An ocean of applicants.
One clear decision.
Sagar means ocean — and that is what a single open role now sends you. HireSagar reads every resume the way your best recruiter would, scores it against the role, and tells you exactly what to ask — then shows you the evidence behind every score. Explainable. Deterministic. Auditable.
From upload to interview in 60 seconds
Upload a resume and a job description. HireSagar does the rest — instantly, explainably, consistently.
Upload the resume
PDF or DOCX. Every signal — dates, companies, skills, gaps — is extracted by rule, not inference. The same document always reads the same way.
The intelligence engine reads it
Validation, profile extraction, timeline reconstruction, skill depth, scoring evidence, JD alignment, hiring intelligence and career insight — run in parallel, complete in ~30 seconds.
Recruiter gets the full picture
Score, evidence, risk flags, career trajectory, and 12–15 candidate-specific interview questions. Make the call with confidence.
No other ATS does this
Every feature below is live, tested, and running in production — not a roadmap promise.
Resume Intelligence
4-tier skill classification (practical / listed / related / missing), experience year calculation cross-checked against actual timeline dates, location extraction, and achievement parsing.
Deterministic + reasoningTimeline Analysis
Finds every employment gap (≥2 months), detects timeline overlaps, calculates average tenure, flags frequent switchers, and gives each gap a risk level with a likely reason.
Deterministic + reasoningExperience Consistency
Compares claimed experience against computed experience. Flags impossible overlaps, technology-age mismatches, and experience inflation — before they waste your time in an interview.
DeterministicHiring Intelligence
Synthesises all 7 prior intelligence modules into a single structured hiring recommendation with evidence. Not a black box — every score traces to a specific resume passage.
ReasoningCandidate-Specific Interview Questions
12–15 questions that reference this candidate’s actual companies, projects, claimed metrics, and detected gaps. Two candidates applying to the same role never get the same questions.
ReasoningCareer Insight
Promotion velocity, leadership evolution, career direction clarity, business impact quality, and learning velocity — across all industries from tech to healthcare to government.
ReasoningRecruiter Learning Engine
When a recruiter corrects a skill classification, that correction is stored and applied to every future candidate at their organisation. The system gets smarter with use.
Deterministic — no black-box MLJD-Resume Alignment
Semantic alignment scoring between the job description and the resume — not keyword matching. Detects conflicts, gaps, and misrepresentations that keyword ATS systems miss entirely.
Deterministic + reasoningWhat recruiters actually see
Real output from a real resume upload. Every field is grounded in evidence — not generated narrative.
Hiring Intelligence Report
The hiring intelligence module synthesises every prior stage into a final verdict with evidence, risks, and a recommendation that explains itself.
- Overall score derived from 7 weighted dimensions, not a single heuristic
- Every risk flag linked to specific resume evidence, not a guess
- Recommendation calibrated to the candidate’s level and the role’s requirements
- Career trajectory covers promotion velocity, growth signals, and ownership depth
vs. Every other ATS
Other platforms track candidates. HireSagar evaluates them.
| Capability | HireSagar | Greenhouse | Lever | Workable | Manatal |
|---|---|---|---|---|---|
| Pipeline management | ✓ | ✓ | ✓ | ✓ | ✓ |
| Skill-gap analysis, 4-tier with evidence | ✓ | — | — | Keyword only | Basic |
| Experience consistency check | ✓ | — | — | — | — |
| Employment timeline intelligence | ✓ | — | — | — | — |
| Candidate-specific interview questions | ✓ | — | — | Generic templates | — |
| Auditable scoring (evidence trail) | ✓ | — | — | — | — |
| Recruiter learning engine | ✓ | — | — | — | — |
| Career trajectory insight | ✓ | — | — | — | — |
| Starting price | ₹7,999/mo | ~₹51,981/mo | ~₹37,805/mo | ₹17,863/mo | ₹1,796/seat |
Every score traces back
to a line on the resume
Hiring decisions that can't be explained can't be defended. HireSagar scores candidates with a deterministic intelligence engine — dates, tenure, skill evidence, and gaps are read from the document itself with rules you can inspect, not guessed by a model. Open any score and you see the exact reasoning and the passage behind it.
- →Skill evidence graded: demonstrated in real work vs merely listed
- →Employment months summed role by role — gaps never counted as tenure
- →Timeline gaps, overlaps and tenure patterns surfaced with dates
- →Every requirement shown as matched, related, listed, or missing
- →Same inputs, same output — every time, for every candidate
An explainable hiring
intelligence engine
Not a chatbot. Sagar helps recruiters make faster, more confident hiring decisions — every answer grounded in your real candidates, interviews and pipeline, with the evidence and reasoning shown. When a rule or your own data can answer, it answers instantly and for free; it reaches for a model only when judgment is genuinely required.
