(00) Features

The intelligence layer

Intelligence you canopen up, take apart,and disagree with.

Atlas does not hand you a mystery percentage and hope you accept it. It separates the signals it reads from the scores it produces, exposes the arithmetic between them, and treats your disagreement as the most useful input it gets. Eight building blocks, three of them scores you can open.

Building blocks: Eight, three of them openable scores
Ranking: Deterministic features, versioned weights
ATS: 24 fixed checks, zero model tokens
Learning: Outcome snapshots from application one

Get started See the workflow

(01) Intelligence layer

Signals into scores

Eight building blocks.
Three of them scores, all of them arguable.

Thesearethepiecestheproductisassembledfrom.Candidatestrength,companysignal,andmatchfitdecomposeintothevaluesunderneaththem;therestarerulesandbehavioursyoucanreadinfull.

Candidate strength

A calibrated profile score built from experience depth, proven skills, resume craft, GitHub evidence, and professional completeness, with the single largest gap named, not a vanity percentage.

Company intelligence

Company quality from five measured signals: responsiveness, hiring velocity, posting freshness, time to first response, and whether the employer is verified. Each carries its sample size, so a score from four applications is not shown as settled.

Explainable matching

Every match opens to its ten drivers: skill coverage, missing must-haves, seniority fit, domain alignment, compensation fit, location and remote fit, employer signal, your strength against the role bar, posting freshness, and competition. Open any score and you see the arithmetic.

Canonical skills

Skills resolve to canonical entries, not raw strings, so "Postgres", "PostgreSQL", and "psql" count once. Coverage is measured against what the role requires, weighted by what it insists on.

Deterministic ATS

Parseability, structure, and content checks run as fixed rules. Same resume, same score, every time. Language suggestions stay advisory and never move the number.

Freshness that decays

A 40-day-old post is not the same opportunity as one from this morning. Freshness decays continuously and reposts are collapsed, so a stale listing cannot masquerade as a live one.

Assisted, not automated

One-click apply on Greenhouse, Lever, and Ashby. Everywhere else the browser extension fills the form in your own session, highlights what it could not map, and you click Submit. Atlas never submits silently.

Data that learns

Feature snapshots and outcome logs let ranking improve over time, with replies, interviews, offers, and rejections all feeding back, without changing how the product feels to use day to day.

Ascoreyoucannotinterrogateisjustanopinionwithadecimalpointonit.Everynumberherecomeswiththeworkshownunderneathit.

(02) How it holds up

What we will stand behind

Four properties the
scoring layer has to keep.

Anyonecanproduceanumber.Thesearetheconstraintsthatdecidewhetherthenumberisworthactingon.

Same inputs, same score, every time.

Reproducible

Ranking is deterministic feature math with versioned weights. No sampling, no temperature, no quiet drift between two views of the same posting an hour apart.

Every number traces back to a row.

Auditable

Open any score and you get the drivers, their weights, and the underlying values with their source and timestamp attached. If a match moved, you can find what moved it.

Weak signals are marked weak, not hidden.

Calibrated

A job with thin extracted data scores neutral rather than flattering. An unverified claim is labelled as unverified. The product would rather say it does not know than guess convincingly.

Language models never touch the ranking.

Bounded

Models parse, enrich, and draft. They do not decide order. That boundary is the difference between a system you can debug and a system you can only argue with.

(03) The architecture

Inputs, scoring, outcomes

Three moves, in order,
and a hard line between each.

Everything becomes a structured signal first.

Inputs

Resumes, code evidence, professional profiles, postings, company records, contacts, applications, and replies detected by the browser extension are parsed into versioned features before anything is scored. Extraction and judgement are separate jobs, and keeping them separate is what makes the second one auditable.

Three scores, kept apart on purpose.

Scoring

Candidate strength, company quality, and match fit are computed independently. Collapse them into one figure and the product loses the ability to tell you which of the three is actually the problem, which is the only thing you needed to know.

The loop closes, then it sharpens.

Outcomes

Applications and results are logged against the feature snapshot that produced the recommendation. Ranking improves from what worked rather than from what sounded right, and the interface stays exactly where you left it.

Fit

Ten drivers — coverage, gaps, seniority, domain, pay, location, employer signal, role bar, freshness, competition — 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.

(04) Workflow

Collect, score, act, learn

Built around the full search loop.

Mosttoolsownonestepandhandyoutherest.Atlascollectsthemarket,turnsitintoscoresyoucanarguewith,strengthenstheprofileyousendout,thenrecordswhatactuallyhappenedsothenextrankingissharperthanthelast.

Collect the right market

Atlas keeps one shared index of roles and companies across official ATS boards, aggregators, startup listings, and URLs you drop in. Each post is parsed into skills, seniority, location, pay, source, and freshness, so you start from structure instead of a hundred open tabs.

One shared index
Duplicate posts collapsed
Dead listings quarantined

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.

Ten weighted drivers
Weights are visible
No single opaque percent

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.

Deterministic ATS checks
Claims matched to evidence
Fixes ranked by impact

Move applications forward

Applications, cold emails, portal submissions, replies detected by the browser extension, interviews, assessments, and follow-ups live on one timeline, not scattered across spreadsheets, inboxes, and memory.

Replies detected in your browser
Follow-ups surfaced
One thread per role

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.

Contacts tied to roles
Outreach history kept
No CRM busywork