TANVEER HUSSAIN / AI ENGINEER
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Case Study · Lead Intelligence Automation

BYO Lead Scraper

A configurable lead generation pipeline where you bring your own targets and sources. It scrapes, cleans, enriches, and qualifies leads at scale, turning messy web data into clean, scored records, 30,000+ processed. Built solo, end to end.

Role: Sole Engineer & Architect Domain: Lead Gen & Sales Intelligence Scale: 30,000+ records

[ 01 ] The business problem

Building lead lists by hand meant hours of searching, copy-pasting, and cleaning, with duplicates, missing fields, and no consistent way to qualify who was actually worth contacting. It couldn't scale past a few hundred leads.

[ 02 ] The technical solution

One pipeline that takes your target criteria, scrapes the right sources, enriches and scores every lead with an LLM, removes duplicates, and hands back a clean, ready-to-use list, thousands at a time, hands-off.

// architecture

How the pipeline works

1

Bring your own targets

You define the criteria and sources you want leads from. The pipeline takes that as input, no fixed niche, no rebuild per use case.

n8nConfig-DrivenSource Ingestion
2

Structured scraping with Apify

Apify actors pull structured data from directories and listings at volume, with pagination and batching handled automatically.

ApifyWeb ScrapingBatch Processing
3

Open-web extraction with Firecrawl

For sources without a clean structure, Firecrawl crawls and extracts content from arbitrary web pages into usable text and fields.

FirecrawlWeb CrawlingData Extraction
4

LLM enrichment & qualification

DeepSeek cleans, classifies, and scores each lead, normalizing fields and structuring messy raw data into consistent, qualified records.

DeepSeekLLM IntegrationLead ScoringPrompt Engineering
5

Dedup, validate & export

Records are deduplicated, validated, and exported as a clean list to the client's destination of choice, ready to use immediately.

DeduplicationValidationETLExport
{ "targets": [...], "sources": [...] }

You bring the targets, it brings the leads. One config kicks off the entire run, from raw web sources to a clean, scored, deduplicated lead list.

// engineering depth

Hard problems solved

~ scale

30,000+ without breaking

Batched, idempotent processing lets huge runs complete reliably and resume cleanly instead of restarting from zero on a single failure.

~ cost

Budget-aware LLM use

Enrichment is batched and rate-limited so large runs don't burn API budget, the LLM is used where it adds value, not on every raw byte.

~ quality

Dedup & normalization

Messy, inconsistent web data is normalized into a single clean schema, with duplicates removed so the final list is genuinely usable.

~ resilience

Rate-limit & retry handling

Scrapers and APIs fail and throttle constantly. Retry logic and backoff keep the pipeline running through it without manual babysitting.

~ flexibility

Bring-your-own design

Criteria and sources are config, not code, so the same pipeline serves new niches and campaigns without a rebuild.

~ ownership

Solo, full-stack

Scraping, AI enrichment, data layer, and orchestration, designed and shipped end to end by one engineer.

// stack

Built with

Scraping / Extraction
ApifyFirecrawlWeb ScrapingWeb CrawlingData ExtractionPagination Handling
AI / LLM
DeepSeekLLM IntegrationPrompt EngineeringData EnrichmentClassificationLead Scoring
Workflow / Orchestration
n8nWorkflow AutomationEvent-DrivenBatch ProcessingIdempotencyRetry Logic
Data / Backend
PythonJavaScriptJSONData CleaningDeduplicationETL PipelinesSchema Normalization
Reliability
Rate-Limit HandlingBackoffError HandlingValidationMonitoring
Domain
Lead GenerationSales IntelligenceMarketing AutomationData MiningSolo Full-Stack
// outcome

The result

30K+
Lead records processed
Manual → Auto
List building fully automated
1
Config runs the whole pipeline
100%
Designed & built solo, end to end
// let's build

Have a problem that needs a system?

I turn messy business problems into reliable AI systems, scraping, agents, RAG, and automation, designed and shipped solo.

Tanveer Hussain · AI Engineer · Building systems that never sleep.