- →Who are my top candidates for the Senior Backend role?
- →Which candidates have timeline gaps I should ask about?
- →Compare my two finalists and give me a recommendation
- →Why did the AI flag this candidate as high risk?
- →Who should I prioritize interviewing this week?
Priya scores 79 — slightly stronger system design, but a 14-month gap between 2022–2023 flagged as medium risk.
Recommendation: Rahul edges ahead on overall fit. I'd lead with system design questions for Priya to address the gap.
No borrowed logos.
Just verifiable engineering.
We're a young product and we won't fake social proof. What we can show you is how seriously the platform is built — every number below is real.
Built for security
from day one
Your candidate data never leaves your isolated tenant. Every access is logged. You stay in full control.
Simple, predictable pricing
Start free. Upgrade when you’re ready. No hidden fees.
Free forever on the Free plan — no credit card. Paid plans start with a 7-day free trial.
What a hiring intelligence platform actually does
A short, honest primer on the difference between tracking candidates and evaluating them — and where each belongs in modern recruitment.
Where an applicant tracking system stops
An applicant tracking system is a system of record. It stores candidates, moves them through stages, keeps hiring organised, and leaves an audit trail. That work matters, and HireSagar does all of it — per-job pipelines you configure yourself, full stage history, team collaboration, and a record of who changed what and when.
But an ATS answers where is this candidate? It does not answer is this candidate right for this role? That question still lands on a person reading resumes at speed, and that is exactly where consistency breaks down. Two recruiters rank the same shortlist differently. The same recruiter ranks it differently on a Friday afternoon than on a Monday morning. Nothing in a traditional ATS notices, because evaluation was never its job.
What hiring intelligence adds
Hiring intelligence is the layer that evaluates rather than tracks. HireSagar reads every resume against the specific role it was submitted for and returns three things together: a score, the evidence behind that score, and the reasoning that connects them. The evidence is the part that matters. A number with no explanation is a black box, and a black box is not something you can defend to a hiring manager — or to a candidate who asks why they were passed over.
The foundation is deterministic. Resume parsing, skill extraction, experience calculation, timeline reconstruction and resume scoring run as explicit rules, not as a language model guessing. The same document always produces the same result, every score traces back to a specific passage, and nothing is invented. Model-backed reasoning is layered on top of those facts for the genuinely judgement-shaped questions — never underneath them.
Screening, evaluation, and the interview
Resume screening is where volume hurts most: a few hundred applicants against one role, most of them plausible on paper. HireSagar grades the evidence for each requirement the job actually states — demonstrated, claimed, adjacent, or absent — so a shortlist is built from what a resume proves rather than which keywords it happens to repeat. Career timelines are reconstructed and cross-checked, so gaps, overlaps and inflated tenure surface before they cost you an interview slot.
Candidate evaluation continues past the shortlist. Interview management keeps scheduling, structured scorecards and feedback attached to the candidate record instead of scattered across calendars and inboxes, and generated interview questions reference that candidate's real companies, projects and detected gaps — so two people applying for the same role are never asked the same generic five questions. Assessment workflows cover the skills a conversation cannot verify, with results landing on the same record as everything else.
Why explainability is the thing to insist on
Screening tools are easy to demo and hard to trust. The question worth asking any vendor is not how accurate the model is, but whether the product can show its working. If a tool cannot point at the line in the document that produced a score, you cannot audit it, you cannot correct it, and you cannot explain a rejection to anyone who asks — which in several jurisdictions is not merely awkward but a compliance problem.
That constraint is why the HireSagar engine is deterministic first. Rules can be inspected, tested and reproduced; a probabilistic guess cannot. It also means the analysis behaves the same whether you run one resume or ten thousand, and that a score never quietly changes because a model was updated underneath you. Your hiring data stays yours and is exportable at any time, and candidate data is never used to train AI models without your explicit consent.
Analytics, automation, and knowing whether it worked
Hiring analytics closes the loop: time-in-stage, conversion between stages, source quality, pipeline health, and whether your scoring actually correlates with the people you go on to hire. Recruitment automation handles the mechanical parts around it — stage-triggered emails, scheduling, reminders, bulk actions — so automation removes busywork rather than removing judgement.
You can see the deterministic engine before creating an account. The free hiring intelligence tools run the same analysis on a single document with no signup, the interactive demo walks a full recruiter workspace on sample data, and the blog goes deeper on screening, evaluation and hiring metrics. When you are ready to compare tiers, pricing is public, and the FAQ answers the specifics on data ownership, security and how scores are derived.
Common questions
Looking for something more specific? Browse the full FAQ — 50+ answers on hiring, AI screening, security, and pricing.
Ready to hire smarter?
Start your 7-day free trial. No credit card. Every resume scored by the deterministic engine, with the evidence attached.