AI lead generation system

very many cases, we can do it, Forbes rated it)
3 days and it's ready! contact us

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Good day.

We will create a system as a separate modular product: lead collection from sources, AI filtering according to GTM logic, scoring, deduplication, mailing queues, follow-up, warm-up, and analytics on opens, responses, and deliverability.

So, it would be like this: for 700 EUR, I would complete the first working stage - architecture, MVP core on a server 24/7, one or two data sources, one workspace, basic scoring, email sending, and reports. Then we can scale to multi-project and new sources without rewriting the core.

I would like to clarify two things.
Which source is more important for the first MVP - Indeed, LinkedIn, company websites, or ready-made databases?
Which email providers do you plan to use for outreach and warm-up?
…
Similar cases in logic:
https://business.ingello.com/vorfahr - automation and AI logic for a complex workflow.
https://business.ingello.com/fractal - modular architecture of agency processes.

Our profile on systems and automation for FLH:
https://systems-fl.ingello.com

The timeframe for the first stage is about 14 days.
If you need not just a prototype, but the entire system with multi-workspace, analytics, warm-up, and scaling, I would divide it into stages, because otherwise it would not be engineering, but an attempt to build a rocket from a paperclip. ))

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Good day. I am implementing a modular system for continuous data collection, filtering, and automated distribution based on Nodejs and RabbitMQ. I am ready to start now. I would appreciate the opportunity to collaborate.

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Hello!

Your task of creating an Enterprise AI-driven lead generation system with a multi-project architecture is 100% our profile. At the venture studio Lumvex, we specialize in developing such scalable AI agents and end-to-end automation systems for the B2B market.

We fully understand the difference between a simple script and a flexible modular platform that must operate seamlessly on a server 24/7 across different niches (Staffing, E-commerce, Logistics, etc.).

Here’s how we envision the technical architecture of such a system:

Modular Collection Layer (Scraping & Data Ingestion): Creation of independent connectors (modules) for each source (Indeed, LinkedIn API, Web Scraping) with a unified output format (JSON), so that new sources can be added without rewriting the core.
…
AI-Scoring & Filtering (LLM Layer): Integration of models (OpenAI/Claude) through asynchronous queues for analyzing job postings/websites for compliance with your GTM strategy and custom lead scoring.

Anti-Duplication and Enrichment: A centralized database (e.g., PostgreSQL) with smart deduplication logic (by domains, names, companies) and integration with email search services.

Outreach & Infrastructure: Integration with SMTP/IMAP or specialized APIs (Instantly / Lemlist) for managing warm-up, automated sequences, and tracking analytics (Open/Reply rates).

Multi-Workspace: Separation of data streams and settings for different projects through isolated configurations on the backend.

Our stack for implementation: Node.js/Python, Docker (for 24/7 operation on the server), PostgreSQL/Redis (queues and logic), LLM APIs/LangChain, Make.com (for rapid prototyping of individual connections).

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I worked on UVWeb (https://ou-uv.com) — a B2B system on Flask/Python with automated data flows, REST API integrations, and event-driven logic — exactly the architectural pattern needed for a multi-module AI lead generation system.

Key aspects of this project include a modular architecture with interchangeable data sources and isolated workspaces per project — so that the system for recruitment, B2B, logistics, and e-commerce operates independently on a single infrastructure. Email warm-up and anti-spam is a separate layer requiring configuration of IP/domain rotation to maintain deliverability after a few weeks.

What I will do:

- Data scraping: Indeed, LinkedIn, company websites, databases — modular connectors with anti-blocking support
- AI filtering and lead scoring: data analysis, filtering by GTM criteria, finding corporate contacts
- Automatic email outreach: follow-up sequences, email warm-up, inbox rotation, anti-duplicates
… - Multi-workspace architecture: isolated projects (recruitment, B2B, logistics, e-commerce) on one platform
- Analytics: open rate, reply rate, deliverability, reporting dashboard
- 24/7 deployment on a server with scalability options

--- OPTIONS ---

- Option A (MVP): 700 EUR (7 days) — basic pipeline: scraping 2-3 sources, AI scoring, outreach with follow-up, 1 workspace
- Option B (Standard): 1120 EUR (11 days) — like A + multi-workspace, email warm-up, anti-spam rotation, analytics dashboard — best scope/price ratio
- Option C (Premium): 1460 EUR (14 days) — like B + pluggable connectors for new sources, advanced AI scoring with custom models, technical documentation

Delivery time: 7 days (MVP). To start: specification of data sources and scoring criteria per workspace, access to the server or cloud preference (VPS/AWS/GCP).

