TalentOS reads every resume in your pool, scores fitness, stability and offer-acceptance for each candidate, runs the interview, and hands you a ranked shortlist with the reasoning attached — in the time it used to take to open the first CV.
No signup, no sales call — the full workspace, seeded with a sample role and candidate pool.
Senior Full-Stack Engineer — shortlist
10 of 10 scored94
88
90
87
82
83
81
76
77
66
71
55
Executive summary
Ananya clears every threshold — strongest JD alignment in the pool, a 4.2-year average tenure, and compensation expectations inside your acceptable band. Recommend moving straight to offer.
8
specialist agents
3
scores per candidate
1
unified hiring score
0
resumes skipped
The bottleneck was never judgement. It was attention. TalentOS gives every candidate the same rigour you’d give your top three.
No single model guessing at everything. Eight narrow specialists, each scoring one dimension it can be held accountable for — then two flagship agents that synthesise the verdict.
Runs the moment a resume lands in the pool. Three independent scores, then a master agent that reconciles them into one recommendation.
Predict offer-acceptance probability
Predicts the probability that a candidate will accept the offered role based on profile signals, career history, compensation expectations, and market fit.
Resume × JD compatibility scoring
Compares candidate resumes against job descriptions and determines role compatibility, strengths, weaknesses, and fit score.
Retention & tenure forecasting
Evaluates employment history and identifies retention likelihood, job-switching patterns, and long-term stability indicators.
Master hiring intelligence agent
The flagship master agent. Runs Fitness, Stability and Acceptance in parallel and synthesizes a unified Hiring Score, executive summary, and risk assessment.
Runs during and after the conversation. Scores the answers, decides whether they were sufficient, and asks the question your panel forgot.
Score interview answers in seconds
Analyzes candidate interview answers and provides competency scores, communication scores, and overall assessment.
Smart probing questions on demand
Generates intelligent follow-up questions when candidate responses require clarification or deeper exploration.
Is the answer good enough?
Determines whether a candidate's response is complete, relevant, and sufficient for evaluation.
Master interview intelligence agent
The flagship interview orchestrator. Combines Q&A Evaluation, Follow-up Generation and Sufficiency Analysis into a complete interview verdict.
No implementation project, no data model to design, no training week. Paste a JD and the workspace is live.
Drop in the job description and define your budget bands — low, acceptable, maximum. The first line of the JD becomes the role title. That's the entire setup.
Budget bands
18L → 24L → 32L
Resumes live in one shared library, not per-role folders. Every role you open afterwards scores against that pool automatically — no re-uploading, no duplicates.
Shared pool
One library, every role
Fitness, Stability and Acceptance each score the candidate independently. LitmusTester then reconciles the three into a unified hiring score, a risk read and an executive summary.
Parallel scoring
Fitness · Stability · Acceptance
You get an ordered shortlist, a Strong Hire / Hire / Hold / Pass call per candidate, the reasoning behind each score, and a PDF or JSON export for the hiring committee.
Recommendation
Strong Hire → Pass
Sourcing through offer. Not a scoring widget bolted onto a spreadsheet — the full loop, with the reasoning preserved at every step.
Fitness, stability and acceptance collapse into a single 0–100 score with a 0–100 risk read and a clear call: Strong Hire, Hire, Hold or Pass. Highlights and concerns are listed separately so you can argue with the verdict.
Set low, acceptable and maximum for the role. Acceptance prediction scores against those numbers, and compensation fit comes back as Aligned, Stretch or Misaligned before you make the offer.
Candidates are uploaded once and scored against every role you open. Drop a .txt or .md anywhere on the field, or paste raw text — the pool is searchable by candidate name and resume content.
Fitness returns a per-skill coverage map alongside the score, plus explicit strengths and weaknesses — so a 78 tells you which 22 points are missing, not just that they're missing.
Average tenure, retention likelihood, job-switch risk and the employment patterns behind them. The signal that separates the hire who stays from the one who looked identical on paper.
The Interviewer agent scores answers on competency, communication and clarity, checks whether each response was sufficient to evaluate, and generates bias-free follow-ups when it wasn't.
Drive the whole pipeline from a chat surface — create a role, upload resumes, trigger evaluation and read the shortlist without learning a new navigation model.
Once a shortlist exists, model outreach at the low or maximum band and see who accepts, who declines and why — before a single email leaves your outbox.
Every agent run exports as a formatted PDF report for the hiring committee and a raw JSON payload for your ATS, warehouse or compliance archive.
Candidates analysed, interviews conducted, hiring-success prediction, per-agent usage and the agent mix — tracked daily and monthly across the whole workspace.
Push the role out to LinkedIn, Naukri, Indeed and your own careers site from the same workspace that scores the applicants coming back in.
Every agent is a swappable endpoint. Point them at BotSpot's hosted APIs or your own inference stack with one environment variable — and run fully mocked until you're ready.
Hiring is the hardest link in the chain, so we shipped it first. The same scoring spine extends across everything that follows.
Job-board distribution, the shared resume pool, and scoring across the whole applicant set.
Shortlisting, interview intelligence, offer-acceptance modelling and outreach simulation.
Carrying the stability and competency signal forward into tenure and performance.
Closing the loop — feeding real attrition outcomes back into the retention model.
Alumni re-engagement scored against the roles you're opening today.
Hiring is a regulated, contested, deeply human decision. An AI-only HR stack only works if every call it makes can be explained to the candidate, the hiring manager and, eventually, a lawyer.
Worth saying plainly: scores are decision support, not a verdict. Calibrate the thresholds against your own hires before you lean on them.
Each of the eight agents is a configurable endpoint. Point them at BotSpot's hosted APIs or your own inference stack — candidate data never has to leave infrastructure you control.
No bare numbers. Each agent returns a written rationale, the signals it weighed and the direction each one pushed — so a decision can be reviewed, challenged and overturned.
The same resume against the same role produces the same score every time. Re-run an evaluation six months later and you can reconstruct exactly what the system saw.
TalentOS ranks, scores and recommends. It never auto-rejects and never sends an offer. Every consequential action stays behind a person clicking the button.
It replaces the part of recruiting that is unpaid reading. Recruiters stop triaging a pile of PDFs and start working a ranked shortlist that already has scores, rationale and risk attached. The judgement calls, the conversations and the offer stay with your team.
A job description, three budget numbers, and resume text. That's it. Paste the resume or drop a .txt or .md file anywhere on the upload field — there's no schema to map and no ATS integration required to start.
Three agents score independently: Fitness measures resume-to-JD alignment, Stability forecasts retention from employment history, and Acceptance predicts offer-acceptance against your budget bands. LitmusTester reconciles the three into a unified score, a 0–100 risk read and a Strong Hire / Hire / Hold / Pass recommendation — each with written reasoning.
Yes. Every agent is a swappable endpoint set through environment variables. Point them at BotSpot's hosted APIs or your own inference stack, and run the workspace fully mocked while you evaluate.
Every score ships with the signals behind it, so a pattern you disagree with is visible rather than buried. Follow-up questions are generated with bias-free phrasing, and nothing is auto-rejected. That said, no scoring system is bias-free by default — calibrate the thresholds against your own hiring outcomes before you rely on them.
Every agent run exports as a formatted PDF report for the hiring committee and a raw JSON payload for your ATS, data warehouse or compliance archive. Nothing is locked inside the workspace.
It's the real workspace, seeded with a sample role and a ten-candidate pool, running against mock agent responses. Create a role, upload a resume and run an evaluation — the flow you see is the flow you get.
Open the workspace and run a real evaluation against a seeded role and candidate pool. It takes about ninety seconds to see whether this changes how you hire.