01
Purchase order reconciliation
An agent cross-checks orders against contracts and receipts, flagging mismatches with sourced reasoning instead of a blanket exception queue.
Agentic AI in supply chain
Agents for the coordination layer of your supply chain.
We build agents that read procurement documents, cross-check vendor and shipment data, and route exceptions — with the audit trail your operations team can actually trust.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Supply chain exceptions do not follow a fixed path
Every ERP already automates the happy path. What consumes planners' time is the rest: a mismatched purchase order, a vendor document that needs cross-referencing, a demand signal that contradicts the forecast. Each one is a multi-source decision made under guardrails, well past what a single-step rule trigger reaches. Automating them fails quietly, when a plausible-sounding extraction lands in an exception queue and nobody checks whether the figure was on the document at all.
every exception routed with reasoning attached
Use cases
Where agents earn trust in supply chain operations
Multi-system workflows with a clear escalation path.
02
Vendor performance review
Reads delivery, quality, and pricing data across suppliers and drafts a sourced review — a planner signs off, not re-derives it from scratch.
03
Exception handling in fulfillment
Routes shipment and inventory exceptions to the right system or team, with a documented reason instead of a silent manual queue.
04
Demand-signal triage
Surfaces where incoming demand signals diverge from the current forecast, so planners investigate the exceptions that matter first.
What the mechanism delivers
Multi-system coordination, measured in production
These numbers come from shipped agentic AI engagements outside supply chain — Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare). They are here because they measure the same three things a supply chain workflow depends on: multi-step validation instead of a single pass, orchestration across many tools, and an extraction layer that stops rather than guesses. Every figure links to the case study behind it.
Agents do not release a purchase order or overwrite a forecast. They compress the reading, cross-checking and drafting that sits before every planner decision. Each step is logged for review.
2 hrs → 3 min
To generate a validated workflow
Not a supply chain deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
95.4%
Success rate in workflow results
Not a supply chain deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
across 329+ nurse-validated scenarios
Not a supply chain deployment — Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production agentic deployments
We have not yet shipped a production agent inside a supply-chain-specific company. These are the closest available references — the same multi-step validation and multi-tool orchestration, shipped in engineering software, print on demand and healthcare.
Delivery path
From workflow audit to a production supply chain agent
TriStorm keeps integration risk and engineering aligned — surfaced before full build commitment, not after.
Map systems and workflow
We audit the target process (exception queue, procurement intake, or vendor review cycle) along with ERP, WMS and TMS boundaries and the data quality behind them, ranking candidates by impact and integration risk.
- Systems & workflow audit
- Data quality review
- Prioritised use case
Build, test and validate
We implement against real procurement and logistics data shapes, with an evaluation suite scored against your own tolerance rules before any output reaches a planner.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and support
Production rollout with monitoring, full audit logging, and a structured handoff so your operations team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own procurement and fulfillment workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in supply chain, answered
What is agentic AI in supply chain?
Agentic AI in supply chain is software that plans and carries out a multi-step operational task across systems — reading a supplier document, cross-checking it against a purchase order and a receipt, and drafting an exception with its reasoning attached — with a planner as the final gate. Unlike a rules engine that executes a fixed path, an agent handles the cases the rules route to a person. Unlike a forecasting model, it does not predict demand; it coordinates the work around the decision.
Where does agentic AI fit into supply chain operations?
In workflows that span multiple systems and require judgment under uncertainty — demand signals that do not match a forecast, a vendor document that needs cross-checking, an exception that needs routing to the right team. We build agents for the coordination layer, not to replace planners.
How is this different from the automation rules already in our ERP or TMS?
Rules engines execute a fixed path. An agent reads unstructured inputs (supplier emails, shipment documents, demand signals) reasons across them, and decides within guardrails, escalating when confidence is low. It handles the exceptions your rules engine currently routes to a person.
Do you have a supply chain deployment we can look at?
Not a published one. We have not yet shipped a production agent inside a supply-chain-specific company, and we would rather say so than dress up an adjacent case as vertical proof. The closest references are Synera, where multi-step validation replaced a single generation pass, and Mixam, where a three-agent system orchestrates 15 tools against more than a billion product combinations. Both are multi-system coordination under guardrails, which is the mechanism supply chain workflows need.
Which supply chain workflows see agents used in production today?
Document-heavy, multi-system workflows: purchase order reconciliation, vendor performance review, exception handling in fulfillment, and demand-signal triage across sourcing and planning teams.
Can an agent actually read a supplier invoice or shipping document correctly?
It can extract and cross-reference, and it must be built so that it stops rather than guesses. The mechanism is staged retrieval and validation rather than one model answering in a single pass — the same architecture behind our zero-hallucination pipeline for Schmitt-Thompson Clinical Content, a healthcare deployment where a wrong recommendation is a patient-safety event. On a shipping document the stakes differ, the discipline does not.
Can agents replace our demand planners?
No, and we do not build them to. An agent surfaces where incoming signals diverge from the forecast and assembles the evidence, so a planner spends their time on the exceptions that matter instead of finding them. The judgment call stays with your team, and every action the agent took is in the log.
Can this integrate with our existing ERP, WMS, or TMS?
Yes, through your existing APIs. We map the systems of record first, then build the agent to read and write through your current auth model — no rip-and-replace.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement.
What happens when the agent is not confident?
It stops and escalates. Confidence thresholds and document coverage gaps route to a planner with the reasoning attached, and the escalation is logged. Agents operate inside your existing access controls — we do not stand up a shadow copy of your supplier or order data.
Do we own the agent code after deployment?
Yes. Full source ownership of agent logic, integrations, and evaluation harnesses transfers to your team at handoff — no proprietary runtime lock-in on what we build.
Start with one workflow
Map one supply chain workflow worth automating
A 30-minute call identifies integration points, data quality gaps, and a realistic path to a working agent.