Case Study
How Sella Delivers Scalable Credit Risk Explanations with Prometheux
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Capabilities / Graph & Complex Data Processing

Same Entity.
Proven Match.

Deduplicate and link customer, supplier and product records across systems, where they live. Every merge names the rule that fired and the exact records it used, so match quality is something you check, not a score you trust.

Proven in production

Why matching goes wrong

Black-Box Scores.
Or Rules You Read.

When the same customer appears five ways in five systems, every report, model and agent inherits the mess. ML scores, vector similarity and LLM judgements hide the decision. Prometheux makes it explicit logic.

Typical matching tool
Entity resolution with Prometheux
Copy every source into a matching hub first
Match across sources in place, with federated joins
An accuracy figure or confidence score you take on trust
Each merge cites the rule and records behind it, so any match can be checked
A matches B and B matches C, but A and C stay apart
Recursive rules close the chain into one entity
One fixed golden record for every team
Different match rules per use case, run on live sources when asked: strict for compliance, broader for marketing
AI suggests merges with no trail behind them
Clear-cut matches stay deterministic; ambiguous pairs go to an LLM step logged in lineage
Match logic buried in scripts per project
Matching rules are defined once, reused everywhere
Matching as logic

Rules That Say
Why Two Match

Write matching criteria as rules over your sources: shared tax IDs, emails, addresses or any combination. Prometheux joins across systems, links records that match, and groups them into resolved entities.

Cross-System Joins

Compare CRM, ERP, mainframe files and spreadsheets in one query, without staging them in a hub.

Transitive Clusters

Recursion links chains of matches so every record of the same entity ends up together.

Evidence for Every Merge

The chase graph records which rule linked two records and which facts it used.

AI for the Hard Cases

Route ambiguous pairs to an LLM concept that returns a typed decision, logged in lineage like any other step.

What this means for you ⟶

Match your records with us

Trust the customer count in every report. Defend each merge to auditors and data owners. Change a matching rule once and re-run everywhere.

How it works

From Duplicates to
Resolved Entities

01

Connect the Systems

Connect each source that holds the entity: CRM, billing, ERP, files. Data stays in place.

02

Write Match Rules

Encode when two records are the same, from strict ID matches to combinations of attributes.

03

Resolve and Group

Recursive rules turn pairwise matches into one resolved entity per real-world thing.

04

Review and Publish

Inspect the evidence for each match, then write the resolved view back or serve it via API.

Where it matters

One Real-World
Thing, One Record

Banking

Customer and Counterparty

Link clients across core banking, lending and onboarding systems before computing exposures.

Life sciences

HCPs and Organisations

Resolve healthcare professionals and institutions across CRM, events and third-party lists.

Procurement

Supplier Master

Find the same supplier under different names and IDs across ERPs and spend files.

Data quality

Cross-System Consistency

Check that an identifier in one system matches downstream systems, and explain every mismatch.

One View Across Heterogeneous Data

“The ability to query heterogeneous databases through a flexible ontology makes it possible to infer deep knowledge and automate complex tasks, delivering trustworthy insights that far exceed the capabilities of traditional systems.”
Anna Fava
Former Senior Manager, Data & AI · Dompé
FAQ

Questions,
Answered

What is entity resolution?

It is deciding when records in different systems refer to the same real-world customer, supplier, product or organisation, and grouping them into one resolved entity. It is also called record linkage, deduplication or match/merge.

Do I have to load all sources into one place first?

No. Prometheux joins across your databases, warehouses and files where they are, and can write the resolved view back to the system you choose.

How do I know why two records were merged?

Every match is derived by a rule, and the chase graph records which rule fired and which records it used. You can trace any resolved entity back to its evidence.

How accurate is the matching?

Matches follow the rules you approve, and every one can be checked: each merge shows the rule and records behind it. When a data owner spots a false match, you fix the rule that caused it and re-run, instead of retuning a score.

How is this different from master data management?

MDM tools usually copy records into a central hub and curate a golden record there. Prometheux resolves entities over your sources in place, keeps the match logic as readable rules, and writes the resolved view, your golden record, back to whichever system you choose.

Can AI help with ambiguous matches?

Yes. An LLM concept can review uncertain pairs and return a structured decision, while clear-cut matches stay deterministic. Both appear in lineage.

Keep exploring

Related Capabilities

Resolve Entities
You Can Defend

Bring two systems that disagree about your customers. We'll match them with rules you can read.

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