AI Insights: The New Era of Talent Acquisition
We've implemented AI Insights for a deep analysis of candidate experience, uncovering real behavioral signals and their ability to build products. This changes the approach to talent acquisition, focusing on results rather than a list of skills.
Authors: slavb18
The introduction of AI Insights into candidate experience analysis radically transforms the approach to talent acquisition, making the process more precise and efficient. We have started extracting AI Insights from candidate experience, and this is profoundly transforming the hiring process.
💡 The Old Approach
Previously, the hiring process often boiled down to:
- stack (React, Python, PostgreSQL)
- list of skills
- years of experience
...and an attempt to guess whether someone "would fit or not."
💡 The Problem
Resumes did not answer the main question:
does this person actually build products - or just complete tasks?
⚙️ What We Did
We added a layer of structured experience analysis.
Now we break down each candidate's experience into:
- stack → all technologies (including AI tools like Cursor, Claude, etc.)
- skills → actual practices (product discovery, A/B testing, architecture, management)
- achievements → specific measurable results
- project context → what exactly the person built
And most importantly:
💡 We Extract AI Insights
We are not looking for "keywords," but signals of how a person actually works:
- AI-native: Uses AI as a multiplier, not a toy
- Product Engineer: Thinks in metrics, not tasks
- MVP Builder: Builds products from scratch in days/weeks
- High Velocity: Works many times faster than the market
- Technical Ownership: Is accountable for the outcome, not just "their part"
- Automation First: Automates everything possible
⚙️ How It Works Under The Hood
We don't just parse text. We:
Decompose experience by structure (technologies, processes, results, team)
Look for behavioral signals
- "built an MVP in 2 weeks"
- "5 experiments per week"
- "reduced churn by 20%"
Formulate AI Insights with explanation
Not just:
AI-native
But:
AI-native - uses Cursor and Claude to multiply development speed
✅ What This Gives Us
Now we see what wasn't visible before:
- two candidates with identical stacks
- but one → a regular developer
- the other → an AI-native product engineer
And the difference between them is x5-x10 in results.
🚨 Most Important
The market is still hiring for:
❌ "5 years of React"
❌ "knowledge of SQL"
When what's needed is:
✅ "will build a product in a week"
✅ "will understand the user and drive to metrics"
⚡ Conclusion
We stop looking for "developers." We start finding:
those who genuinely build products
And AI Insights is what makes them visible.
If you want, I can show real examples where this completely changes candidate selection.