Generation and segmentation of the database of drivers and transportation companies in the USA

👋 Hello. My portfolio -
Freelancehunt

I have experience in automating data collection and processing, lead generation, and building AI processes for data enrichment and segmentation.

💪 I can implement a system that:
— collects data on drivers and transportation companies in the USA
— enriches it through AI and available sources
— cleans and structures the information
… — segments leads according to your criteria
— forms a ready database for CRM and further team work

Stack: Python, parsing, API integrations, AI tools.

⏰ In terms of deadlines — the estimate is about 7 days for an MVP with working logic for collection and segmentation.

We can discuss the details to better understand the sources and structure of the data before starting.

🚀 I take every project seriously and work towards results. I don’t just close tasks, but bring the work to a state where the product looks high-quality, professional, and instills trust in clients.

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Hello, Evgeny! This is Nina, the manager of the automation and development team.
Your task is completely clear to us. A raw scraped table from the internet will not work here - in the transportation sector of the USA, it is critically important to build a repeatable, automated process where parsers collect a data array, and artificial intelligence acts as an intelligent filter (checking compliance with criteria, cleaning duplicates, classifying by priority, and structuring leads for CRM).

Our Senior Developer Valentin is responsible for data architecture, parsing, and AI integrations (working with Python / API / LLM).
Here’s how we envision the implementation and architecture of the process:
Direction: Transportation companies (B2B)
Sources: We are building a legal and scalable data collection directly from the official US registry — FMCSA SAFER / Census (scraping DOT/MC numbers, geography, type of business, region, and exact fleet size).
Enrichment: We enrich raw data through integrations with professional B2B platforms (Apollo, Clay, LinkedIn API) to find direct contacts of decision-makers (LPR).
Direction: Drivers (HR / Recruitment)
… Sources: Monitoring and automatic data collection from open profile databases, recruiting platforms, local classifieds, and thematic groups.
Segmentation through AI: We connect OpenAI API for analyzing unstructured text from resumes/profiles. The AI will automatically classify drivers by license type (CDL Class A/B), experience, state, availability, and assign a priority (scoring) to the lead for your recruitment team.
Our experience and tools:
Valentin regularly creates automated data funnels. We use Python (Scrapy, Selenium/Playwright, Asyncio) for custom scraping without blocks, n8n/Make for process linking, OpenAI API for segmentation, and we structure the final arrays for import into HubSpot/Pipedrive/Zoho.
Cost, timelines, and the first step:
The budget you specified of 7777 UAH is fully sufficient for the First Step (3–4 days):
We will develop the exact logic for segmentation, approve the structure of the final database with you, set up a pilot data collection script, and provide you with a test, fully enriched AI sample for both directions (100–200 leads) for quality verification. After that, we will agree on scaling the system to full volume.
Evgeny, please let us know which CRM system your team plans to integrate the finished database into, and whether you already have any software set up for further outreach (Email/SMS/Cold calls)?
Let’s discuss the details in the chat!

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👋 Good day, the best and largest projects —> Freelancehunt

I understand the task — it's not just about collecting a database, but building an AI process for searching, enriching, and segmenting leads (drivers + transport companies in the USA).

I have done similar tasks in data automation and lead generation — where it's important not to collect data manually, but to build a system that finds, cleans, and structures it for CRM on its own.

🔥 How I approach the task:
• I build the logic for searching and data sources
• I set up collection and enrichment through AI/parsing
… • I segment leads according to specified criteria
• I bring everything into a structure for CRM

⏳ Deadline: ~7 days

⚡️ At the start, I will provide a clear process scheme to agree on the approach immediately and then implement it quickly.

🤝 I am ready to discuss the details and get started.

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Hello, Evgeny!

I work specifically with AI automation, data enrichment, and lead-gen pipelines, so I see the task clearly: not a one-time scraped table, but a repeatable system where automation collects raw data, and AI acts as a filter (segment matching, lead scoring, structuring for CRM).

