Textile & Apparel AI
The data already exists in your operation. We build the system that turns it into an asset.
Custom-built knowledge systems, fabric intelligence platforms, and production management tools for mills, manufacturers, and brands. Your data, your infrastructure, your competitive edge.
Mill
Fabric recipe traceability and batch knowledge that survives staff turnover.
Manufacturer
Production audit trail that resolves chargebacks in minutes, not weeks.
Brand
Wash and test data that reaches designers before the next season's order is placed.
The problem
The data exists. It just lives in the wrong place.
Every mill, manufacturer, and brand in the textile supply chain is sitting on data that should be making their operation faster, cheaper, and more consistent. Test reports. Production process records. Tech packs. Wash test results. Chargeback histories. It exists. The problem is that almost none of it is structured in a way that anyone can actually use.
The Mill
Fabric recipes tracked in incomplete Excel sheets. No system to record machine settings, passover counts, pressure, or chemical finishers. Cannot explain why a batch that passed last season is failing this year.
The cost
Chargebacks from the manufacturer, lost repeat business, and IP walking out the door with experienced staff.
The Manufacturer
Receives tech packs in poor condition from brands with no clear fabric specification. Goes into bulk production and discovers problems that were visible in the test data but nobody had a system to surface them.
The cost
Chargebacks from the brand, rework costs, delayed schedules, and absorbing the cost of data that should have been visible before bulk started.
The Brand
Wash test results, fabric test reports, and chargeback data never reach the designers and product developers who could use them. Decisions about next season are made without data from this season's failures.
The cost
Size-related returns, colourway inconsistency, failed durability tests, and growing regulatory exposure as digital product passports become mandatory.
What we build
Three systems. All connected. All owned by you.
Three software products, one for each layer of the data problem. They deploy independently or as an integrated suite depending on which buyer is at the table.
Fabric Knowledge Base
for millsA structured system for capturing and querying the complete recipe of how a fabric is made. Process parameters, dyeing and finisher formulations, mechanical settings, all stage test results, and full batch traceability. The system enforces completeness: a record cannot be saved with required fields missing, ending the gaps that make season-to-season diagnosis impossible.
Production Management System
for manufacturersA workflow and document platform replacing the manual handling of tech packs, test reports, and production records. It flags missing or inconsistent specs before production, cross-references fabric test data against brand requirements, tracks the production record at each stage, and generates an audit trail to support or dispute chargebacks.
Fabric Intelligence Layer
for brandsAn AI analysis layer above the data from mills and manufacturers. It explains test and wash results in plain language, flags fabric combinations that historically led to returns or chargebacks, supports digital product passport compliance, and advises on sourcing risk from historical performance before an order is placed.
Data Sovereignty
Fabric recipes are mill IP. Production records are manufacturer IP. Systems run on infrastructure the client controls. Nothing is processed through shared third-party model infrastructure or used outside the client's own operation.
What changes
Measurable outcomes at each level of the supply chain.
How the programme works
Start where the pain is. Build from there.
Every engagement starts with one buyer and one system. The fastest path to a working product is to focus on the highest-pain workflow first, deliver something that runs in production, and expand from a reference that is working.
Phase 1
Discovery
Weeks 1 to 4
Map the existing data structure. Audit what lives in Excel, in PDFs, and only in people's heads. Define what the system must capture and output. Agree data governance — what stays on the client's infrastructure and what, if anything, is shared across the chain.
Phase 2
Build
Weeks 5 to 10
Build the knowledge base or production management system to the agreed specification. Configure the data model to the client's actual fabric types, processes, and test regimes. Integrate with existing ERP or QMS. Set up the validation rules that enforce completeness.
Phase 3
Pilot
Weeks 11 to 14
Run live on one product line, fabric type, or supplier relationship. Train the team. Watch it operate in real production conditions. Tune the data model and rules based on what the team actually encounters.
Phase 4
Scale and handover
Weeks 15 to 20
Roll out to the full operation. Hand over with documentation, training, and a period of supported operation. Optional managed-service tail for teams building confidence or wanting Ravon to continue developing the analysis layer.
What is included. What is not.
How we work
Diagnosis before prescription.
Every engagement follows three phases — discovery and diagnostic, impact & scoping, and solution design with explicit checkpoints. See how we reduce delivery risk before you commit scope.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context. We will identify the highest leverage intervention areas before any scope is agreed.