Hiring Felt Like Sci-Fi Until We Built Tina
In sci-fi movies, I always loved how an AI recruiter instantly picked the perfect candidate for a mission. No bias, no endless waiting — just a fast, precise match.
When I started building companies, hiring felt like the opposite. Endless resumes. Weeks of delays. Burning budgets before finding someone who could actually contribute to growth.
That’s why we built Tina — an AI recruiter that sources, screens, and interviews candidates in 48 hours. If you’re the right fit, you’re hired instantly. If not, it moves on — no wasted time.
We’re early, still learning, but the mission is clear: make hiring feel less like chaos, and more like the future we’ve always imagined.
Having spent some time in the hiring space, I can relate with the problem you’re tackling. Curious—how are you approaching the skill matching challenge? For example, someone experienced in Java could probably ramp up quickly on Python or C#, but would be a mismatch to languages like Erlang or Haskell?
Are you building some kind of skill ontology or transferability model under the hood?
Really like the framing sci-fi analogy makes the problem instantly relatable the 48h turnaround is ambitious but compelling I’m curious how you’re tackling the balance between speed and depth especially for roles where culture fit or nuanced skills matter
Solid write up, feels like you have hit a real pain. How do you keep bias low while moving that fast, and what do the first 48 hours look like for candidates and hiring teams? Can a small team try it on one role to see the time saved before committing?