€1.4M pipeline without advertising

Challenge

The HR tech startup had a good product, but couldn't stand out in the crowded market of HR tools. Traditional campaigns (massive advertising, generic emails) were not yielding results and were too expensive. The small marketing budget could not compete with the big players.

Main Challenges

• Crowded market - 90+ new HR tech startups annually just in EMEA

• Small marketing budget - they couldn't compete with large campaigns

• Low trust by companies in 'another HR solution' - HR fatigue

• 6-month sales cycle - too long to maintain cash flow

• They don't know how to identify companies with real HR problems they are solving

Solution

We analyzed 2.4 million words from 50+ Slack/Discord HR communities, Reddit forums, 35 podcasts, and 700 blogs using AI. GPT-4 identified the top 12 HR pain points and the Neo4j knowledge graph linked companies with their issues and our solutions. We reached out to 120 companies with hyper-personalized offers.

How We Addressed It

We collected 2.4 million words of text from Slack and Discord HR communities, Reddit HR forums, 35 HR podcasts, and 700+ HR blogs. AI systematically read all the content and searched for pain points that companies are facing.

Semantic analysis through LangChain + GPT-4 embeddings identified the top 12 HR pain points (e.g., 'slow hiring', 'bad culture fit', 'high turnover'). We linked each pain point with specific companies that mentioned it.

We created a Neo4j knowledge graph that connects companies, their problems, our product, and benefits. The system automatically suggested which companies to contact and with what messaging based on their specific pains.

GPT-4 generated hyper-personalized emails and LinkedIn messages for each company. The messages were not generic sales pitches - they quoted a specific pain point from their podcast or Slack conversation.

Trigger events monitoring tracked LinkedIn, Crunchbase, and PR news. If a company received an investment round or changed its HR manager, AI automatically added them to the outreach list and reached out within 1 week.

Technologies used: GPT-4 Embeddings, LangChain framework, Neo4j Knowledge Graph, Firestore Vector DB, HDBSCAN clustering

Results

1. €1.4M pipeline value: CLV for 24 pilot contracts

2. Sales cycle of 3.4 months: Almost halved

3. High conversion rate: 24 out of 120 contacted signed the pilot

Wow Factor

AI analyzed 120 hours of audio from HR podcasts using OpenAI Whisper transcription and identified the 12 most common HR issues. The knowledge graph in Neo4j linked these issues with 120 companies and our product. HR managers were pleasantly surprised that the email specifically mentioned their unique problems.