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How Sella Delivers Scalable Credit Risk Explanations with Prometheux
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By Industry / Supply Chain & Logistics

Trusted Agents for
Supply Chains

Prometheux helps supply chain leaders keep goods moving through disruption, balance stock against cash and deliver on time, with AI that runs your own rules on the ERP, supplier, inventory and logistics data you already have. Planners, buyers and agents get the same answer every time, with the evidence behind it, at any tier of the chain. No migration, no new store.

Proven in production

Why supply chain AI stalls

Data Everywhere.
Answers Nowhere.

Procurement, planning, warehousing and transport each run on their own systems, and the questions leaders ask cut across all of them. So every disruption, audit or cost review starts with people pulling spreadsheets together, and AI pilots never earn enough trust to act.

Today
With Prometheux
Supplier, inventory and shipment data split across ERP, WMS, TMS and spreadsheets
Your supply chain rules run across every system at once, at any tier
Demand, supply and finance teams each plan from their own numbers
One set of rules defines stock, demand and cost the same way for every team and tool
Every disruption means rebuilding the picture by hand
Rules re-run on a schedule or when data changes and push what is new to the right team
AI pilots give different answers to the same question, so they never reach production
Agents call deterministic rules over MCP: same input, same output
Answers nobody can explain to the plant manager, the auditor or the board
Every result comes with its path, rules and source rows
Modernising means a risky migration to one more platform
Data is processed in place, where it already lives: nothing to copy or migrate
Use case menu

Where Supply Chain Teams
Start

Each use case runs your own rules on data where it already lives, and reuses the connections and rules the last one built. Pick the area that matters to you this quarter.

Risk & Resilience

Know what a disruption breaks before it reaches your customers.

01Supplier Risk Monitoring
Supplier risk rules re-run on a schedule or when supplier data changes, and new exposures are written back for the team that owns them.Agentic Workflows →
02Disruption Impact Analysis
A supplier, site or port goes down: which plants, products and customer orders are affected, and through which path?Graph Traversal →
03Supplier 360
One view of each supplier across ERP, procurement and quality systems, with the rule behind every record matched.Entity Resolution →
04Single Points of Failure
Which suppliers sit on the most supply paths, so that one failure would reach the most plants and products?Graph Analytics without a Graph Database →
05Risk Questions for Agents
Planners ask an agent "what's exposed if X fails?"; it calls your exposure rules over MCP or API and returns the results with their chains.AI Agents →

Sourcing & Compliance

Buy to policy, and prove it, at every tier.

01Supplier Performance Scorecards
On-time delivery, quality and price performance per supplier, computed by the same rules from every system, on a schedule.Cross-Database Processing →
02Contract and PO Compliance
Which purchase orders break the agreed price, volume or payment terms, against which term and which PO line?Deterministic AI →
03Certificates and Audits
Which active suppliers have an expired or missing certificate or audit, and which open orders depend on them?Cross-Database Processing →
04Restricted Origins at Every Tier
Which products depend, at any tier, on a supplier in a restricted region, skipping suppliers with an approved exception?AI Governance →
05Sourcing Policies to Rules
An LLM step drafts rules from sourcing policies and buyer know-how (non-deterministic); your team reviews them before they run.Context as Code →

Planning & Inventory

Align demand, supply and stock, so cash is not tied up and shelves are not empty.

01One Plan Across Teams
Demand, supply and finance read the same definitions of stock, demand and cost, written once as rules and served to every tool.Semantic Layer →
02Demand Trends and Anomalies
Python steps flag trends and anomalies in demand and supplier lead times, and rules decide which ones planners review.In-Place Data Processing →
03Stockouts and Excess Stock
Which SKUs sit below their safety-stock rule or above their cover target at each site, and which open orders change that?Deterministic AI →
04BOM Explosion at Any Depth
List every sub-component of a product from the BOM tables you already keep, however deep the assembly goes.Recursion →
05Recall Where-Used
Which finished products, open orders and stock contain a recalled part, at any level of the BOM?Graph Traversal →
06Rebalance Stock Across Sites
Which distribution centres hold unsold stock that could cover a shortage elsewhere, and by which route through your network?Graph Traversal →

Logistics & Cost

Shipments, freight and product cost, reconciled across systems.