I want to raise one point immediately, as it directly affects whether the database will actually be suitable for an American company — and most contractors overlook this:

Your two areas have different legal statuses and require different sources:

1. Transportation companies (B2B) — completely clean. The FMCSA SAFER/Census registry is public (DOT/MC numbers, fleet size, type of transportation, region, contacts), plus enrichment of B2B contacts through Apollo/Clay. Reliable, legal, scalable.
…
2. Drivers (individuals) — here, caution is needed. Personal CDL data from federal registries (FMCSA Clearinghouse) is NOT public — it is protected by the Privacy Act, scraping it is illegal. The legal way is to use drivers who have publicly posted their resumes and contacts on job boards while considering CCPA requirements. I build the process so that your team does not rely on a legally risky database.

How I see the process: source identification → automatic collection (Python scrapers + API, proxies, deduplication already at the collection stage) → AI enrichment and cleaning → segmentation according to your criteria (license type, state, experience / fleet size, region, business type) → prioritization (hot/warm/cold) → structured export to CSV or ready for CRM.

Tools: Python (Scrapy, Selenium), FMCSA SAFER, Apollo/Clay, OpenAI API for classification and deduplication, Make/n8n for automation.

I would suggest the first step as follows: a short pilot (5-7 days) — we define segmentation criteria, check sources for both areas, build the database structure, and provide a pilot sample so you can see the actual data quality before scaling.

Two questions: which states and types of drivers are prioritized at the start? And what CRM does your team use?

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Good day! We have experience in building lead generation, data enrichment, and AI automation systems for B2B companies and recruitment. For your task, we can create a process that will allow:
• to automatically find drivers and transportation companies in the USA;
• to collect and enrich data from open sources and professional databases;
• to segment contacts by specified criteria;
• to use AI for qualifying and prioritizing leads;
• to form a ready data structure for CRM and further outreach campaigns.
We work with tools like Apollo, Clay, OpenAI, Google Maps, LinkedIn, various APIs, and automation systems (Make, n8n).
In the first stage, we propose to conduct an audit of data sources, define segmentation criteria, and build an MVP process for collecting and enriching the database.
We are ready to discuss the details, propose a solution architecture, and estimate timelines and budget after clarifying the requirements.

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Hello, Yevhen! The description of your project completely matches my profile. The task of building a systematic lead generation and data enrichment process for the U.S. transportation sector is absolutely clear to me. I understand that you need not a one-time manual export, but a scalable logic where automation collects a data array, and artificial intelligence acts as an intelligent filter (analyzing compliance with criteria, classifying leads by priority, and structuring contacts).
I have practical experience in developing parsers and automation systems in Python (using Selenium, Scrapy, and API integrations), as well as in connecting AI tools (OpenAI API / LLM) for analyzing unstructured text, scoring leads, and automatic segmentation.
Here’s how I propose to implement this task:
Stage 1: Identifying sources and search methodology
For drivers: setting up parsing/monitoring of open databases, recruiting platforms, thematic bulletin boards, and social networks (LinkedIn, Facebook groups). Collecting information about the type of license (CDL Class A/B), geography, and experience.
For transportation companies: working with registries (e.g., FMCSA/DOT databases, if applicable), Google Maps API, LinkedIn, and industry directories. Collecting data on fleet size, type of transportation, and contacts of decision-makers (LPR).
Stage 2: Automated collection (Scraping & API)
Writing scripts for fast, streaming collection of raw data without blocks (using proxies, proper delays, and simulating user actions).
Stage 3: AI Integration and Data Enrichment
… Connecting AI to clean the database from duplicates and "junk."
Setting up an AI model for analyzing profiles/texts: automatically determining lead priority (Hot/Warm/Cold), classifying by segments, and checking for compliance with your customer profile.
Stage 4: Structuring and preparing for CRM
Forming the final architecture of the database (mainly in integrated format or ready CSV/XLSX files, fully validated and cleaned for easy import into any CRM).
Expected results you will receive:
A fully prepared and segmented test database of drivers and companies.
A transparent search methodology and step-by-step scheme for data collection and enrichment.
A set of recommendations for complete automation of this "turnkey" process for regular use by your HR team.

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If you have experience with similar tasks, please write:

what exactly you did;

what tools you used;

how you see the process;

what results you can deliver;
…
what first step you would suggest to start.

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Hello! I have implemented similar projects in AI-driven lead generation, where I set up data collection, AI qualification of leads, segmentation, and information transfer to CRM through Make.com, ChatGPT, HubSpot, Zoho CRM, and Pipedrive.

