How McLeuker AI works
A purpose-built agentic architecture that transforms natural language questions into structured, professional intelligence - in minutes.
One brief, carried through
You bring a direction. It comes back as the whole week's work.
What we do
Know it. Create it. Produce it.
Three things, in the order a season actually happens. Each one is an outcome you want and a studio you open to get it - the same structure from two sides, which is why the two menus in the header carry the same three headings.
Know
Ask a question, get a cited and editable document back.
The outcomes
The studios
Create
Write a brief, get finished creative - imagery, boards, books, film.
The outcomes
The studios
Produce
Hand over the work, get the specs and operations that ship it.
The outcomes
The studios
Brief to deliverable
Four stops, about three minutes.
- You write a line
A theme, a season, a material, a city. No reference boards.
- It researches live
Runways, trade press and live signals, every claim cited.
- It builds the work
Looks, boards, decks, spec sheets, each shipped as it finishes.
- You download it
PDF, XLSX, PPTX, DOCX, MP4, packaged in one ZIP.
The 5-Layer Pipeline
Task Interpretation
Understanding your intent
Your natural language query is parsed, classified by domain, and decomposed into structured research sub-tasks.
"Analyze SS26 womenswear trends from Milan" → Domain: Fashion, Task: Trend Analysis, Scope: Milan FW SS26
LLM Layer
Multi-model intelligence
Each sub-task is routed to the most capable model - one optimized for deep synthesis, another for real-time data retrieval, a third for rapid factual lookups, and a fourth for long-document comprehension.
Trend synthesis → Deep reasoning model | Live signals → Real-time model | Quick facts → Speed-optimized model
Real-Time Web Research
Live intelligence gathering
AI agents search the web in real-time, pulling data from fashion weeks, trade publications, social media, and industry databases.
47 runway shows analyzed, 2,300 social posts scanned, 15 trade reports cross-referenced
Logic & Structuring
Making sense of data
Raw intelligence is validated, cross-referenced, and organized into structured formats - tables, charts, comparisons, and narratives.
Trend heatmap generated, supplier matrix built, competitive landscape mapped
Execution Layer
Professional deliverables
Final outputs are generated as professional documents - PDF reports, Excel spreadsheets, PowerPoint decks, and Word documents.
12-page PDF with executive summary, charts, and source citations
What makes it different
Multi-Model AI
Best model selected per task - not one-size-fits-all
Real-Time Data
Live web research, not stale training data
Structured Outputs
Tables, charts, and comparisons - not just text
Professional Exports
PDF, Excel, PPTX, Word - ready to share
Source Transparency
Every claim cited, every source traceable
Minutes, Not Weeks
Research that took days, delivered in minutes
From question to deliverable
“Analyze SS26 womenswear trends from Milan and Paris Fashion Weeks”
“Find GOTS-certified denim suppliers in Europe with MOQ under 500”
“Size the European sustainable fashion market for DTC brands”
“Assess our brand's readiness for CSRD reporting”
Our methodology
Domain-Specific Intelligence
McLeuker AI combines large language models with curated fashion industry data sources. Our systems understand domain-specific terminology, frameworks, and context - from Pantone references to certification standards.
Continuous Validation
Every output is validated against primary sources and industry benchmarks. When data is uncertain or unavailable, we clearly indicate limitations. Source citations are included in every report.