Foundation · Build
Make your brand data legible to agents.
PLM, ERP, PIM, DAM and the range plan - turned into something an agent can actually read.
A fashion context layer is the retrieval and tool infrastructure that makes a brand's existing systems - PLM, ERP, PIM, DAM, range plans - readable by AI agents. It is the layer that determines whether agent pilots scale or stall.
Not advice. Working parts.
- 01System inventory and schema mapping
- 02Retrieval layer over PLM, ERP, PIM and DAM
- 03MCP tool definitions per system
- 04Brand vocabulary and taxonomy normalisation
- 05Evidence logging and provenance
- 06Access control and governance boundaries
The agent isn't the hard part. Your product data lives in six systems, three spreadsheet conventions and one person's head. Until an agent can read it, every pilot is a demo.
Artifacts, not a deck.
Schema map across every connected system
Working retrieval layer with MCP tool definitions
Normalised brand taxonomy and vocabulary
Governance and access-control boundary document
What has to be read, and what it will not tell you.
PLM
Holds the BOM and the calendar. Rarely holds why a decision was made, which is the part an agent needs.
ERP
Authoritative on cost and stock, hostile to anything that is not a transaction. Attribute data is usually a free-text field.
PIM
The only system that thinks in attributes, and the one most likely to disagree with the other two about what a style is.
DAM
Every asset, almost no structured link back to the SKU it depicts.
The range plan
Usually a spreadsheet, usually the real source of truth, usually owned by one person.
The archive
Physical, or in a drive nobody has indexed. The highest-value context and the least machine-readable.
Nothing published for this one yet.
No published artifact for this capability yet. First engagements are in progress, and what they produce is published here on completion - with the client unnamed unless they ask to be named.
Consulting installs it. The platform runs it.
The context layer is infrastructure inside your systems, not a surface you open. When it is working you never see it: every studio you do open is reading through it.
Build. Twelve weeks and up, in your infrastructure, IP transferred.
The architecture that runs McLeuker AI, constructed inside your own stack.
Diagnose
Where agents change the economics, and where they do not.
Design
Which agents, which tools, which data, which guardrails.
Deploy
Shipped into the operation with your context loaded.
Operate
Retained partnership, monthly sessions, continuous updates.
What buyers ask first.
Do we have to replace our PLM?
No. The context layer sits over the systems you already run. Replacing a PLM to suit an agent is the most expensive possible way to solve a retrieval problem.
What is MCP and why does it appear here?
It is the shared transport layer every major agent camp has converged on. Defining your systems as MCP tools means the retrieval work is not tied to one vendor, one model or one platform - including ours.
How long before an agent can read our data?
The first system is usually readable inside three weeks. Full coverage across PLM, ERP, PIM and DAM depends on how much taxonomy normalisation the catalogue needs, which the diagnostic scopes before this starts.
Who owns what gets built?
You do. The context layer is built inside your infrastructure under the Build door, with full IP transfer at handover.
Rarely bought alone.
Foundation
Agent Orchestration
Single think-act-observe-ship loop, multi-model routing, parallel dispatch, self-verification.
Foundation
Agent Opportunity Diagnostic
Four weeks. A ranked map of the workflows worth automating, and the ones that aren't.
Foundation
LLM-Native Platform Build
For houses that will not put brand data on anyone's platform. Built inside your infrastructure.
Start with the diagnostic.
Four weeks, fixed fee, and a ranked map of what is worth automating in your operation - including what is not.