Vina
The AI mainkick that hires like your best
62 followers
The AI mainkick that hires like your best
62 followers
Vina helps your interviewers with the right questions for all roles and levels; listens to the interviews; scores the applicants on every trait that matters for the job; compares applicants, and recommends strong hires, backed up with reasons
Hiring decisions are among the most critical choices a company makes and also the most quietly biased.
I’ve seen this up close. We all carry internal stories about what makes a “great hire.” Sometimes it’s grit. Sometimes it’s polish. Sometimes, it’s just someone who feels like us. But too often, those instincts overpower actual role-fit.
Vina was born out of this realization. Bias isn’t evil, but it needs guardrails. That’s what Vina does. It brings structure to hiring pipelines. It makes sure every candidate is evaluated for the right reasons, not the familiar ones. Not “who clicks with me,” but “who actually fits this role.”
I’ve made hiring mistakes based on overvaluing a trait I personally respect, only to realize later it wasn’t enough. That one strength blinded me to other gaps. That’s the Halo Effect. And it’s more common than we’d like to admit.
Vina offers a hiring stack that’s not just efficient but fair. It starts from consuming the job description to identify role specific traits, prepares script to drive aligned interviews, does structured evaluations and even compares candidate interviews to find great fit among group of good fits.
If you’re building, scaling, or simply hiring, check out Vina. Would love your thoughts.
We made Vina to scratch our own itch. We wanted to hire the 95th percentile consistently, irrespective of the role or seniority. There wasn't a tool that would methodically crack the traits that matter and the questions that would make the applicants think and give a carefully thought out answer.
We went ahead and built Vina. Every hire we make is now based on the questions that Vina gives us. She also reviews all the interviews and gives her view on whom to hire. We mostly agree with her assessment.
Oh, we are looking for strategic partners in the HR/HRTech domains to take Vina to the market. Hit us up through the contact form on our website (https://moative.com)
Cool.
Any measures built in, to avoid gender/race bias in your tool? When I use it and find good candidates, I just don't want to have to worry about later being accused of using a biased algorithm to make my hire.
Thanks.
Vina doesn't have bias, only preference for your criteria. Evaluation/Selection in Vina is a two phased process - (a) Identify required skills explicitly (b) Rank candidate on identified skill.
This approach leaves the scope for ambiguity in "how candidate was selected" out of the gate by design. Assessment will not have bias for anything which is not part of required skills to get the job done.
This is solving a very real and painful problem. Manual hiring is often inconsistent, interviewers ask different questions, miss key signals, and rely heavily on gut feel. Standardizing interviews and scoring candidates objectively can dramatically improve hiring quality and reduce bias.
@recotap Thank you. This is exactly why we dog-fooded Vina for 6 months.
@recotap Thanks. We found these repeating patterns across companies. Clear example has been how teams change during scaling up workforce. So many hires, so little time, so many competing priorities. Vina brings objectivity to the table in all this.
As a data scientist, I’m genuinely intrigued by how Vina was built. Scoring interviews, comparing candidates, and recommending hires — that’s a massive challenge involving everything from NLP to behavioral modeling. It’s not just about automation, but about making judgment calls that are usually very human and very subjective.
I’d love to know how the team tackled things like bias, model interpretability, and adapting to different roles or seniority levels. The whole thing seems like a really thoughtful application of AI — one that’s actually useful and grounded in real-world hiring pain points.
Credit to the team for building something that doesn’t just work, but clearly understands the space it’s trying to improve.
@anisha_mittal1 For NLP, LLMs work great for us. Though lot of prompt engineering effort went in to get exactly what we need.
On the nuances of hiring, we've tried to capture the subjectivity of these choices in a battle tested objective framework of Radford Levels. We process the job description to identify skills on axises like weather a person requires to know how to get the job done (execution) or also needs to manage a team of individuals to drive the deliverables (management).
The requirements goes through a pipeline which is built on top of above, and more, aspects of a role. Once we have that structure in approach, Vian builds path to follow that strucutre.
How does Vina evaluate answers in new or rapidly evolving fields, where it may not have much prior data or established benchmarks?
YouMind
I couldn’t find a pricing page on the website. Is Vina always free to use?
@michelle_thirteen We are experimenting on pricing and hence no mention yet. We may price per interview.
YouMind
@moative_ashutosh got it