Skip to content

Agentic Supplier Management

Answer a supply disruption with a cited list of compliant alternative suppliers, while the ERP stays in place.

Request an intro

Two agents turn a disruption signal into a cited shortlist of alternative suppliers, with a procurement manager approving each step.

What the agents do

Scores which suppliers a disruption puts at risk, then searches contracts in any language to propose compliant alternatives.

Built by
MongoDB Industry Solutions, with Voyage AI
Contributed by
MongoDB (Built the reference implementation and the data layer)
Industry
Retail
Audience
B2B
Category
Fulfillment & Supply ChainSearch & Discovery
01
The problem

Why this exists

Retail supply chains get tested by geopolitics, weather, and logistics bottlenecks, often overnight. The supplier information procurement needs sits in rigid ERP tables, spreadsheets, and PDF contracts and emails that nobody can query. When a disruption hits, teams spend hours or days gathering data across disconnected systems, and the delay shows up as stockouts, unplanned costs, and lost customer trust.

Read more

Replacing the ERP is rarely an option. It owns the transactional rules and workflows, so any new capability has to sit above it.

02
Outcomes

What this demonstrates

  • A disruption signal becomes a ranked exposure assessment, with a risk score and a plain-language summary, without manual work.
  • Two human decision points: a manager picks the impacted supplier, then approves an alternative. The agents recommend; they do not decide.
  • An English query retrieves contracts and certificates written in Arabic, Spanish, Chinese, or Vietnamese, with no translation step.
Read more
  • The ERP stays the system of record; its supplier and order data streams into the database the agents read.

What you would add to ship it

  • Replace the demo trigger signals with live geopolitical, weather, and logistics feeds.
03
How it works

Architecture

The ERP keeps enforcing business rules. Supplier and order data streams into MongoDB Atlas through change data capture, and operational records, vector embeddings, search indexes, and agent memory live in the same collections under the same access controls.

Read more

An ingestion engine with no LLM normalizes geopolitical, climate, and logistics signals into one format. The risk evaluator, a LangGraph agent, wakes when signals land, finds exposed suppliers with geospatial queries, calculates a Risk Priority Number, and checks agent memory for the supplier's own history and for precedent across suppliers.

When a manager flags a supplier, the alternative finder, also a LangGraph agent, searches chunked supplier documents with hybrid vector and full-text retrieval, reranks the candidates with Voyage AI, checks every claim against the cited evidence, and ranks the options by proximity. Documents are embedded automatically on every insert or update.

The ERP keeps enforcing business rules and streams supplier and order data into MongoDB Atlas through change data capture. Geopolitical, climate, and logistics signals pass through an ingestion engine with no LLM, which normalizes them into one format. In Atlas, supplier and order records, vector embeddings, search indexes, and agent memory share the same collections and access controls. When signals land, the risk evaluator, a LangGraph agent, finds exposed suppliers with geospatial queries, calculates a Risk Priority Number, and checks supplier history and precedent. When a manager flags a supplier, the alternative finder, also a LangGraph agent, runs hybrid vector and full-text retrieval over chunked supplier documents, reranks with Voyage AI, checks every claim against cited evidence, and ranks the options by proximity.

The ERP keeps enforcing business rules and streams supplier and order data into MongoDB Atlas through change data capture. Geopolitical, climate, and logistics signals pass through an ingestion engine with no LLM, which normalizes them into one format. In Atlas, supplier and order records, vector embeddings, search indexes, and agent memory share the same collections and access controls. When signals land, the risk evaluator, a LangGraph agent, finds exposed suppliers with geospatial queries, calculates a Risk Priority Number, and checks supplier history and precedent. When a manager flags a supplier, the alternative finder, also a LangGraph agent, runs hybrid vector and full-text retrieval over chunked supplier documents, reranks with Voyage AI, checks every claim against cited evidence, and ranks the options by proximity.
Open standards used
REST APIs
Tech stack
MongoDB AtlasAtlas Vector SearchVoyage AILangGraphAnthropic APIPythonDocker
Builders

Everyone credited on this solution

Get hands-on

Resources

Blog post
Next step

Take the code

This one is published in full. Read it, run it, fork it. Nothing here is behind a conversation.

Open the repositoryRead the docs

Request an intro

MongoDB is behind this solution. Ask about responding to supplier disruptions faster, and the MACH Alliance will put you in touch with the person who can answer.

The MACH Alliance passes your details to a named contact at MongoDB who knows this work and can answer your questions. We aim to reply within 2 business days. Your details are handled under the MACH Alliance privacy policy.

Not ready to talk to anyone? MACH Insider is the Alliance mailing list. New solutions, program openings, and the research behind them, once a month.

Join MACH Insider
Keep reading

Related solutions

Verification

Published case study

The detail on this page is drawn from a case study published by the contributor or the MACH Alliance. Any figures on this page are the ones that case study states.

What this page does not establish

  • The demo generates three fixed trigger signals per session instead of calling live feeds such as MarineTraffic or NOAA.
  • No retailer runs this in production yet, so there is no measured reduction in response time.

Where this came from

Written from MongoDB's Solutions Library page, public repository, and blog post on agentic supplier management, and the write-up MongoDB sent the MACH Alliance in October 2026.

Status: Reference prototypeAdded Oct 29, 2026A listing is an editorial decision, not a MACH Alliance certification.
Put a solution forwardBack to the Hub

Built something worth showing?

Put your agentic solution in front of the enterprise leaders who use the hub to decide what to build next. Submissions are open to MACH Alliance Members, Collaborators, or Ambassadors.

Put a solution forward