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Data Infrastructure & Entity Resolution

Data Consolidation Agent

Establish a single, canonical source of truth by mathematically resolving conflicts across SEC Form D filings, Data Partners workforce metrics, Data Partners sentiment, and your CRM.

Run a schema conflict & duplication audit

The Problem

  • •Signal Fragmentation: High-value signals (e.g., a Series B filing via Form D) cannot be correlated with operational signals (e.g., headcount churn via Data Partners) due to ID mismatches.
  • •Diligence Drag: Investment teams and RevOps waste high-cost hours manually reconciling CIK codes, domains, and CRM IDs in spreadsheets.
  • •Data Trust Decay: Downstream AI agents hallucinate or fail to execute when presented with conflicting firmographics across vendor silos.

How It Works

This agent acts as an autonomous Master Data Management (MDM) layer. It continuously ingests disparate schemas, resolves identity conflicts, and maintains a persistent 'Golden Record' graph.

1

Ingestion & Normalization: Standardizes schemas from SEC Form D (Capital), Data Partners (People), Data Partners (PR), and CRM into a unified staging environment.

2

Entity Resolution: Applies weighted confidence scoring using deterministic (EIN, CIK, Domain) and probabilistic (fuzzy name matching, location triangulation) logic.

3

Graph Maintenance: Publishes a queryable, unified company object with full data lineage, preserving source-specific metadata for auditability.

Data Sources

SEC Form D Tracker (CIK, Funding Amounts, Executive Signatories)Data Partners (Headcount Velocity, Engineering/Sales Ratios, Churn Signals)Data Partners (Sentiment Analysis, PR Reach, Brand Risk Scoring)Data Partners (Granular Sector/Product Categorization)Internal CRM / Data Warehouse (Historical Context & Account Status)

Success Metrics

  • Entity Match Rate: >0% precision across external vendors and internal CRM.
  • Data Latency: Reduction of signal-to-system time from weeks to <0 hours.
  • Diligence Velocity: 0% elimination of manual row-by-row data cleaning for investment memos.

ROI Calculator

Your Inputs

  • 1
    Total Company Records in CRM
  • 2
    Current Duplicate/Conflict Rate (%)
  • 3
    Analyst Hours/Month dedicated to Data Cleaning

Formula

Value = (Records × Reduced_Error_Rate × Cost_Per_Bad_Record) + (Analyst_Hours_Saved × Hourly_OpEx)

Example Output

Reducing a 20% duplicate rate on 100k records while reclaiming 40 hours of monthly analyst time yields a net operational impact in the six-figure range annually, excluding the upside of missed deal flow prevention.

Implementation Timeline

1
Weeks 1-2

Weeks 1–3: Establish 'Capital & People' backbone (Form D Tracker + Data Partners + CRM) and calibrate matching logic.

2
Weeks 3-4

Weeks 4–6: Integrate 'Context & Sentiment' layers (Data Partners + Data Partners); activate the Unified Graph for priority diligence agents.

3
Week 5+

Ongoing: Continuous anomaly detection, new vendor ingestion, and refinement of confidence thresholds based on user feedback.

Coming Soon

  • ◆Autonomous Schema Mapping: Self-healing pipelines that adapt automatically when vendor API structures change.
  • ◆Predictive Data Decay: Alerts indicating when specific record fields (e.g., last funding date) exceed freshness thresholds.
  • ◆Federated Querying: Allowing agents to query the graph without fully ingesting the raw data.

The Data Consolidation Agent transforms your data stack from a liability into an asset. By ensuring that capital signals (Form D) and operational health signals (Data Partners) refer to the exact same entity, you unlock proprietary deal flow and precise speed-to-lead.

Run a schema conflict & duplication audit

Data Consolidation Agent

AUTONOMOUS AGENT

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