Supply Chain Workflow Studio · Supply chain software
AI that designs the workflow, then gets out of the way
Supply-chain planners own their process but can't customize their tools. Every change is a six-month IT ticket, and nobody wants an LLM improvising with allocation math at runtime.
0
LLM tokens consumed at runtime. AI works at design time only
100%
deterministic: every run produces a verifiable hash receipt
~12 min
for AI to generate a working allocation workflow from spec
The constraint that makes it interesting
Everyone is bolting LLMs onto business software. The problem: you cannot run a supply chain on a model that might answer differently tomorrow. An allocation engine that gives two different answers to the same question isn’t a tool. It’s a liability.
So we built the opposite. In the Workflow Studio, AI and the planner design a workflow together, through conversation. The AI writes validated, locked configuration against a strictly bounded customization surface. At runtime, zero AI is involved: execution is pure, deterministic code.
Who it’s for
Supply-chain planning teams of fewer than ten people who own their process but can’t touch their tools. Today, changing how allocation works means filing a ticket and waiting months for IT or a vendor. The Studio collapses that loop to a conversation: describe the change, review what the AI proposes, lock it, run it.
What we built
- A design studio where planners describe changes in plain language and the AI produces only schema-validated workflow overlays. It cannot touch the engine, only the surface the template explicitly exposes.
- A deterministic engine with fixed-point decimal math, canonical data forms, and a hash contract: every run records input and output hashes, and re-runs are byte-identical, guaranteed.
- Reviewed workflow templates, starting with available-to-promise allocation: priority-aware partial fills, displacement analysis with dollar impact, and safety-stock breach detection.
- A loud ingest pipeline that turns messy CSVs into a validated canonical dataset. Nothing gets silently dropped; every rejected row says why.
- Portable execution. The same engine runs on Cloudflare Workers, Node.js, browsers, and desktop, with zero platform-specific imports.
The proof
In the phase-zero experiment, the AI generated two production-grade workflows from written specs with zero manual corrections, validated against hand-computed reference results. The whole generation took about twelve minutes.
That’s the pattern we bring to client work: AI as a design tool with hard guardrails, not a runtime gamble.