AI Agents · TREND-TO-DESIGN · Live

Trend-to-Design Agent.

Trend brief → manufacturable design package — image, BOM, costing, line sheets — without humans in the middle of the cycle.

Live BUY L4 Powered by → IntelliLoom

Trend business run

718 designs

10 brands · ~2 days · autonomous

Selection rate

~60%

design team retained for range

Pipeline depth

8 steps

Brief → Context → Artwork → Garment → Review → Costing → Variation → Deliver

Category coverage

43 pods

F&L operating model · mens / womens / kids / footwear / premium

01 · The job to be done

The pain

Manual design scaffolding takes weeks. Trend signals from WGSN, Pinterest, retail crawlers arrive unstitched to brand DNA. Tech-pack handoff to vendors loses fidelity — colour, print placement, BOM drift between spec and stitch.

What the agent does

Ingests trend signals + brand context + season constraints. Generates artwork variants, then full garment images (front + back, on-model, 2K). VLM reviewer scores 30+ quality checks per garment with auto-retry on failure. Calculates BOM cost per piece. Produces silhouette and colour variations from reference images. Delivers share-ready line sheets to vendors and buyers.

What humans still do

Buyer reviews finished design packages — not briefs. Designer takes selected concepts into pattern-making and final tech-pack approval. Agent does the production-ready scaffolding; humans select what to range.

02 · How it works · the closed loop

Inputs (blue) → 8-step autonomous pipeline (grey) with VLM quality gate (yellow, auto-retry on fail) → vendor-ready package (green).

Brief ingestion

Brand DNA, season direction, category specs, moodboard palette, target price band — pulled from buyer brief and the upstream Category Intelligence report.

Context agent

Extracts brand palette, silhouette direction, print techniques and fabric preferences from the brief and reference set.

Artwork generation

0-3 artwork images per garment with smart print detection. Solid garments skip artwork; printed garments get placement or AOP (all-over print) variants.

Garment image

Full garment image (front + back, on-model, 2K resolution). Zone-aware artwork placement, gender-correct model, age-appropriate styling.

Quality review · VLM

VLM reviewer scores 30+ quality checks per garment — colour accuracy, print fidelity, silhouette, category compliance. Auto-retry to step 03 on failure; ship only when checks pass.

BOM costing

Cost per piece calculated via Costing Engine API — fabric, trims, SMV (standard minute value), overheads.

Variation engine

Generates N silhouette and colour variations from reference images using the moodboard palette. Used for range building and style explosions.

Delivery

Images uploaded to cloud storage via presigned URLs. Per-style webhook callbacks fire to downstream systems (PLM, vendor portals) in real time.

03 · Underlying data

Data source	Classification	Key entities
Trend signals (WGSN, EDITED, Pinterest, retail crawlers)	Mixed	Trend velocity · colour confidence · silhouette signals
Brand DNA brief	Human-Created	Palette · construction guardrails · target consumer
Manufacturability library	Machine-Readable	Approved bases · fabrics · trims · lead-time lanes
Reference images	Mixed	Moodboard · prior-season bestsellers
Costing Engine API	Machine-Created	Fabric cost · trims · SMV · landed cost

Refresh cadence: Per-brief run · trend signals fresh-pulled. Manufacturability library refreshed seasonally.

KPIs moved:

Time-to-design (weeks → days)Selection rate (% agent designs taken to range)Tech-pack handoff fidelityRange coverage per category pod (43-pod model)

04 · Design patterns used

Multi-Agent Orchestration

8-step pipeline coordinates Context, Artwork, Garment, Review, Costing, Variation as specialist agents

Wide Research

Variation engine generates many silhouette × colour candidates before curation narrows to micro-collections

Tool Calling

Image gen APIs (GPT Image 2, Vertex), VLM reviewer, Costing Engine, cloud storage, webhooks — all tool-mediated

Evals

VLM reviewer's 30+ checks per garment is a per-output eval suite with auto-retry on fail

Observability

Per-style webhook trace · BOM-to-image-to-spec lineage · regen attempts logged

05 · Evals · how we know it works

Two layers. Layer 1 — VLM reviewer scores every generated garment against 30+ checks (colour, print, silhouette, category) and auto- retries on failure. Layer 2 — buying team selection rate: % of agent designs taken into the range is the real-world signal. Trend business runs hit ~60% selection.

Gates and thresholds.

VLM 30+ checks pass before image ships
Selection rate stays above 50% per brand cohort
Cost-per-piece estimate within 10% of vendor RFQ on commercial sample