Foundation · Build
The loop we run, built inside your stack.
Single think-act-observe-ship loop, multi-model routing, parallel dispatch, self-verification.
Agent orchestration is the control layer that decides which model, which tools and which sequence a task requires, then dispatches everything that has no dependency in parallel. McLeuker builds it as a single think-act-observe-ship loop with no phase machines or middleware.
Fashion companies are being told to compete for AI talent outside their own ecosystem. Orchestration is where that talent gap bites hardest and where a wrong architecture costs a year.
Not advice. Working parts.
- 01Routing copilot across providers
- 02Parallel tool dispatch
- 03Self-verification and retry
- 04Evidence logging
- 05Cost and latency observability
- 06Guardrails sized to your governance bar
Artifacts, not a deck.
Working orchestration layer in your infrastructure
Routing policy with per-task selection and failover
Observability dashboard for cost and latency
Guardrail and escalation specification
Consulting installs it. The platform runs it.
This capability has a counterpart on McLeuker AI, the agentic platform the same team built and operates. The engagement is how it arrives inside your operation, loaded with your brand context; the platform is where it runs afterwards.
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.
Why route across several models instead of standardising on one?
Different frontier models are strongest at different jobs, and providers go down. The router selects per task with cost-aware routing and failover, so the operation does not stop when one vendor does. Which models sit behind it is an engineering decision we own and revisit.
What does "no phase machines or middleware" mean in practice?
One loop plans, acts, observes the result and ships. There is no orchestration framework in between deciding what step comes next. Fewer moving parts, fewer places for a task to get stuck, and a trace you can actually read when one does.
Can this run against models we host ourselves?
Yes. The routing layer treats a self-hosted endpoint as one more provider. Brands with residency or sovereignty requirements usually run a mix.
How do we know what an agent did?
Evidence logging is part of the install, not an add-on. Every call, tool result and decision is recorded, which is also what the AI Act documentation obligations need.
Rarely bought alone.
Foundation
Fashion Context Layer
PLM, ERP, PIM, DAM and the range plan - turned into something an agent can actually read.
Foundation
LLM-Native Platform Build
For houses that will not put brand data on anyone's platform. Built inside your infrastructure.
Frontier
AI Governance & Compliance
EU AI Act, content provenance and DPP-ready product data - as an architecture problem, not a policy document.
Start with the diagnostic.
Four weeks, fixed fee, and a ranked map of what is worth automating in your operation - including what is not.