Portfolio:

- https://ou-uv.com — B2B system with data flow automation, REST API, event-driven architecture
- https://poseidon.codezerogroup.com — enterprise web app, external integrations, multi-layer architecture
- https://nyxvx.com — custom platform, process automation, API integrations

8 years in automation and integration of AI systems — from simple scripts to complex pipelines with AI scoring, email outreach, and multi-workspace architecture.

Write to me — I will discuss the architecture of workspaces and the warm-up strategy before starting.

Since I am new to the freelancehunt service and want to quickly gain a few initial projects for my portfolio, I am offering a 15% discount for the first 5 clients.

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Good day!

I have reviewed the description — the task looks interesting and quite feasible with the right architecture. I have experience in developing automation systems, AI integrations, and backend services with long-running workflows, so I understand how to approach building such a platform with scalability and future expansion in mind.

The logic with a multi-project/workspace architecture, modular approach, and the ability to connect new data sources is the right direction for such systems. It would also be wise to think about separate services for scraping/data collection, AI processing, lead scoring, outreach orchestration, analytics, and an anti-duplicate layer, so that the system does not turn into a monolith as it grows.

For the MVP, a stable base can be assembled with a gradual expansion of workflows, AI logic, and outreach channels. It is also important to consider deliverability, warm-up, and limits of email providers, as this often becomes a critical point in such systems.

I am ready to discuss the technical specifications, roadmap, stack, and suggest implementation options based on your budget and priorities.

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Parsing LinkedIn and Google Maps, then running through filters using AI, feel free to contact me, I will be happy to help.

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Good day!
Two clarifications to provide a specific quote: Is AI filtering prompt rules for each workspace or a trained scoring based on the history of past campaigns? And for LinkedIn - is it through the Sales Navigator API or through third-party parsers (there are different risks regarding bans and rate limits)?
I built a similar system - for the jewelry niche, I parsed databases of stores across Europe and the USA, AI determines the country/language and writes a letter in the local language, then an autonomous outreach queue with follow-ups. Your architecture is similar: modular Python backend, workers for sources, multi-tenant database.
From a critical standpoint - a global anti-duplicate system, not per workspace (otherwise one company will be bombarded from three niches at once). And warm-up on separate domains for each workspace, otherwise staffing and B2B will negatively impact each other's deliverability.
Please send a detailed technical specification or roadmap - I will look at the volume.

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Welcome!

The Devoxen team specializes in developing CRM, automation, and lead generation systems, so the tasks are well known to us.

We can implement:
- collection and processing of leads from various sources;
- automation of the funnel and distribution of applications;
- integrations with CRM, Telegram, email, API services;
- a dashboard for managers and administration;
… - analytics, statuses, filters, and logging;
- a scalable backend architecture for further development.

We work with both MVPs and full-fledged SaaS solutions. We can also assist with the logic, structure, and technical aspects of the project if everything is not yet finalized.

We are ready to discuss the details, stack, and offer the optimal implementation option for your budget and goals. After launch, we provide a guarantee and support.

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Hello, I am interested in the project and would be happy to discuss it in more detail and complete it. I have experience in contact scraping.

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Hi, Vadims Mandrikovs

I can develop the AI driven lead generation and outreach automation system. I have experience building automation platforms, API integrations, email automation systems, and multi workspace architectures.

I understand the system concept: automated data collection from multiple sources (Indeed, LinkedIn, websites, databases), AI analysis and filtering based on GTM logic, contact discovery, automated email outreach with follow-up sequences, and multi-project architecture supporting different niches simultaneously (staffing, B2B, e-commerce, logistics, etc.).

Technology approach: Node.js backend for automation workflows running 24/7 on server, PostgreSQL for multi-workspace data architecture with project isolation, AI integration using OpenAI or similar for lead filtering and scoring, email automation with SMTP rotation and warm-up system, API integrations for data sources (Indeed API, LinkedIn scraping via Puppeteer, web scraping), modular plugin architecture for adding new data sources, queue system with Redis for handling multiple concurrent workflows, analytics dashboard tracking opens, replies, deliverability rates, and anti-duplicate logic across projects.

The system will support easy addition of new workflows, data source modules, and AI filtering rules without core code changes. Email warm-up will gradually increase sending volume per domain to maintain deliverability.
…
I am interested in discussing detailed technical specification, operational logic, architecture design, and development roadmap to ensure the system meets all requirements and scales effectively.