01Shipments at Risk of Missing Delivery
In-transit shipments checked against delivery rules on a schedule or when carrier events arrive: which customer orders and plants depend on the late ones?Agentic Workflows →
02Freight Invoice Reconciliation
Which freight invoices don't match contracted rates, shipments or receipts across TMS, WMS and ERP, and why?Cross-Database Processing →
03Cost to Serve and Product Cost
Roll part, freight and handling cost up to every product and assembly as the recursion runs, and see which inputs drive the total.Recursion →
04Routes Through the Network
What is the shortest path between a site and a customer through the lanes and hubs still open, and which ones are cut off?Graph Analytics without a Graph Database →
05Exceptions Written Back
Delivery and cost exceptions are written back to the ERP or WMS databases, with the rows that caused them.In-Place Data Processing →
Where connected data matters

The Questions Other
Tools Can't Answer

Disruption

Supplier Blast Radius

Kora Steel shuts down. One rule returns Lyon, Turin and Gdańsk, and shows Lyon is exposed at tier 2 through Fastenix, traced to the BOM rows. See graph traversal.

Engineering

BOM Explosion, Recall and Cost

Every sub-component of a product, every finished product containing a recalled part, and cost rolled up each assembly, from the BOM tables you already keep. See recursion.

Compliance

Sourcing Rules Across Tiers

Write origin and restricted-supplier policies once and check them against every tier, with the supplier path behind each flag for the audit file. See data lineage.

Master data

One Supplier Across Systems

The same supplier appears under different names and codes in ERP, procurement and quality. Readable matching rules link them, with the evidence behind every merge. See entity resolution.

Architecture

One Rules Layer
Across the Chain

Typical supply chain platforms load your data into their own store and sell modules on top. Prometheux sits between your supply chain data and the people, tools and agents that act on it, and runs the rules where the data already lives.

Analytics

Planning dashboards, graph and BI

Integrations

Back into the databases behind ERP, WMS and TMS

Agents

Supply agents, deterministic answers

Models

Define the ontology, read answers, and power analytics, integrations and agents

Your supply chain data

ERP, procurement, PLM, warehouse, transport, supplier and order data, connected in place. Nothing to migrate.

  • Supply Chain Operations

    • Supplier exposure, sourcing checks, BOMs, inventory and shipments on one set of rules
    • Agents call deterministic supply chain rules over MCP and API, with the derivation behind every answer
    • Results written back to ERP, WMS and TMS databases, on a schedule or when data changes
  • The Supply Chain Rules Engine

    • One model of suppliers, parts, sites, stock and shipments across systems, instead of a copy per tool
    • Recursion with aggregation and graph algorithms: exposure at any tier, cost rolled up the BOM, routes through the network
    • Same input, same output, with lineage to the rules and source rows
  • Cloud Agnostic

    • Run on your own servers, your cloud or Prometheux cloud, under your own controls
    • Scale horizontally on clusters to every supplier, part and shipment, with optional GPU acceleration
    • 1-click deployment anywhere: EKS, AKS, GKE, Databricks Clusters, Snowpark Containers
  • Supply-Data Agnostic

    • Read and write supplier, BOM, inventory, shipment and order data in any database, format and schema
    • Connect ERPs, warehouses, supplier portals, files and APIs across clouds and on-premises
    • No new data store to migrate into and keep in sync
Layer by layer

Same Layers.
Better Built.

Map a typical supply chain planning or risk platform to Prometheux and the difference is where the work happens: in place, in rules you own, with an answer you can check.

