How Enrichment Works
ABM.dev uses a sophisticated multi-source enrichment engine that gathers evidence from multiple providers, synthesizes insights with AI, and delivers 90 standardized fields with confidence scores.
Overview
When you submit an enrichment request, ABM.dev runs a sophisticated pipeline: analyzing source availability, gathering evidence in parallel from multiple providers, synthesizing insights with AI, and outputting 90 standardized canonical fields with per-field confidence scores.
Multi-Source
4+ providers queried in parallel for comprehensive coverage
AI Synthesis
Intelligent merging with narrative generation and persona matching
90 Fields
Standardized canonical fields with per-field confidence
CRM Sync
Automatic field mapping and writeback to HubSpot, Salesforce
The Enrichment Pipeline
Every enrichment request flows through a 6-stage pipeline designed for maximum accuracy and efficiency:
Input Normalization
Your input data (email, name, company, LinkedIn URL) is normalized and validated. Domain names are extracted from emails, names are parsed into components, and LinkedIn URLs are standardized.
jane.smith@acme.com → domain: acme.com, likely name: Jane SmithSource Portfolio Analysis
Before querying any sources, we analyze which providers can contribute based on your input. This produces a portfolio score predicting enrichment quality.
| Tier | Score | Meaning |
|---|---|---|
| Excellent | ≥0.85 | All key sources available (LinkedIn + email + company) |
| Very Good | ≥0.70 | Most sources available, high-quality expected |
| Moderate | ≥0.50 | Core sources available, solid enrichment |
| Poor | <0.50 | Limited sources, may need more input data |
Parallel Evidence Gathering
All available sources are queried simultaneously to minimize latency. Each source returns evidence with its own confidence level.
See Data Sources for details on each provider.
AI Synthesis
Evidence from all sources is merged using AI models that resolve conflicts, generate narrative fields, and match buyer personas. This stage:
- Resolves conflicting data using source reliability and recency
- Generates summaries, highlights, and outreach angles
- Matches against your buyer personas with confidence scoring
- Calculates ICP fit scores for companies
AI Models
Projection & Validation
Synthesized data is projected into 90 canonical fields. Each field receives:
- Confidence score (0-100) — how reliable is this value?
- Source attribution — which sources contributed?
- Freshness timestamp — when was data last verified?
See Canonical Fields for the complete field reference.
Field Mapping & Writeback
If CRM integration is enabled, canonical fields are transformed and written to your CRM using configurable field mappings. Transformations can format, concat, split, or convert values.
canonical: title → hubspot: jobtitle → "VP of Engineering"See Field Mapping for transformation options.
Understanding Confidence Scores
Confidence scores help you decide how to use enriched data in your workflows.
High Confidence
Multiple sources agree. Safe for automated workflows.
Medium Confidence
Good for enrichment, consider human review for critical use cases.
Low Confidence
Limited source agreement. Recommend manual verification.
Best Practice
Data Sources
ABM.dev aggregates data from multiple providers, each with different strengths:
| Source Type | Best For | Data Freshness |
|---|---|---|
| Social Networks | Current job title, profile photo, connections | Real-time |
| Company Databases | Company size, funding, industry | Weekly updates |
| Email Verification | Email validity, deliverability | Real-time |
| Technographics | Tech stack, tools used | Monthly scans |