What is entity resolution?

Entity resolution is the process of identifying and merging records that represent the same real-world entity across different systems. It uses matching logic, probabilistic scoring, and governance controls to produce a trusted unified profile.

Why do duplicate records matter?

Duplicate and fragmented entities distort nearly every downstream workflow, from forecasting to attribution to AI recommendations. Teams lose confidence when different systems report different answers for the same account or contact.

How does Syntaxia resolve entities?

Syntaxia applies deterministic and probabilistic matching across records, assigns confidence levels, and routes ambiguous cases for review when needed. Resolved entities are maintained as a governed canonical model that the rest of the platform consumes.

One record per entity, across every system

When the same entity exists as multiple records across multiple systems, every downstream output is built on unreliable data. Entity resolution identifies and merges those records into one trusted profile. Syntaxia combines probabilistic matching, confidence scoring, and human review for ambiguous cases, so reporting, AI outputs, and attribution all operate from clean, unified records.

How it works

Matching starts with specific identifiers, then firmographic and historical evidence, then graph context across related entities. Ambiguous cases go to review. Everything else lands in one governed golden record.

Scattered source records

Different names and native IDs for the same company across systems.

Salesforce

Northline Systems

sf_id 0018e00001Kx…

domain northline.io

HubSpot

Northline Systems LLC

hs_id 51288401

domain northline.io

ZoomInfo

NORTHLINE SYSTEMS

zi_id 349201188

duns 08-441-2291

Golden record

97% confidence

Northline Systems

One governed profile with lineage back to every source ID, firmographic history, and connected entities.

match → score → graph confirm → canonical model

Resolution steps

1
Match across systems
Deterministic and probabilistic matching runs across every connected source to find records that represent the same entity.
2
Score and review
Each match carries a confidence level. Ambiguous cases are routed for human review instead of silently merged.
3
Maintain the canonical model
Resolved entities are maintained as a governed canonical model that every other platform capability consumes.

What It Enables

Trusted reporting

Every system reports the same answer for the same account or contact.

Clean attribution

Revenue and activity roll up to one entity, not fragments spread across duplicates.

Reliable automation

AI recommendations and workflows operate on unified records instead of amplifying fragmentation.

Frequently asked questions

Keep exploring

Foundation

Entity Resolution

One record per entity, across every system. Probabilistic resolution with confidence scoring and human review for ambiguous cases.

You are here

One record per entity, across every system

How it works

Match across systems

Score and review

Maintain the canonical model