Job-Post Lead Engine for B2B Sales Teams

Scrapes job postings to detect hiring signals that predict company spending, then alerts sales teams via Slack/CRM.

Build

The core insight is sharp: hiring patterns often precede tool purchases. But execution is hard — you need reliable scraping, accurate classification, and a clear signal-to-noise ratio. The biggest risk is false positives that erode trust. If you can deliver a 10:1 signal-to-noise ratio and integrate seamlessly into existing sales workflows, this could be a sticky product. What has to be true: sales teams must see at least one qualified lead per week from this that they wouldn't have found otherwise.

Quick Metrics

Entry Difficulty

Medium80%

Scraping and NLP needed, but no regulatory hurdles.

Time to MVP

14–28 days

Basic scraper + classifier + Slack bot.

Time to First $

72–120h

Sell pre-built lead lists manually to SDRs.

Opportunity Breakdown

Opportunity

8/10

Strong

Untapped signal in job postings.

Problem

7/10

Meaningful

Sales teams need better lead sources.

Feasibility

8/10

Achievable

Technical challenge is moderate.

Why Now?

Superpowers Unlocked

9/ 10

LLMs make classification easy.

Cultural Tailwinds

7/ 10

Sales teams embrace AI tools.

Blue Ocean Gap

6/ 10

Few competitors use job signals.

Ship Now or Regret Later

5/ 10

Window may close as others catch on.

Creator Economy Boost

3/ 10

Not relevant to creators.

Economic Pressure

8/ 10

Companies hire strategically in downturns.

Heuristic scoring based on model judgment, not factual measurement.

Scorecard

Strength Profile

Demand

8.0/10

Sales teams constantly seek better lead sources.

Problem Severity

7.0/10

Hiring signals are underutilized but valuable.

Monetization Readiness

8.0/10

Sales tools have established budgets.

Competitive Gap

6.0/10

Some intent data tools exist, but job-based angle is novel.

Timing

8.0/10

AI makes scraping and classification easier than ever.

Founder Fit

7.0/10

Needs scraping and NLP skills; doable for a technical founder.

Revenue Criticality

9.0/10

Directly generates sales leads; revenue impact is clear.

Risk Profile

Operational Complexity

Moderate complexity

Scraping maintenance and classification tuning required.

Liquidity Risk

Low risk

Low upfront cost; can sell pre-built leads quickly.

Regulatory Risk

Moderate risk

Scraping public job boards is generally allowed.

Lower values indicate lower risk.

Demand Signals

Sales teams actively search for 'intent data' and 'lead generation tools' on Google.

LinkedIn groups for SDRs frequently discuss lead sourcing challenges.

Existing intent data tools (e.g., ZoomInfo) are expensive, creating demand for cheaper alternatives.

Job boards like LinkedIn and Indeed have millions of postings, a rich data source.

AI-powered classification tools (LLMs) are now accessible, making this feasible.

Companies like Gong and Chorus use conversation data for intent; job data is untapped.

Insights

Hiring a compliance manager often means new regulatory software budget.

Three CS hires in a month signals scaling pain and tool evaluation.

Sales teams already use intent data but job postings are a fresh signal.

Scraping job boards is technically straightforward but requires constant updates.

Classification accuracy is critical; false positives kill trust.

Slack integration reduces friction for sales teams.

Pricing at $199-$499/mo aligns with sales tool budgets.

First customers could be SDR teams in mid-market B2B SaaS.

Risks

Job boards may block scraping; need to rotate IPs or use proxies.

Classification accuracy may be low initially, leading to user distrust.

Sales teams may already have too many tools; adoption friction.

Signal-to-noise ratio: too many alerts may cause users to ignore.

Superpowers

First-mover in job-signal intent data for SMB sales teams.

Low cost to build and iterate due to AI APIs.

Direct integration into existing sales workflows (Slack/CRM).

Potential to expand into predictive analytics for hiring trends.

Made Not Sold