Atlas

We run the GEM framework here — ten factors, from Sales & Deal Management to Learning Agility & Range, scored one candidate at a time. Atlas gives each factor a strength score and a gap score, one to ten, with bullet evidence for both. On top of that it runs a 21-dimension read of cultural fit (corporate vs startup, hierarchical vs flat, process vs agile) and writes up to seven Black Hat questions aimed at the weak spots.

We ship three depths: gem_full for the ten-factor deep read, gem_lite for a snapshot, career_path for trajectory. Run the full read on final-stage candidates; gem_lite covers the early rounds.

Point Atlas at one JD and one CV and it reports where they actually meet, and where they don't — a fit score backed by concrete strengths, real gaps, and live market context. One candidate runs synchronous; a small set ranks async.

The practical limit is five candidates. Past that this becomes a triage job, not a match job, so we hand off to atlas-shortlist, which is built for volume.

Atlas writes interview questions built for this candidate and this JD, at the pressure level you choose — supportive, standard, or aggressive. It then reads the candidate's actual answers and generates follow-ups that push where an answer got thin.

We tuned aggressive mode for senior and leadership seats — harder probing, fewer soft landings. Supportive suits early-career or a lateral move.

Each set runs one to fifteen questions, five by default, generated for this specific candidate and conversation — not pulled from a generic bank.

This is our high-volume triage — we built it for the day forty applicants land at once and scoring them by hand is not a plan. Atlas screens the whole pile in one go: batch GEM scoring and batch JD-fit across every candidate, ranked into a shortlist you can act on.

We made the batch resilient: one bad CV does not kill the run — it keeps going across the pile.

The input is the CV stack; the output is the ranked order, top to bottom.

Our default framework is broad, but some roles hire for things it does not measure — so we let you train Atlas on your own criteria.

Upload a few example CVs — strong, weak, mixed — and Atlas infers a rubric from what you actually reward, which you can then tune by dimension and weight. Once the rubric is built, it batch-evaluates every candidate against your criteria, not the defaults.

We keep setup and training free; you pay only per candidate evaluated.

We put a review step ahead of the matching: Atlas reads the job description itself, before any candidate is scored against it. It checks quality, bias and inclusivity, market competitiveness, and salary benchmarks, and pulls the real skills out of the text.

Give it an industry and a location and switch on live search, and the salary and market numbers come from real-time data instead of a guess. This capability reviews the JD, not the candidates.

Fixing the posting first makes everything downstream easier.

These are the workforce questions you would otherwise pay a consultant for: salary benchmarks, talent supply and demand, competitor hiring intel, comp strategy.

We ship two modes. 1. A light advisor for the quick calls — offer structure, notice periods. 2. A full chat with live tools for the data-heavy ones — for example, how many React developers are actually available in Berlin. Atlas is the recruiter-facing half of the pair; CeeVee answers the same market from the candidate's side.

Short questions get straight answers, with real data underneath.

We turned the consulting engagement into a report run: Atlas writes sixteen report types, one PDF each, with market data, talent intelligence, and recommendations you can act on.

1. Recruitment — talent market landscape, salary benchmarking, competitor talent, sourcing strategy. 2. Workforce planning — skills gap, succession, demographics, attrition risk. 3. Strategic HR — DEI, employer branding, L&D, org design, total rewards, HR tech, change management.

You get consulting-grade research without the six-week engagement or the consulting invoice.