Typical supply chain platform
Prometheux
Data: ERP, supplier and logistics data loaded into the vendor's platform
Data: sources read and written in place, across clouds and on-premises; no new store
Entities: supplier and part matching inside the vendor's model
Entities: matching rules you can read, with the rule and records behind every merge
Logic: sourcing, stock and service policies configured inside vendor workflows
Logic: your policies as versioned rules your team owns
Questions: dependency walks of known depth
Questions: recursion with aggregation at every hop: exposure at any tier, cost rolled up the BOM
Analytics: proprietary risk scores and ML forecasts
Analytics: deterministic rules, graph algorithms and Python steps, with lineage on each result
AI interface: a copilot over the platform
AI interface: agents call deterministic rules over MCP and see how each answer was reached
Deployment: the vendor's cloud
Deployment: your own servers, your cloud, Prometheux cloud, or inside Snowflake and Databricks
What you get

The Full Chain.
Not a Guess.

Supply networks are recursive: suppliers of suppliers, parts of parts. Prometheux handles that recursion natively on your tables, so you need no graph database and no hand-written loops, and the same question always returns the same answer.

Any Tier, Any Depth

Recursive rules cover every tier and stop when the chain ends, even when supply links loop back on themselves.

BOMs That Roll Up

Explode an assembly into every sub-part, find every product using a part, and roll up cost as the recursion runs.

Edges From Anywhere

Supplier links in one database, BOMs in another, shipments in Parquet: one rule set reads them all where they are.

A Route Per Answer

Each exposed plant or product links to the supplier path, rules and source rows behind it, ready to share.

What this means for you ⟶

Map your supply chain with us

See the blast radius of a supplier in one query. Show the plant manager exactly why. Change a sourcing rule once, everywhere.

How supply chain teams start

One Question.
Then the Next.

01

Connect in Place

Point Prometheux at the systems behind one question: ERP, procurement, PLM, warehouse or transport. The data is processed where it lives; nothing is copied. See in-place data processing.

02

Codify the Knowledge

Your planners, our forward deployed engineers and the AI assistant turn spreadsheets, policies and expertise into readable rules, such as "a plant depends on every supplier of a supplier it depends on". See recursion.

03

Run One Use Case

Supplier exposure, a sourcing check, a BOM roll-up or a late-shipment impact, on current data, for teams and agents alike, with the path to each result. See data lineage.

04

Expand Use Case by Use Case

Each new use case reuses the rules and connections already in place, so the next team starts further ahead.

FAQ

Questions,
Answered

How does Prometheux find exposure beyond tier one?

With recursive rules. You state the base case (a supplier supplies a plant) and the recursive case (a plant depends on every supplier of a supplier it depends on), and the engine follows the chain to every tier until no new links are found.

Which supply chain questions can Prometheux answer?

Any question you can state as rules over data you hold: supplier exposure at any tier, sourcing and certificate checks, BOM explosion and cost roll-up, stock against policy, and the orders behind a late shipment. The use case menu above lists common starting points.

Do we need a graph database or a new supply chain platform?

No. Prometheux reads supplier, BOM, inventory and logistics tables where they already live, in your ERP, databases, warehouses and files, and runs the rules over them. Nothing has to be loaded into a graph store first.

What if our supply links contain cycles?

Rules are evaluated to a fixed point: once no new facts can be derived, the engine stops. Following a loop in supplier links (who depends on whom) stops once every link has been found; for computed values such as path length, a minimum-style aggregation lets the result settle.

Can we see why a plant, product or order is flagged?

Yes. Every derived result keeps its provenance: the rules that fired and the source rows they used, including each hop of a multi-tier chain.

Can it tell us when exposure changes?

Yes. Rules can run on a schedule or when data changes and write results back, so a new exposure reaches planners or an agent without anyone re-running the analysis.

Does this replace our planning or supplier-risk tools?

No. Risk-intelligence services add outside data on suppliers you may not know about; planning suites model scenarios inside their own platform. Prometheux runs your dependency and sourcing rules on the data you hold, including supplier data you license, and dashboards, planning tools and agents call the results through SQL, the REST API, the Python SDK or MCP.

Bring One Supply Chain
Question.

Supplier exposure, a sourcing check, a BOM roll-up or a late shipment on your own data. Our forward deployed engineers will run it with you, in place, on your infrastructure.

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