# Entity Graph

*/Opportunities/Entity_Graph*

## Opportunity Overview

**Wedge**: The initial beachhead targets private equity due diligence firms conducting target evaluations. This niche feels the pain acutely during high-velocity deal windows, possesses ready-to-process data rooms, and demands immediate relationship mapping. Once established as the system of record for private market entity maps, the product expands into continuous corporate monitoring and compliance for corporate banks.
**Timing**: Context window expansions and improved reasoning models now allow systems to ingest hundreds of pages of legal text and output highly accurate, deterministic relationship connections. Previously, entity extraction required brittle named entity recognition models that failed at long-range cross-document coreference.
**Why This I C P**: Financial due diligence and risk teams face strict regulatory penalties for missing beneficial ownership links, making accurate entity resolution a non-discretionary spend. They also process highly dense document types like corporate filings and legal structures, providing a high-value environment for automated extraction.
**Size Of Prize**: There are approximately 15,000 mid-to-large financial institutions and corporate risk teams globally spending an average of $250,000 annually on manual entity resolution and investigative labor. This yields an addressable market of roughly $3.75 billion for automated entity graph generation.
**Gap Narrative**: Risk and compliance teams manually parse unstructured documents to map corporate hierarchies, ultimate beneficial owners, and risk exposures. Existing databases remain static and fail to capture real-time relationships buried in news, legal filings, and internal contracts. This leaves organizations relying on manual analysts to bridge the gap between raw text and actionable entity relationship maps.
**Defensibility**: Defensibility stems from proprietary data accumulation as the system processes millions of private corporate documents, mapping non-public entity relationships. This creates a cross-tenant global graph where a resolved entity in one private deal accelerates resolution in future deals. Once integrated into the firm's core risk underwriting workflow, the operational switching cost becomes prohibitively high.
**Why This Thesis**: A Service-as-Software approach directly replaces the outsourced analyst teams currently hired to read documents and populate graph databases. By delivering the final populated entity graph rather than a tool to build it, the product intercepts the existing operational budget immediately.

## Opportunity Linked Thesis

**Thesis**: [Software](/Theses/Software)

## Opportunity Linked I C P

**Icp**: [Financial Institution](/CompanyTypes/Financial_Institution)

## Opportunity Market Sizing

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**S A M**: ~$1.5B-2.5B focusing on ~10,000 tier-1 and tier-2 North American and European commercial banks, asset managers, and insurance carriers
**S O M**: ~$50M-100M achievable over 3 years capturing early-adopter mid-market regional banks and specialty lenders
**T A M**: ~35,000 global mid-to-large financial institutions × ~$150,000-200,000/yr on entity data infrastructure ≈ ~$5.2B-7B
**Growth Rate**: ~15-20%/yr, driven by expanding global KYC/AML regulatory requirements and the necessity of real-time counterparty risk scoring
**Paid Comparable Spend**: ~$200,000-500,000/yr per institution spent on legacy master data management (MDM) software, external data vendor subscriptions, and manual data reconciliation labor for compliance teams

## Opportunity Incumbents

- [Neo4j](/Products/Neo4j) — Open-Source
- [Palantir Foundry](/Products/Palantir_Foundry) — Tool
- [Amazon Neptune](/Products/Amazon_Neptune) — Tool
- [In-House Data Pipeline](/Products/In-House_Data_Pipeline) — DIY
- [Quantexa](/Products/Quantexa) — Tool
- [Custom SQL Scripts](/Products/Custom_SQL_Scripts) — DIY
- [TigerGraph](/Products/TigerGraph) — Tool

## Opportunity Win Conditions

**Kill Thresholds**:
- Integration time > 45 days for standard KYC and CRM data sources
- Pilot-to-paid conversion rate < 40% after 90 days of live testing
- Analyst weekly active user retention < 50% during the proof of concept
- Infrastructure cloud costs > 25% of the projected annual contract value
**Leading Metrics**:
- Days-to-first-graph-query
- Number of distinct external data sources ingested per account
- Daily active analysts executing multi-hop queries
- False-positive resolution time in minutes
- API query latency under 100ms for 3-hop traversals
**What Proves Right**: Customers connect three or more disjointed entity data sources within their first 14 days of deployment. Compliance analysts resolve flagged counterparty risks 50% faster by querying the graph directly instead of manually cross-referencing tables. The product commands a $150,000 annual contract value with pilot conversions happening in under 90 days.
**What Proves Wrong**: Data integration requires custom professional services exceeding 60 days per deployment, eroding margins and stalling time-to-value. Financial institutions refuse to transition from existing relational database models due to compliance anxieties around graph data lineage. Analysts abandon the visual graph interface and revert to spreadsheet exports to complete counterparty risk assessments.

## Opportunity Build Profile

**Hardest Part**: Achieving sub-second deterministic entity resolution across conflicting, sparse, or noisy records without generating false positives that break downstream compliance workflows.
**Min Viable Scope**: Deliver a read-only API and basic dashboard strictly for B2B vendor and supplier disambiguation. Exclude consumer identity resolution, real-time streaming updates, and multi-language cross-border matching.
**Cold Start Problem**: The graph provides zero matching confidence without a critical mass of connected nodes. Break this by pre-computing a massive baseline graph using public registries and commercial datasets before signing the first customer.
**Time To First Value**: Immediate via API lookup against the pre-computed base graph, followed by 1 to 2 weeks to map and ingest proprietary internal records.
**Data Moat Available**: true
**Technical Difficulty**: High

## Neighborhood

### Embodied by

- [Headless SaaS](/Theses/Headless_SaaS) — embodies · Theses

### Incumbent in

- [Homegrown Data Pipeline](/Products/Homegrown_Data_Pipeline) — incumbent in · Products
- [Custom Database Scripts](/Products/Custom_Database_Scripts) — incumbent in · Products
- [TigerGraph](/Products/TigerGraph) — incumbent in · Products
- [Amazon Neptune](/Products/Amazon_Neptune) — incumbent in · Products
- [Palantir Foundry](/Products/Palantir_Foundry) — incumbent in · Products
- [Quantexa](/Products/Quantexa) — incumbent in · Products
- [Neo4j](/Products/Neo4j) — incumbent in · Products

### Applies thesis

- [Financial Institution](/CompanyTypes/Financial_Institution) — applies thesis · CompanyTypes

### Embodies

- [Software](/Theses/Software) — embodies · Theses

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