We Built LayersRank Because We Kept Getting Hiring Wrong
And we realized the problem wasn't our judgment — it was that we had no idea how much to trust our own assessments.
The Problem We Faced
Every Founder Knows This Feeling
You interview a candidate. They seem great. Smart answers, good energy, says all the right things.
You hire them.
Three months later, you're wondering what went wrong.
We've been there. Multiple times. As founders building teams across India and working with startups globally, we watched the same pattern repeat:
The interview felt good. The data said nothing.
We'd walk out of interviews with a “feeling” about someone. Maybe they reminded us of a past colleague. Maybe they used the right buzzwords. Maybe we were just tired that day and anyone competent seemed like a relief.
When it worked, we called it “good instincts.” When it failed, we blamed the candidate for “not being a culture fit.”
But here's what we never had: a way to know how confident we should actually be in our assessment.
The Insight
The Problem Isn't Bad Judgment. It's Hidden Uncertainty.
We started asking ourselves a different question. Not “What score should this candidate get?” but “How much should we trust any score we give them?”
Think about it:
- A score of 75 from a clear, detailed, consistent response is very different from…
- A score of 75 from a vague, rambling answer where you're basically guessing
Traditional hiring tools treat both the same. They give you a number and expect you to act on it.
But experienced hiring managers know the difference. They just can't articulate it — and they definitely can't scale it across a team of interviewers.
That's when we found our answer in an unexpected place: fuzzy mathematics. Specifically, a framework called TR-q-ROFNs (Type-Reduced q-Rung Orthopair Fuzzy Numbers) that was designed for complex decision-making in supply chain evaluation — situations where you need to make high-stakes decisions with incomplete information.
Sound familiar?
We adapted this framework for hiring. Not to make scores more “accurate” — but to quantify the uncertainty that's always been there, hiding in plain sight.
The Solution
LayersRank: Scores That Tell You What They Mean
LayersRank is an AI-powered interview platform that evaluates candidates across three dimensions — technical ability, behavioral signals, and contextual fit.
But here's what makes it different:
Every score comes with a confidence level.
When we tell you a candidate scored 78, we also tell you:
- The confidence interval (± 4 points)
- How certain we are (91% confidence)
- Which dimensions are solid and which need verification
When confidence is low, we don't just shrug and give you a number anyway. The system triggers adaptive follow-up questions to dig deeper — automatically clarifying vague responses until we have enough signal to give you a score you can actually trust.
The result: You know when to move fast, and you know when to slow down and verify.
No more treating every assessment as equally reliable. No more gut decisions dressed up as data.
Why “LayersRank”?
The Name
Layers because we evaluate candidates across multiple dimensions — not just technical skills, but how they think, how they communicate, and how they'd fit your specific context.
Rank because ultimately, you need to make a decision. Who moves forward? Who doesn't? We give you defensible rankings backed by transparent logic.
The acronym works too: Logic-Assisted Yield Evaluation & Ranking System.
But mostly, we liked that it captures what we actually do: add layers of rigor to a process that's been running on intuition for too long.
Why The Algorithm?
Built by The Algorithm
LayersRank is a product of The Algorithm, a company focused on applying structured decision-making to messy human problems.
We're not an HR tech company that stumbled into AI. We're a team that's spent years thinking about how to make better decisions under uncertainty — and hiring is one of the most consequential, uncertain decisions a company makes.
Our background:
- Founded in India, serving teams globally
- Research published on SSRN: “Evaluating Recruitment Robustness: A Multi-Dimensional Ranking Framework Using Type-Reduced q-Rung Orthopair Fuzzy Sets”
- Built for the realities of high-growth hiring: speed matters, but so does getting it right
We built LayersRank because we needed it ourselves. Now we're sharing it with teams who feel the same frustration we did.
What We Believe
Our Principles
Uncertainty Isn’t a Bug — It’s Information
Most tools hide uncertainty to seem more confident. We surface it because knowing what you don’t know is half the battle.
AI Should Support Judgment, Not Replace It
We don’t make hiring decisions. You do. Our job is to give you better information, faster — and be honest about where the gaps are.
Bias Hides in “Gut Feelings”
Structured evaluation isn’t about removing humanity from hiring. It’s about making sure the human decision is based on signal, not noise.
Speed and Quality Aren’t Trade-offs
The companies that win talent aren’t choosing between moving fast and hiring well. They’re doing both. That requires better tools, not more process.
Hiring Affects Livelihoods
We take this seriously. Every candidate is a person making career decisions. We build with that weight in mind — no “move fast and break things” when people’s futures are involved.
Indore, India
London, UK
Denver, USA
Ready to Stop Guessing?
See how LayersRank evaluates candidates — with scores you can actually trust.