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Industries Textiles and apparel

Textiles and apparel

Right first time, from the order to the shipment.

Garment makers and trims suppliers work to thin margins, short lead times and rules set by every retailer and country. We turn scattered orders, specifications and capacity into one reliable picture, and automate the checks that slow your people down.

Rows of sewing stations on a garment factory floor
The industry

Where the data lives.

A single garment order touches the retailer, the factory, the trims and packaging suppliers, the mill and the courier. Each keeps its own records, in its own format, on its own timetable.

The data needed to get it right usually exists. It sits in inboxes, spreadsheets, label manuals and the heads of experienced staff. That is where errors, reprints and late shipments start.

Who we work with Factory owners, merchandisers, customer service teams, planners and quality managers.

The data challenge

Where data gets in the way.

Orders arrive in every format.

Portal, EDI, email, spreadsheet and PDF, much of it keyed in by hand, each line a chance to get something wrong.

Every retailer has its own rulebook.

Label manuals, price files and artwork versions change by customer and season, and mostly live in people's heads.

Capacity is spread across sites.

Work goes where it usually goes rather than where there is room, and nobody sees the crunch a week ahead.

Buyers want to see their order.

Status is chased by email and phone because production, dispatch and courier data sit in different systems.

How Jambo Labs helps

What we build.

01

Order intake that reads anything

AI reads emails, spreadsheets, PDFs and scans into clean order lines, and shows where every value came from.

document AI · EDI · portal integration

02

Checks that never skip a step

Retailer rules, labelling law and price files applied to every order before it prints, with the reason for every catch.

rules engine · barcode and price checks · audit trail

03

Planning across sites

Capacity, materials and courier times in one view, so each order goes where it lands on time.

capacity planning · forecasting · early warnings

04

One picture for buyers and managers

Order status, on-time performance, quality and sustainability claims in one place, built on the systems you already run.

data platform · dashboards · buyer portal

Interactive demo, 1 of 2

From inbox to the month, in about two minutes.

A care label order arrives by email. Watch the system read it, catch three errors against the law and the retailer's rules, draft the reply, and show the data behind it all.

A contained demo on invented data. Nothing you do here leaves the page.

Techniques in this demoDocument AIStep 2, Read: the email and the spreadsheet become order lines, each pointing to its source.Entity extractionStep 2: style, sizes, fibres, store countries and barcodes pulled out as fields.Rules engineStep 3, Check: labelling law and retailer rules applied the same way to every order.Generative draftingStep 4, Fix: the reply to the factory, drafted from the catches only.Data pipelinesStep 5, The data: every source collected, read, checked and joined into one record.Natural-language questionsStep 6, Possible: ask the record in plain English, every figure linked to its order.
Next demo: Every order, live for buyers
Interactive demo, 2 of 2

Every order your buyers care about, live.

A buyer opens one link and sees every order. Watch the factory spot a late order 13 days early, pick a fix, answer a question in plain English, and share it all inside its own buyer portal.

A contained demo on invented data. Nothing you do here leaves the page.

Techniques in this demoLive data integrationStep 1, Orders: every order's stage from the factory's own records.Embedded viewsStep 1: the buyer's view inside the factory's own portal, with its branding.OCRStep 2, One order: the handwritten hourly board read from a photo.Predictive forecastingStep 3, Ahead: the ship date at today's pace, 13 days before it is missed.Scenario planningStep 4, Fix: three fixes, each with its effect and its cost.AI agent on governed dataStep 5, Ask: a plain-English question answered with its working.
Also possible

Where the same data goes next.

  • Demand forecasting by retailer and season, so ribbon, board and yarn are bought before orders land.
  • Supply chain planning across sites and suppliers, with early warning of late materials.
  • Supplier and line scorecards from on-time and quality data.
In practice

What our team has done before.

Work our team has delivered across their careers. Clients are anonymised.

Data platforms & engineering
Many ERPs into one

Consolidation of ERP systems for a global consumer goods manufacturer, with reporting rebuilt on top.

Analytics & data science
Supply chain, end to end

Supply, stock and delivery analytics for the same manufacturer, from raw material to customer.

Analytics & data science
Testing, claims and complaints together

Quality dashboards linking product testing, claims and complaints, so causes surface early.

Where to start

Start with two weeks. We replay a quarter of your orders through the checks and show what would have been caught, what it cost you, and what to build first.

The two-week diagnostic
Start a conversation

Tell us the report your team rebuilds every month.

No pitch. We will show you what your own records could already do.

Talk through your use case

Photo: Garment factory in Bangladesh, Tareq Salahuddin, CC BY 2.0.