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ATS Mako
Recruiting Guide

Parsing Reads. Ranking Decides.

The plain-English breakdown of how resume parsing structures your applicant data and how AI candidate ranking turns it into a best-fit shortlist - for recruiters who hire at volume.

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The short answer

Two steps, one flow - not the same thing

Resume parsing is data capture: it reads a resume and structures it into clean, searchable fields. AI candidate ranking is prioritization: it scores that structured data against your criteria and returns an ordered shortlist. Parsing runs first and answers what is on the resume. Ranking runs next and answers who to contact first.

Resume Parsing

It reads and structures the resume

Resume parsing is the step that turns a messy PDF, Word doc, or pasted resume into clean, structured data your ATS can actually use. It reads the file, identifies each section - contact details, work history, skills, education, certifications - and maps every piece into consistent fields on the applicant record. In short: parsing is data capture. It answers what is on this resume, and puts the answer where a recruiter and a system can search it.

  • + Extracts name, phone, email, and location into standardized fields
  • + Separates job titles, employers, dates, and responsibilities
  • + Pulls skills, certifications, and education into searchable data
  • + Normalizes different formats so every applicant record looks the same
AI Candidate Ranking

It scores and prioritizes the candidate

AI candidate ranking is the step that decides who to look at first. Once resumes are parsed into structured data, ranking scores each applicant against the criteria you set - keywords, weighting, and engagement signals - and returns an ordered shortlist. Parsing tells you what is on the resume; ranking tells you how strong a match it is for the role. It is built on your criteria, not ours, so the shortlist reflects how your team actually hires.

  • + Scores every applicant against your keywords and custom weighting
  • + Factors in engagement signals like replies and open rates
  • + Returns an ordered shortlist instead of a raw pile of resumes
  • + Surfaces the strongest matches so recruiters close instead of search
Side by side

Resume parsing vs. AI candidate ranking

Same pipeline, different jobs. Here is exactly where each step starts and stops.

Core job
Resume Parsing Extracts and structures the information on a resume.
AI Candidate Ranking Scores and orders candidates by fit for the role.
Question it answers
Resume Parsing What is on this resume?
AI Candidate Ranking Who should I contact first?
Input
Resume Parsing Raw files - PDF, Word, pasted text, inbound email.
AI Candidate Ranking Parsed, structured applicant data plus your criteria.
Output
Resume Parsing A clean, searchable applicant record.
AI Candidate Ranking A ranked shortlist of best-fit candidates.
Runs first?
Resume Parsing Yes - parsing has to happen before ranking can work.
AI Candidate Ranking No - ranking depends on parsed data as its foundation.
Where it lives in ATS Mako
Resume Parsing Resume parsing and CV scoring, at applicant capture.
AI Candidate Ranking AI-powered candidate ranking, on your shortlist view.
Explore resume parsing and CV scoring Explore AI candidate ranking
How it runs

From resume to ranked shortlist

Five steps, one flow inside ATS Mako - capture, parse, score, rank, then screen with AI voice.

  1. 01 Capture

    The resume arrives

    An applicant submits through a branded Apply Here page, a job board, a portal, or a resume emailed to a unique inbound address. Smart source and job-posting matching turns the submission into an applicant record instantly - no manual upload, no retyping.

  2. 02 Parse

    The file becomes data

    The parser reads the document and identifies each section. Contact details, employers, titles, dates, skills, and education are extracted and mapped into standardized fields, so every record is consistent and searchable regardless of how the original resume was formatted.

  3. 03 Score

    Data meets your criteria

    Now that the resume is structured, AI ranking scores it against the keywords and weighting your team defined. It reads the parsed fields, matches them to what the role actually needs, and calculates a fit score - built on your criteria, not a generic template.

  4. 04 Rank

    The shortlist appears

    Applicants are ordered by fit, with engagement signals like replies and opens folded in. Instead of scrolling a raw pile, recruiters open their pipeline to a ranked shortlist and start with the strongest matches. Stop guessing. Start knowing.

  5. 05 Screen

    AI voice takes the next step

    For the top of the list, AI Voice and Call Screening can call candidates, run a structured phone screen, and feed the results straight into the pipeline. Run it one candidate at a time or as an AI Call Campaign across a whole segment - so your best-ranked applicants move forward without waiting on a recruiter to dial.

Myth vs. reality

Clearing up the confusion

Myth

Parsing decides who is best.

Reality

Parsing does not judge fit. It only structures the resume. Deciding who is strongest for a role is the ranking step - and it only works well when parsing has captured clean data first.

Myth

AI ranking replaces the recruiter.

Reality

Ranking prioritizes the pile so recruiters spend their time on the strongest candidates. The recruiter still owns the conversation, the judgment, and the close. AI hands you finished work; it does not make the hire.

Myth

One tool does it all the same way.

Reality

Parsing and ranking are distinct steps with different inputs and outputs. A platform that treats them as one black box hides the criteria that should be yours. In ATS Mako, both live in one flow but stay controllable and transparent.

After the ranking

Let AI voice screen your best-ranked candidates

Ranking gets your strongest applicants to the top. AI Voice and Call Screening takes it from there - calling candidates, running a structured phone screen, and feeding the results straight into the pipeline. Run it per candidate or as an AI Call Campaignacross a whole segment.

See AI voice and call screening Book a demo

Included on the Great White Shark plan with 1,000 AI voice minutes. Additional usage billed per minute.

The AI and Candidates toolset

  • 1 Resume parsing and CV scoring at capture
  • 2 AI candidate ranking on your criteria
  • 3 AI voice calls and AI call campaigns
Questions, answered

How does resume parsing work? FAQ

How does resume parsing work? +

Resume parsing works by reading a resume file - a PDF, Word document, pasted text, or an emailed attachment - and identifying the distinct sections within it: contact information, work history, skills, education, and certifications. The parser extracts each element and maps it into standardized fields on the applicant record. This normalizes wildly different resume formats into consistent, searchable data. The result is a clean applicant profile a recruiter can filter and a ranking engine can score - without anyone manually retyping details from the document.

What is the difference between resume parsing and AI candidate ranking? +

Resume parsing structures the data on a resume; AI candidate ranking prioritizes candidates based on that data. Parsing answers what is on this resume and puts the answer into clean fields. Ranking answers who should I contact first by scoring each applicant against your keywords, custom weighting, and engagement signals, then returning an ordered shortlist. Parsing has to run first - ranking depends on the structured data parsing produces. In ATS Mako they are two connected steps in a single flow.

Does AI candidate ranking need resume parsing to work? +

Yes. Ranking scores applicants against your criteria, and it can only do that once the resume has been parsed into structured, comparable fields. Without parsing, the ranking engine would be working from an unstructured blob of text instead of clean data. That is why the two steps run in sequence: capture, then parse, then score and rank.

Can I control what AI candidate ranking prioritizes? +

Yes. ATS Mako ranks on your criteria, not ours. You set the keywords and the weighting that matter for each role, and the engine scores applicants against them. Engagement signals like replies and opens can factor in as well. The shortlist reflects how your team actually hires, which keeps recruiters in control of the outcome.

Where does AI voice call screening fit in? +

AI Voice and Call Screening is the step after ranking. Once you have a ranked shortlist, AI Voice Calls can phone candidates, run a structured screen, and feed the results back into the pipeline. You can also run AI Call Campaigns across an entire candidate segment. It is included on the Great White Shark plan with 1,000 AI voice minutes, with additional usage billed per minute.

Stop scrolling resumes. Start closing candidates.

Parsing structures every applicant. Ranking surfaces the best. AI voice screens them. All in one white-label platform, fully branded to you.

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