I see the process as an automated pipeline: data search → enrichment through AI → segmentation by specified criteria → lead prioritization → transfer to CRM for team action.

As the first step, I would suggest defining segmentation criteria for drivers and companies and building a data structure tailored to your recruitment and sales process.

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Good afternoon. I am ready to complete this project; I have extensive experience in developing various applications.

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Hello, Evgeny! I have done something similar: scraping → normalization → AI enrichment → scoring/segmentation → structured database. I will propose a process: sources on transport companies and drivers in the USA, gathering contacts, AI classification for your segment, lead prioritization, export to Sheets/CRM. The price in the bid is conditional — negotiable based on the final volume.

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I already have a practically ready solution for searching, enriching, and scoring leads, which can be quickly adapted for drivers and transportation companies in the USA. I'm ready to discuss it here; I'm available. ))

For similar tasks, we have done data collection and enrichment, lead prioritization, preparation of the structure for CRM, and a semi-automatic process for data quality checking.

Tools include parsing open sources, Apollo and similar tools, Google Sheets or CRM, enrichment through AI, duplicate checking, and classification by rules and priorities.

I would start with a short phase of 5-7 days - to gather methodology, test 2 search directions, create a working database structure, and provide a pilot sample.

- Drivers - license, state, experience, availability, type of transportation, contacts, source, quality assessment
… - Transportation companies - size, region, type of business, hiring signs, contacts, priority, sales notes
- Output - table or CRM structure, segmentation logic, enrichment scheme, automation recommendations

The final result at the first stage is not just a table, but a repeatable process that the team can use further.

Look, there’s a nuance - for the USA, it’s important to agree in advance on acceptable sources and data processing rules, so as not to collect junk in a nice table, but to obtain a database suitable for sales and recruiting.

I would like to clarify 2 points:
- Which states and types of drivers are a priority at the start?
- What CRM or table is currently used by the team?

Relevant examples from Ingello:
- https://business.ingello.com/vorfahr - automation and AI logic for data search and processing
- https://business.ingello.com/fractal - agent approach and automation of business processes
- https://systems-fl.ingello.com - a brief overview of our team and approach to systematic automation

Let’s not complicate things - I would start with a pilot, check the quality of sources, and then scale up collection and segmentation. A good process is visible through data, not through presentation. =)

Estimated cost for the initial phase - 28,000 UAH, duration - 7 days.

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Good day.
Our team has many years of experience in developing ERP, CRM, CMS, and specialized software for businesses. We create effective digital solutions that help automate processes, increase productivity, and scale companies.

We work with modern technologies — from bots and scripts to AI agents and analytical systems. We develop websites of varying complexity. Our portfolio includes implemented ERP solutions for the hotel business, as well as for companies engaged in the import and sale of goods, and our own product XFitness — an ERP system created specifically for fitness clubs.

We are ready to implement your project and offer the best solution tailored to your needs.
Our portfolio: Freelancehunt

We specialize in the following areas:
… - Development of ERP Systems
- Development of CRM Systems
- Development of Websites of any complexity
- Development of CMS Systems
- Website Support
- OpenCart Development
- OpenCart Support
- OpenCart Modification
- OpenCart Improvement
- WordPress Development
- WordPress Support
- WordPress Modification
- WordPress Improvement
- ECommerce Development
- ECommerce Support
- ECommerce Modification
- ECommerce Improvement
- Web Application Development
- 1C Server Support
- Web Server Support
- Mobile Application Development
- Data Parsing
- Bot Development
- AI Agent Development

and on the following technologies:
- Python
- PHP
- Laravel
- Symfony
- Yii2
- JS
- NodeJS
- jQuery
- TypeScript
- MySQL
- HTML
- CSS
- Vue
- Nuxt.js
- React
- React Native
- C++

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To start, I would suggest defining data sources, segmentation criteria, and the target structure of the database, after which we can collect a pilot process on a limited sample and scale it to the full volume.

I have implemented projects for data collection and processing, automated analytics, AI classification, and structuring large amounts of information.

I am ready to begin implementation today.

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Hello! Ready to collaborate. I offer a loyal price and quality work. Write to me.

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