Lehire is the hiring decision intelligence layer that sits on top of your ATS. It converts interviews, scorecards, and candidate evidence into clear 0 to 100 fit scores so your team chooses with confidence.
Most hiring failures are not sourcing failures. You usually have enough candidates. What you lack is a reliable way to compare them, weigh the evidence, and agree on who to move forward. Hiring decision intelligence is the discipline of turning scattered interview notes, resumes, and opinions into a structured, comparable signal you can act on.
Lehire is built for exactly this moment in the funnel: the decision. Your ATS tracks candidates and your team brings them in. Lehire evaluates them against one rubric per role, produces evidence-backed scores, ranks finalists, and remembers what you learned so the next decision is faster and sharper.
The result is a hiring process where the choice is explainable. Instead of "I have a good feeling about this one," you have a fit score tied to specific evidence, a scorecard every interviewer can see, and a ranking the whole panel can defend to leadership.
What is What is hiring decision intelligence??
Hiring decision intelligence is the practice of applying structured evaluation, evidence-based scoring, and analytics to the moment a team decides whom to hire. It standardizes how candidates are assessed against a single role rubric, converts qualitative interview signal into comparable 0 to 100 fit scores, and ranks finalists so decisions rest on evidence rather than gut feel. It is a decision layer that sits on top of an applicant tracking system, not a replacement for it.
Why hiring decisions break down without intelligence
The typical decision meeting is a negotiation of memories. One interviewer remembers a candidate being sharp on system design, another remembers them stumbling on a behavioral question, and a third was double booked and skimmed the resume on the way in. Each person scores on a different scale, weighs different things, and anchors on whoever spoke last. The loudest voice, not the strongest candidate, often wins.
This is not a people problem, it is a structure problem. Without a shared rubric and a way to capture evidence as it happens, every panel re-litigates the criteria for the role mid-decision. Hiring decision intelligence fixes the inputs: one rubric per role defined before interviews start, scorecards captured against that rubric, and a fit score that reflects the evidence rather than the energy in the room.
Evidence-backed fit scores, not vibes
A Lehire fit score is a number from 0 to 100, and every point of it traces back to something specific: a rubric criterion, an interview answer, a scorecard rating, or a piece of resume evidence. When a hiring manager asks why a candidate scored a 78 and not an 85, you can show them. The score is an argument, not an oracle.
Because the scoring model is the same for every candidate on a role, comparison becomes honest. Two engineers interviewed by two different panels are still measured against the same criteria with the same weights. That is what makes a ranking trustworthy: the differences in score reflect differences in the candidates, not differences in who happened to interview them.
The Decision Engine: from a long list to a ranked shortlist
Once candidates are evaluated, Lehire's Decision Engine ranks them against the role. You see finalists ordered by fit, with the evidence behind each position one click away. For roles with high volume, this collapses the work of sorting fifty applicants into reviewing a ranked shortlist where the top of the list is genuinely the strongest fit.
Ranking also surfaces the close calls. When two candidates are within a few points, the engine shows you exactly where they diverge so the panel can focus its discussion on the criteria that actually separate them, instead of re-reading everything from scratch.
Hiring memory: decisions that compound
Most hiring tools forget everything the moment a role closes. The strong runner-up who lost a close race disappears into the void, and six weeks later when a similar role opens you start from zero. Lehire's hiring memory keeps your evaluated candidates and lets you re-rank them against a new role without re-uploading or re-interviewing.
Over time this turns hiring into a compounding asset. Every evaluation you run makes the next decision faster, because your strongest near-misses are already scored and ready to be reconsidered against the next opening.
Where decision intelligence sits in your stack
Lehire is deliberately not an ATS, a candidate database, or a sourcing tool. Your ATS owns the pipeline, the stages, and the system of record. Lehire owns the decision: how candidates are evaluated, scored, ranked, and ultimately chosen. They work together, and Lehire exports cleanly back to your ATS or to CSV.
This separation is the point. The ATS was built to move candidates through stages, not to help you decide between them. Decision intelligence is the missing layer that makes the most consequential step in hiring, the choice itself, structured and defensible.
Evidence-backed fit scores
Every 0 to 100 score traces to specific rubric criteria, interview answers, and scorecard evidence you can inspect.
Decision Engine ranking
Turn a long list of candidates into a ranked shortlist ordered by fit against the role, with close calls flagged.
Structured scorecards
Interviewers rate against one shared rubric per role, so every assessment is on the same scale.
Hiring memory
Re-rank past candidates against new roles without re-uploading or re-interviewing them.
AI Interviewer
Run a turn-based screening interview that scores answers and feeds directly into the fit signal.
Hiring analytics
See score distributions, panel consistency, and funnel quality to keep decisions honest over time.
Most teams decide with spreadsheets and instinct. Here is what changes when the decision layer is structured.
Use cases
High-volume roles
Collapse fifty applicants into a ranked shortlist so recruiters spend time on the strongest fits, not on sorting.
Cross-panel consistency
Give distributed interview panels one rubric and one scale, so scores from different rooms are actually comparable.
Defending decisions to leadership
Walk an exec or a hiring committee through exactly why a candidate ranked where they did, with evidence attached.
Reopening a closed role
When a similar role opens, re-rank last quarter's near-misses in minutes instead of restarting the search.
Stop deciding from memory. Start deciding from evidence.
See how hiring decision intelligence turns your interviews and scorecards into a ranking you can defend.