, where each document’s data had to land, and which fields caused errors. The corpus became both the training reality and the honest benchmark: extraction had to prove itself against these documents, not demo samples.
Process mapping showed the deeper issue: extraction fed spreadsheets because there was no system of record to feed. The scope grew deliberately — document AI plus the order-and-inventory core that gives extracted data somewhere structured to live.
The Solution
What We Designed and Built
A pipeline that reads, a system that remembers, and dashboards that finally tell the truth.
Document AI pipeline
Invoices, POs, packing lists, and shipping documents extracted, validated against orders, and filed automatically — with confidence thresholds routing uncertain fields to human review.
Order & inventory core
Purchase and sales orders, multi-warehouse stock, and landed-cost tracking per shipment — the structured home the data never had.
Margin & operations dashboards
Live stock positions, shipment status, and landed-cost-aware margin views — pricing decisions on numbers, not instinct.
Scope Delivered
Everything That Shipped
- Multi-format document extraction pipeline
- Confidence-based human review queue
- Automatic PO / invoice / packing-list matching
- Multi-warehouse inventory system
- Landed-cost tracking per shipment
- Margin & stock dashboards
- Accounting system integration
Implementation
How the Build Ran
- 1
Corpus & benchmarkWeeks 1–2
A real month of documents assembled; accuracy targets per field agreed as the pipeline’s pass/fail bar.
- 2
Extraction pipelineWeeks 3–6
Extraction, validation, and the review queue built and iterated against the corpus until the benchmark held.
- 3
Operations coreWeeks 5–9
Orders, inventory, and landed costs — extracted data flowing into structure instead of spreadsheets.
- 4
Parallel run & cutoverWeeks 9–12
Old and new processes run side by side until trust was earned in numbers; then the typing stopped.
Stack
- Python
- OpenAI
- Node.js
- PostgreSQL
- Next.js
Results & ROI
What It Delivered
~90%Documents processed without manual entry
HoursDaily staff time returned
LiveStock & margin visibility
~12 wksCorpus to cutover
- Staff shifted from typing every document to reviewing the uncertain minority
- Errors caught at ingestion by validation rules, not weeks later at reconciliation
- One live stock truth across warehouses ended the oversell-here, dead-stock-there pattern
- Margin per shipment became a number consulted before pricing, not after
The transformation wasn’t exotic AI — it was the disciplined pairing of extraction with somewhere structured for the data to go. The parallel-run phase mattered as much as the model: trust was earned by weeks of matching numbers, and by a review queue that made the system’s uncertainty honest. The company now scales volume without scaling keyboards.
Takeaways
What This Project Teaches
Benchmark on your own ugly documents
Real supplier formats — stamps, scans, handwriting — are the test that matters. Demo accuracy is marketing.
Extraction needs a destination
Document AI feeding spreadsheets just automates chaos. The inventory core was half the value.
Parallel runs buy the trust cutover needs
Weeks of side-by-side matching converted sceptics better than any demo could.
Before You Ask
What Readers Usually Want to Know
How accurate is document extraction, honestly?
Accurate enough that staff move from typing to reviewing exceptions — but not perfect, and we do not design as though it is. Every field carries a confidence level, anything below the threshold goes to a human review queue, and validation rules check extracted data against the matching order. The honest framing is that AI does the first pass instantly and flags what it is unsure about.
Do we have to replace our ERP or accounting system?
No. In this project the extraction pipeline and operations core were built to feed the existing systems rather than displace them. ERPs are usually competent at accounting and weak at the shop-floor and document layers — building there and integrating cleanly is both cheaper and far less disruptive than a migration.
How do you get a sceptical team to trust an automated pipeline?
By running it in parallel until the numbers match. For several weeks the old process and the new one ran side by side, and the team compared results themselves. That period does more for adoption than any demonstration, because trust in a system that touches money has to be earned with evidence rather than promised.
The Services Behind This Build
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