Your documents contain thousands of entity mentions. Names, places, organizations — you treat them as simple strings.
Entity Recognition
See entity resolution in action
FluentMD identifies entities, classifies their types, and resolves ambiguous mentions to canonical references.
Document Input — Entity Resolution
“Mercury CapitalORG confirmed the acquisition of a 12% stake in Apex GroupORG, a move attributed to Apex’s dominant position in Southeast AsiaLOC. Separately, Mercury reported a $340M write-down on its ParisLOC operations following regulatory changes in Q3 2024DATE. Jordan LeePER, Mercury’s CFO, attributed the loss to currency headwinds.”
Disambiguation
Domain Expertise
Built for finance. Ready for more.
FluentMD is purpose-built for financial entity recognition and disambiguation — from company names and tickers to people and instruments. The same precision extends naturally to adjacent regulated industries.
Integration
Extract entities in one call
Simple API. Full entity extraction and disambiguation in a single request.
from fluentmd import FluentMD
client = FluentMD(api_key="...")
result = client.extract(
"Mercury Capital confirmed the acquisition of "
"a 12% stake in Apex, attributed to Apex's "
"dominant position in the region."
)
result.entities
# [Entity(text="Mercury", type="ORG", link="Mercury Capital", score=0.97),
# Entity(text="Apex", type="ORG", link="Apex Group", score=0.94),
# Entity(text="the region", type="LOC", link="Southeast Asia", score=0.91)]Python & TypeScript SDKs, plus a REST API for any language. Coming soon — request early access.