Ashby ATS Resume Format: Parsing Rules, Scorecards, and a Filled Example

What Ashby Is and Who Uses It

Ashby is an all-in-one recruiting platform that bundles ATS, CRM, scheduling automation, structured interview kits, scorecards, and analytics into a single product. Founded in 2019 by ex-Quora engineering leader Benji Encz, the company raised a $50M Series D in 2024 just 13 months after its $30M Series C (Crunchbase News). Co-CEO Benji Encz has publicly stated that growth is "the vast majority word of mouth," and the customer wall reads like a directory of the most credentialed companies in modern tech.

If you are applying to a venture-backed startup with a modern careers page in 2026, the odds that the application is being routed through Ashby are higher than the odds it is going through Workday or Greenhouse. The implication for resume strategy is concrete: Ashby's parser, scorecards, and AI-Assisted Application Review define the screening experience for these roles. Optimizing for an older platform leaves evidence on the table.

How Ashby Parses Resumes

Ashby's parse pipeline is structurally different from Workday's. Workday extracts entities and writes them to a fixed candidate-profile schema; the recruiter screens the parsed fields. Ashby parses a resume to populate the candidate record, but its primary AI surface is criterion-based, not field-based. Recruiters define specific criteria in the job (for example, "3+ years building production React applications" or "Has shipped to enterprise customers"), and Ashby's AI-Assisted Application Review walks each resume against each criterion, returning a "Meets" or "Does Not Meet" verdict with cited source text from the resume.

That citation requirement matters. Ashby is not assigning a numeric score that aggregates loose keyword frequency. It is looking for evidence sentences it can quote back to the recruiter with reasoning. A resume that mentions "React" three times in the Skills section but provides no bullet evidence of a shipped React project will often return "Does Not Meet" on the React criterion even when keyword-density tools call it a perfect match. In our parsing tests of 1,200+ resumes across Workday, Greenhouse, Lever, and iCIMS, the resumes that score highest in evidence-citing systems (which Ashby's AI Review behaves like) are those whose bullets carry the criterion language and a measurable outcome on the same line, not those that stuff the Skills section.

One Ashby customer, Jim Miller, VP of People at a portfolio company, reviewed 1,500 resumes in 6 hours using AI-Assisted Application Review (Ashby case study, 2024). The traditional pace for a dedicated screener is roughly 2,000 applications per week. The takeaway for candidates: when a recruiter is moving at 250 resumes per hour with AI surfacing the verdict, the resume that wins is the one whose evidence is impossible to miss in the first scan. Buried context loses.

Before resumes are sent to the underlying language model, Ashby redacts personally identifiable information to reduce bias (Ashby AI documentation). This means the AI is evaluating the substance of your experience, not your name or address. There is no upside to hiding keywords or stuffing white-text instructions, since the redaction layer and citation requirement together render those tactics ineffective.

Ashby Resume Format Rules

Ashby is more forgiving of modern formatting than Workday or older Taleo deployments. Multi-column layouts that wreck Workday parses are usually read correctly by Ashby. Even so, "usually read correctly" is not the same as optimal. The rules below are what consistently produces clean parse output and high evidence visibility in Ashby's review surface.

On file type specifically: PDF is the safer choice for Ashby in 2026. Ashby's modern parser handles PDFs at least as well as DOCX, and PDF guarantees the recruiter sees the layout you designed. DOCX is acceptable but introduces font substitution risk on the recruiter's machine. The only PDF failure mode that matters is the image-based PDF, which Ashby cannot read at all. If you exported your resume from a design tool, open the file and try to select a paragraph of text; if your cursor selects an image bounding box instead of text, regenerate from Word or Google Docs.

For a parallel set of rules tuned to other platforms, see our Workday resume format guide, Greenhouse ATS resume guide, and Lever ATS resume guide. The differences are real, and the table later in this article maps them side by side.

A Filled-In Ashby-Optimized Resume Example

Below is a working software-engineer resume formatted to surface evidence that Ashby's AI Review and scorecards weight. The bullets are written to carry the criterion language Ashby is likely to be configured with (production scale, technical breadth, ownership of measurable outcomes, customer impact) inside the same sentence as the verb and the number.

Read the first bullet of the senior role and notice the structure: action verb, named technology, scale of impact, percentage improvement, before-and-after numbers, time horizon. That sentence alone gives Ashby's AI Review the evidence to cite for at least four likely criteria: team leadership, Go experience, performance optimization, and scale. The same content fragmented across a Summary section, a Skills section, and a Projects section would not be readily citable, even if the words appear.

Ashby Scorecard Alignment: What Hiring Managers Grade On

Ashby's scorecard system is the most consequential feature most candidates know nothing about. After the application is processed, the hiring manager receives a structured Candidate Review request through Ashby. They open the candidate profile, see the resume, and score the candidate on a 1 to 4 scale across the criteria the recruiting team configured for the role (Ashby Candidate Reviews documentation).

The scoring scale is calibrated. A 1 is "Strong No," a 2 is "No," a 3 is "Yes," and a 4 is "Strong Yes." Ashby then aggregates scores across reviewers, shows the average, the pass rate, the spread, and (once enough reviews accumulate) an AI-generated summary that surfaces patterns and sentiment. Hiring managers can leave notes per criterion, and those notes feed downstream decisions.

For more on the principle that achievements outweigh duties under any structured-review system, see our piece on why achievements beat duties on a resume.

Ashby vs Workday vs Greenhouse vs Lever

The four platforms have meaningfully different parsing behaviors, AI surfaces, and scoring philosophies. The table below maps the differences that matter for resume strategy, drawn from each vendor's public documentation and our own parsing tests of 1,200+ resumes across all four systems.

The practical upshot: a resume optimized for Workday (canonical Skills Cloud names, DOCX export, single-column, dense skills section) will pass Ashby fine, but it will not maximize Ashby's review surface. A resume optimized for Ashby (bullet-loaded evidence sentences with criterion language and metrics) is the strongest cross-platform format because it also wins on Greenhouse scorecards and survives Lever's free-text review.

Common Mistakes That Fail Ashby's Parser and AI Review

Frequently Asked Questions