Available immediately for detailed discussion.

Thanks, Youssef

Hello,

I built a similar modular lead generation system with AI scoring, LinkedIn/Indeed parsing, email outreach, and warm-up. I know where things usually break: deduplication between workspaces, deliverability in parallel niches, and AI filters that cannot take into account the GTM context of a specific project.

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I approach such projects as the creation of a complete internal ecosystem for lead generation, rather than a set of separate scripts. As a result, you will receive a modular platform architecture where each component can be scaled, refined, and connected independently of the others. This is especially important for your tasks, as today the system works for staffing and subcontracting, and tomorrow it can be used for logistics, suppliers, or e-commerce without completely rewriting the logic. I also pay great attention to the stability of the infrastructure, anti-spam logic, task queues, AI filtering, and the quality of outbound communication, because these elements determine the real effectiveness of such systems.

For implementation, I propose using Python + FastAPI or Node.js/NestJS for the backend, PostgreSQL for data storage, Redis and task queues for asynchronous processing, Docker for containerization, AI integration through OpenAI API/LLM models, as well as a scalable scraping architecture with the ability to connect new data sources. The outreach system can be implemented with automatic follow-up sequences, email warm-up, tracking opens/replies, and anti-duplicate logic. Additionally, AI lead scoring and GTM filtering can be implemented so that the system automatically selects the most relevant leads. As a result, you will receive not just an automation tool, but a flexible lead generation platform ready to scale for different business tasks.

Work plan:

1. Analysis of business logic, GTM scenarios, and system roadmap.
2. Design of modular multi-workspace architecture.
3. Setup of backend infrastructure and server environment.
… 4. Implementation of data collection layer from various sources.
5. Development of AI filtering and lead scoring system.
6. Creation of anti-duplicate and validation logic.
7. Implementation of automated outreach system via email.
8. Setup of follow-up sequences and email warm-up.
9. Development of analytics dashboard for opens, replies, and deliverability.
10. Connection of multi-project workflow management.
11. Optimization of scalability and stability for 24/7 operation.
12. Testing of the system and preparation for scaling.

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Good day! I develop custom Python solutions: data analysis, process automation, scripts, parsers, ETL pipelines. The cost starts from 4000 UAH. Details: www.engine-web.od.ua/

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I have experience in developing AI automation systems with parsing, data processing, and multi-step workflows, including integrations with LLM, email outreach, and custom filtering logic. Such a project is better built as a modular backend platform with workers, queues, and AI scoring, so it can be scaled for different scenarios — from lead generation to staffing and suppliers. I can help design the architecture and build an MVP that can be easily expanded for new sources and workflows.

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

Feel free to write :)

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Good day!

I am an automation engineer with experience in creating AI-driven lead generation systems. I am ready to implement a flexible multi-project platform for automatic collection, AI filtering, and outreach 24/7.

Experience: API, databases, web scraping, email automation, scaling. Write to me, and we will discuss the details.

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Hello! After reviewing your project, I am ready to start working on it. Let's discuss the details for the best result.

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Hello!

Ready to take on — AI-driven lead generation systems with automated outreach is our specialty.

Here’s how we envision the implementation:

1. Data collection — modular parsers for Indeed, LinkedIn, websites, and databases. Each source is a separate module, and new sources can be connected without changing the architecture.

2. AI filtering — Claude/GPT analyzes leads based on GTM logic, scores relevance, and filters out non-target contacts. Each project has its own criteria.
…
3. Email outreach — automated follow-up sequences, warm-up system for new domains, analytics for opens and replies.

4. Multi-workspace — each project (staffing, B2B, logistics) has a separate workspace with its own settings, sources, and sequences.

5. Anti-duplicate + analytics — deduplication by domain/email, dashboard with deliverability, opens, replies for each project.

Message us privately — we will provide a free consultation on architecture and roadmap.

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Hello.

I develop bots in NodeJS. I'm ready to take on the task. Write to me, and we will discuss.

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The task is clear: a modular lead collection system from multiple sources, AI scoring, automatic outreach with warm-up and analytics, multi-workspace on a server 24/7.

I will implement it in Python: scrapy/playwright for parsing, OpenAI API for filtering and scoring, SMTP + warm-up logic, queues via Celery, PostgreSQL. The architecture is modular — new sources and workspaces can be connected without rewriting the core.

Budget is 700 EUR — we will discuss the scope for it in a call.

Which data source is a priority for the first MVP: LinkedIn, Indeed, or something else?

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