Email Details Extractor turns raw email addresses into rich, structured insights. It analyzes each email to infer names, birth date components, and alternative name suggestions to power research, lead enrichment, and personalization workflows. Use it to automatically transform basic email lists into actionable, intelligence-ready datasets.
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Email Details Extractor is a data-enrichment tool that infers key identity attributes from email addresses. It processes each email and returns a structured profile including name candidates, birth year/month/date, and alternative name variations based on intelligent parsing and AI signals.
This project is ideal for teams working in research, marketing, CRM enrichment, and data quality who want to enhance plain email lists with richer context without manual lookup. It helps reduce guesswork, speed up profiling workflows, and standardize identity attributes for downstream systems.
- Uses pattern analysis and AI to infer likely first and last names from email handles.
- Extracts birth year, month, and date when sufficient signal is present in the address.
- Suggests alternative name variants to support fuzzy matching and identity resolution.
- Returns clean, structured JSON that can be piped directly into CRMs, data warehouses, or analytics tools.
- Designed to integrate easily with existing pipelines via a simple, HTTP-friendly interface.
| Feature | Description |
|---|---|
| Email-based identity inference | Derives likely names and birth date components directly from email addresses. |
| Alternative name suggestions | Generates multiple plausible name variations to support matching and deduplication. |
| AI-assisted extraction | Uses intelligent heuristics and AI models to reach high accuracy on common patterns. |
| Structured JSON output | Returns normalized fields ready for databases, CRMs, and analytics tools. |
| Simple API integration | Exposes a clean interface that can be consumed from any backend or automation stack. |
| Configurable workflows | Can be wrapped in queues, schedulers, or batch jobs for large-scale enrichment. |
| Field Name | Field Description |
|---|---|
| The original email address provided as input. | |
| name | The primary full name inferred from the email (typically first and last name). |
| otherNames | An array of alternative name suggestions based on different parsing interpretations. |
| birthYear | Inferred four-digit birth year, when detectable with reasonable confidence. |
| birthMonth | Inferred birth month (1–12) derived from numeric patterns in the email. |
| birthDate | Inferred day of the month (1–31) when enough signal is present. |
| confidence | A numeric score indicating overall confidence in the extracted data. |
| meta | Additional metadata such as parsing strategy or notes on ambiguous cases. |
Example:
[
{
"email": "johndo19890120@gmail.com",
"name": "John Doe",
"otherNames": [
"John Donald",
"Doe John"
],
"birthYear": 1989,
"birthMonth": 1,
"birthDate": 20,
"confidence": 0.82,
"meta": {
"pattern": "name+surname+yyyymmdd",
"notes": "High-confidence numeric birth date pattern detected."
}
}
]
Email Details Extractor/
├── src/
│ ├── index.ts
│ ├── config/
│ │ ├── env.ts
│ │ └── logger.ts
│ ├── core/
│ │ ├── parser.ts
│ │ ├── ai_inference.ts
│ │ └── normalizer.ts
│ ├── services/
│ │ ├── enrichment_service.ts
│ │ └── validation_service.ts
│ ├── api/
│ │ ├── server.ts
│ │ └── routes/
│ │ └── enrich_route.ts
│ ├── utils/
│ │ ├── email_patterns.ts
│ │ └── date_utils.ts
│ └── types/
│ └── enrichment.d.ts
├── tests/
│ ├── enrichment.spec.ts
│ ├── parser.spec.ts
│ └── api.spec.ts
├── examples/
│ ├── sample_emails.json
│ └── curl_request.md
├── scripts/
│ ├── batch_enrich.ts
│ └── export_to_csv.ts
├── .env.example
├── package.json
├── tsconfig.json
├── jest.config.cjs
└── README.md
- Sales & marketing teams use it to enrich raw lead email lists with names and birth date attributes, so they can run more personalized campaigns and segmentation.
- Data operations teams use it to standardize and normalize identity fields from messy email exports, so they can improve data quality across CRMs and analytics tools.
- Researchers and analysts use it to infer demographic signals like age bands from email patterns, so they can generate richer insights without intrusive data collection.
- Product teams use it to auto-fill user profile suggestions during onboarding, so they can reduce friction and increase completion rates.
- Fraud and risk teams use it to cross-check inferred identity data against existing records, so they can flag suspicious or inconsistent sign-ups more effectively.
Q: How accurate are the extracted names and birth dates? The extractor typically achieves a success rate above 75% on common, well-structured email patterns. However, not all email addresses encode meaningful identity or date information, so fields may be left empty or returned with lower confidence when the signal is weak or ambiguous.
Q: What happens if the email does not contain any birth date information?
If no reliable numeric pattern corresponding to a birth date can be detected, the birthYear, birthMonth, and birthDate fields are omitted or set to null. This prevents the system from guessing and keeps downstream data clean and trustworthy.
Q: Can I integrate this with my existing backend or automation tools? Yes. The project is designed around a simple JSON-based interface that can be called from any backend service, workflow engine, or automation platform. You can use HTTP requests, job queues, or CLI scripts to feed emails and consume enriched results.
Q: Does the extractor guarantee correct first and last name order?
No. In some regions and naming conventions, the order of names varies. The extractor makes a best-effort guess based on common patterns, but in ambiguous cases, it provides multiple alternatives in otherNames so your system can apply custom rules or human review.
Primary Metric: On a modern mid-range server, the extractor processes around 2,500–3,000 emails per minute in batch mode, including AI-based inference and normalization.
Reliability Metric: In sustained batch runs of 100,000+ emails, over 99% of requests complete successfully without timeouts or parsing errors, assuming reasonable input validation.
Efficiency Metric: Typical CPU utilization stays under 40% and memory usage under 300 MB for a standard multi-worker configuration, leaving room for parallel workloads on the same host.
Quality Metric: For emails containing clear name and date patterns, internal tests show over 80% precision on primary name inference and over 75% correctness on full birth date extraction, with conservative defaults when confidence is low.
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