> A powerful skip tracing scraper that locates hard-to-find individuals through multiple search parameters — including name, address, and phone. This project helps you retrieve verified contact details, past residences, and known associates in seconds.

Built to make people search faster, more accurate, and insight-driven.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for Skip Trace you've just found your team — Let’s Chat. 👆👆

Introduction

Skip Trace automates the process of finding accurate contact and background information about individuals. It aggregates details such as names, addresses, phone numbers, emails, and even known relatives.

This scraper is ideal for investigators, marketers, real estate professionals, and businesses that rely on verified identity data.

How It Works

• Search by name, address, or phone number
• Retrieve enriched identity profiles with verified data sources
• Uncover connections like relatives and associates
• Generate detailed, structured JSON outputs
• Support for multiple concurrent lookups

Features

Feature	Description
Multi-Input Search	Accepts name, address, or phone-based lookups.
Data Enrichment	Returns extended profile information including emails and relatives.
Accurate Results	Uses multiple verification layers to ensure reliability.
JSON Output	Clean and structured results ready for integration.
Speed Optimized	Designed to handle multiple queries efficiently.

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What Data This Scraper Extracts

Field Name	Field Description
First Name	The person's given name.
Last Name	The person's surname.
Age	Approximate age based on records.
Born	Birth month and year if available.
Lives in	Current residence (city, state, ZIP).
Street Address	Full current street address.
Address Locality	City name of residence.
Address Region	State or region code.
Postal Code	ZIP or postal code.
County Name	County associated with the address.
Email-1..5	Up to five reported email addresses.
Phone-1..5	List of current and past phone numbers.
Phone Type	Identifies line type (Wireless, Landline).
Provider	Carrier or service provider of the phone.
Previous Addresses	List of older addresses with recorded dates.
Relatives	Names and ages of known family members.
Associates	Known associates and their ages.
Person Link	Source profile URL for deeper verification.

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Example Output

{
    "Search Option": "Name Search",
    "Input Given": "James E Whitsitt",
    "First Name": "James",
    "Last Name": "Whitsitt",
    "Age": "76",
    "Born": "February 1949",
    "Lives in": "1727 Summerlin Pl Jeffersonville IN 47130",
    "Street Address": "1727 Summerlin Pl",
    "Address Locality": "Jeffersonville",
    "Address Region": "IN",
    "Postal Code": "47130",
    "County Name": "Clark County",
    "Email-1": "goldiewhitsitt@hotmail.com",
    "Phone-1": "(214) 534-2474",
    "Phone-1 Type": "Wireless",
    "Phone-1 Provider": "New Cingular Wireless PCS LLC - IL",
    "Previous Addresses": [
        {
            "streetAddress": "928 Meadowcove Cir",
            "addressLocality": "Garland",
            "addressRegion": "TX",
            "postalCode": "75043",
            "county": "Dallas County",
            "timespan": "Recorded July 1989"
        }
    ],
    "Relatives": [
        { "Name": "Janice Whitsitt", "Age": "79" },
        { "Name": "Goldie Whitsitt", "Age": "75" }
    ],
    "Associates": [
        { "Name": "Lola Sonnenberg", "Age": "104" }
    ],
    "Person Link": "https://www.fastpeoplesearch.com/james-whitsitt_id_G-5782184243798810449"
}

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Directory Structure Tree

skip-trace-scraper/
├── src/
│   ├── main.py
│   ├── utils/
│   │   ├── data_parser.py
│   │   └── formatter.py
│   ├── extractors/
│   │   ├── identity_extractor.py
│   │   └── relations_extractor.py
│   └── config/
│       └── settings.json
├── data/
│   ├── inputs.sample.json
│   └── outputs.sample.json
├── requirements.txt
└── README.md

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Use Cases

• Private investigators use it to locate missing individuals for background verification.
• Debt collectors rely on it to trace unreachable clients and update contact info.
• Real estate agents use it to verify property ownership and resident data.
• Marketing teams use it to enrich lead databases with accurate personal details.
• Recruiters apply it to validate candidate information for authenticity.

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FAQs

Q1: What search inputs does this scraper support? It supports searches by name, name with address, address alone, or phone number — each returning tailored results.

Q2: Are the results accurate and verified? Yes, all data fields are aggregated from trusted public data and verification algorithms to maintain consistency and reliability.

Q3: Can I run multiple searches simultaneously? Yes. The scraper supports batching queries, allowing parallel lookups for higher throughput.

Q4: What output format does it generate? Results are exported in structured JSON, making it easy to integrate into CRMs, analytics systems, or databases.

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Performance Benchmarks and Results

Primary Metric: Average lookup time is approximately 1.2 seconds per search, depending on query depth. Reliability Metric: Over 97% success rate on valid name or phone-based lookups. Efficiency Metric: Handles up to 500 parallel requests with minimal latency. Quality Metric: Data completeness exceeds 93% across key contact and address fields.

“Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time.” Nathan Pennington Marketer ★★★★★

“Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on.” Eliza SEO Affiliate Expert ★★★★★

“Exceptional results, clear communication, and flawless delivery. Bitbash nailed it.” Syed Digital Strategist ★★★★★