Reddit Lead Capture: Automating High-Intent Sales Discovery with Agentic AI Scrapers

THWORKS built Reddit Lead Capture — a multi-tenant SaaS platform that automates high-intent lead discovery on Reddit. Using a dual-layer scraping strategy (RSS + JSON fallback) with centralized caching, the system monitors 1,000+ subreddits hourly without expensive API credentials. GPT-4 scores each lead on a 0-100 relevance scale based on buying intent, urgency, and engagement velocity, then delivers instant contextual alerts with AI-drafted reply suggestions via Telegram. Architecture achieves 90% API call reduction through 'fetch once, match many' design.

A B2B SaaS lead-generation team

75%

Infrastructure Savings

2min

User Onboarding

5K+

Daily Leads Captured

Results

Results: From Manual Scrolling to Automated AI-Scored Alerts

Before

Manual Ctrl+F scrolling through Reddit. 24-hour lead discovery delay. High risk of API rate limit bans. No scoring or prioritization. Duplicate searches across team members.

After

Instant Telegram alerts with AI relevance scores and deep links. GPT-4 generated reply drafts. 90% fewer API calls via centralized caching. 2-minute user onboarding. 5,000+ leads captured daily across all tenants.

75%

Infrastructure Savings

Prospecting infrastructure cost cut against the prior paid tooling

2min

User Onboarding

A new tenant is watching subreddits within a couple of minutes

5K+

Daily Leads Captured

Scored leads surfaced each day across 1,000+ subreddits

Challenge

The Challenge: 10+ Hours Weekly Wasted on Manual Reddit Prospecting

Sales teams and agencies spent 10+ hours weekly manually scrolling subreddits for prospects — using browser search to find keywords, copying posts into spreadsheets, and losing track of which threads they'd already checked. Reddit's restrictive API rate limits made existing tools expensive and unreliable, while the high noise-to-signal ratio meant 80% of flagged posts were irrelevant casual mentions, not genuine buying signals.

In 2026 B2B sales, being the first responder to a Reddit query is the single biggest conversion predictor — but manual monitoring doesn't scale. The goal was a multi-tenant SaaS serving thousands of concurrent users while keeping infrastructure costs under $200/month through architectural efficiency, not hardware scaling.

Solution

Our Solution: Centralized Discovery Architecture with AI Lead Scoring

We designed a 'Centralized Discovery' architecture. Instead of scraping per user, the system fetches each subreddit once per cycle and matches the payload against a global keyword manifest across all tenant configurations. This means 1,000 users monitoring 'r/SaaS' generate a single scraping pass — reducing API footprint by 90% compared to per-user architectures.

For 100% reliability independent of Reddit's API pricing changes, we implemented dual-failover scraping: RSS feeds as primary data source with Reddit JSON endpoints as fallback. This 'API-less' approach ensures the platform remains operational regardless of Reddit's monetization decisions. GPT-4 then scores each matched post using a weighted model combining semantic intent analysis, post growth rate, and comment velocity.

Calls we would still defend

  • AI Engagement Intelligence — GPT-4 calculates 'Weighted Priority Scores' (0-100) analyzing buying intent keywords, question urgency patterns, post growth rate, and comment velocity — filtering out 80% of noise that manual monitoring misses.

  • Multi-Gateway Payment Routing — Intelligent billing layer toggling between Stripe (Global) and Razorpay (APAC) based on user IP geolocation to optimize checkout conversion rates across markets.

  • Single-Pass Cron Pipeline — Unified hourly worker handling scraping, keyword matching, lead scoring, and Telegram broadcasting in one cycle — eliminating the need for separate microservices and reducing infrastructure costs 75%.

FAQ

The questions this one raises

What was actually built, the constraints it had to meet, and what we would do differently.

  • We use a 'fetch once, match many' centralized architecture. Each subreddit is scraped once per cycle and matched against all user keywords globally — reducing outgoing requests by 90%. The dual RSS/JSON fallback strategy operates independently of Reddit's official API, ensuring 99.8% uptime regardless of API policy changes.

  • GPT-4o achieves 94% accuracy in distinguishing high-intent buying signals from casual mentions. The scoring model weighs semantic intent (40%), engagement velocity (30%), post recency (20%), and author history (10%) to produce a 0-100 priority score. Users typically focus only on 80+ scores.

  • Yes. Users define custom watchlists of subreddits paired with boolean keyword rules (AND/OR/NOT). For example: monitor r/SaaS for posts containing 'looking for' AND 'CRM' but NOT 'free.' The system also supports negative keywords to filter out noise.

  • Each alert contains: the post title, AI relevance score (0-100), matched keywords highlighted, a direct link to the Reddit thread, and a GPT-4 generated contextual reply draft. Users can engage directly from the notification without opening the dashboard.

  • The centralized scraping model means adding 1,000 new users monitoring the same subreddit costs zero additional API calls. Combined with the single-pass cron pipeline (scrape → match → score → notify in one cycle), infrastructure runs under $200/month serving thousands of concurrent tenants.

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