An AI-driven pipeline that writes tech packs.
Brief → AI extraction → tech-pack assets · co-authored with Claude Code
A footwear tech pack is the spec a factory builds from — bills of materials, points of measure, construction, grading. This pipeline reads what a designer already makes (sketches, 3D renders, written notes) and auto-populates those documents, applying the brand's own material, trim, and fit rules, then validates the result before anything reaches a sample line.
The pipeline, running.
This is content supply chain fluency made literal — a brief turned into the spec a manufacturer can actually build from.
Sole builder, brief to working pipeline.
I designed and built every layer: the multimodal ingestion, the logic that populates each spec section, the brand-standards layer, and the validation rules — composed through an AI build interface, with the judgment to know when its output was wrong and the error handling was missing.
Tech packs are the blueprint. Making them by hand is the bottleneck.
Every garment needs a detailed spec before it can be made, and producing those by hand is slow and error-prone — it gates the seasonal calendar. Worse, an error caught late doesn't just cost an edit; it costs another physical sample round, measured in weeks and dollars.
Turned a designer's inputs into a populated, checked spec.
Reads what designers already make
Sketches, 3D renders, and written notes go in as-is. No new input format, no extra step bolted onto the team.
BOM, POM, construction, grading
The pipeline fills the load-bearing spec sections from those inputs, instead of a person re-typing them by hand.
The brand's rules, baked in
Materials, trims, and fit rules follow the brand's own taxonomy, so the output reads on-brand, not generic.
Catches errors before sampling
Missing measurements, incompatible fabrics, and regulatory gaps are flagged in real time — at the desk, not at the factory.
From a designer's inputs to a factory-ready spec.
The pipeline moves left to right: a designer's existing work goes in, the model reads it, the spec sections are populated against the brand's standards, and a validation pass gates the output before it ships downstream.
The validation pass is the load-bearing step. It catches the errors that would otherwise surface at the sample line, where each one costs a round — the same place the savings actually live.
Built to sit inside a real content supply chain.
The pipeline runs standalone today. The design goal is for it to live where the work already happens, rather than as a separate tool the team has to remember to open:
- Plug into existing PLM and 3D tools instead of replacing them
- Configurable to each brand's taxonomy for materials, trims, and fit
- Scales with product volume across a season's worth of styles
- Shared surface for design, tech design, and sourcing to work from one source of truth
Proof at the spec level, not a speedup number.
These are what the pipeline does, not a percentage it saves — the savings live downstream, in the sample rounds it prevents.