Candidate strength
A calibrated profile score built from experience depth, proven skills, resume craft, GitHub evidence, and professional completeness — not a vanity percentage.
Features
How it works
Intelligence you can open up and inspect.
Atlas does not hand you a mystery percentage. It separates the signals it reads from the scores it produces, so every recommendation comes with its reasons — and a clear path to a better one.
(01) Intelligence layer
Signals → scores
Four building blocks.
Each one is a score you can decompose, not a label you have to trust on faith.
Company intelligence
Company quality from hiring velocity, open-role history, compensation signals, tech relevance, reputation, and stability — so you spend effort where it pays off.
Explainable matching
Every match opens up to show its drivers: skill coverage, seniority fit, company score, freshness, location fit, and the role bar. No black boxes.
Data that learns
Feature snapshots and outcome logs let ranking improve over time — without changing how the product feels to use day to day.
(01) Inputs
Everything becomes a structured signal.
Resumes, GitHub evidence, LinkedIn data, job posts, company records, contacts, applications, and inbox-detected replies are parsed into versioned features. The product stays simple because the system underneath is doing the organising.
(02) Scoring
Three scores, kept separate on purpose.
Candidate strength, company quality, and match fit are computed independently — so a recommendation can always be traced back to the signals behind it, and you can see exactly what to improve.
(03) Outcomes
The loop closes, then sharpens.
Every application and result is logged as outcome data. Ranking improves over time from what actually worked, without changing how the product feels to use day to day.
Fit
Skill coverage, seniority, company quality, pay, freshness, and location are scored together, not guessed.
Proof
Resume claims are checked against projects, GitHub evidence, profile data, and parsed work history.
Loop
Applications and outcomes are logged, so ranking keeps learning which signals actually matter.
(02) Workflow
Collect → score → act → learn
Built around the full search loop.
This is not a generic tracker. Atlas collects market data, turns it into explainable scores, strengthens your profile, and records outcomes so ranking keeps getting better.
01
Collect the right market
Atlas keeps one shared index of roles and companies across Indian job boards, startup listings, internships, and URLs you drop in. Each post is parsed into skills, seniority, location, pay, source, and freshness — so you start from structure, not a hundred open tabs.
02
Score fit honestly
Fit is more than resume-to-job keyword overlap. Atlas weighs proven skills, seniority match, company quality, location, compensation, posting freshness, and your own profile strength into one match score you can actually defend.
03
Strengthen your profile
Resume parsing, ATS checks, completeness scoring, GitHub evidence, and LinkedIn imports show exactly what is strong, what is missing, and what to fix next for the role you are actually chasing.
04
Move applications forward
Applications, cold emails, portal submissions, inbox-detected replies, interviews, assessments, and follow-ups live on one timeline — not scattered across spreadsheets, inboxes, and memory.
05
Use contacts with context
Referral posts, contacts, company notes, and recruiter threads stay tied to the roles and companies they can actually influence — so a warm intro never gets lost.