Sample report
This is a synthetic resume — not real data. Your own report will be customized to your actual resume and target.
Your judgment
synthetic-resume.pdf
target: Senior Software Engineer at Stripe
- name100%
- email100%
- phone100%
- linkedin url95%
- github url88%
- personal urls67%
- skills79%
- education100%
Experience alignment: 83%
| Query | Mean | σ scalar | ρ reasoning |
|---|---|---|---|
| Seniority (1-10) | 6.50 | 0.50 | 0.12 |
| Technical depth (1-10) | 7.00 | 0.70 | 0.18 |
| Top strengths | — | — | 0.31 |
| Role fit (1-10) | 7.20 | 0.40 | 0.14 |
| Final-round probability | 0.42 | 0.06 | 0.16 |
| Key credential | — | — | 0.22 |
| Missing signal | — | — | 0.28 |
| AI-authored probability | 0.18 | 0.05 | 0.09 |
How far the LLM read of your seniority drifts from what the ATS parsers extracted as your level. Soft signal — calibration improves in M5.
- •Quantified ownership of system-design work at scale (Meta TAO migrations)
- •Cross-stack range — proficient in both backend Go and frontend TypeScript
- •Demonstrable shipped product impact (B2B SaaS revenue retention)
Across surviving LLMs, ranked by mention frequency.
No public-facing technical writing, conference talks, or open-source contributions linked from the resume — for a Senior IC role at Stripe, evidence of external technical voice is a routine signal that's currently missing.
Most-detailed answer from any responding LLM.
Your judgment, in plain English
The narrative version
Plain-language explanation of the data above, with the specific numbers from your resume. The tables remain authoritative — this section translates them.
All three parsers extracted name, email, and phone identically and agreed perfectly on the education section. Disagreement clustered around personal-URL extraction (33% across parsers) and skills (21% Jaccard distance), suggesting the resume's contact block is robust but the skills section may read inconsistently to keyword-matching ATSes. ATS legibility (mean 78%) is solid; fragility (variance 0.04) is low — meaning all three parsers extracted similar fill rates, so structurally this resume is on firm ground.
Across 11 total bullets in the two experience roles, 7 (64%) contain quantified outcomes — a healthy quantification rate that beats the rough median for engineering resumes. 9 of 11 (82%) start with strong action verbs (built, shipped, led), and only 1 contained a vague phrase ('cross-functional'). The earlier Meta L4 role had stronger quantification (5/6 bullets) than the current SaaS role (2/5) — adding numbers to the recent bullets would tighten the narrative. Mean bullet length is 142 characters, in the readable range.
All four LLMs agreed closely on seniority (σ 0.5 — mean 6.5/10, mid-to-senior), with similar consensus on fit for Senior Software Engineer at Stripe (σ 0.4 — mean 7.2/10). Disagreement was higher on technical depth (σ 0.7) and the top-strengths list (ρ 0.31 — the four models pointed to slightly different dimensions of strength). Inter-modal δ of 0.11 means the AI judges' read of seniority is essentially aligned with the ATS-derived structural level — no wide gap between how the parsers and the LLMs see this candidate. Final-round probability mean of 42% reflects competitive but not stretched targeting.
- Add quantified outcomes to bullets 2 and 3 of the current SaaS role — even rough numbers ('reduced page load time ~40%') anchor the bullet against AI-and-ATS readers alike. The Meta L4 bullets are a good template.
- Replace 'cross-functional' (the one buzzword detected) with a concrete description of which teams collaborated and what shipped as a result — this is the kind of specificity that improves both AI readability and human signal.
- The personal-URL section had highest parser disagreement (33%); audit your portfolio/scholar/blog links for consistent formatting (https:// prefix, no trailing slashes) so the parsers extract them identically.
Placeholder weights — calibration against real outcome data lands in M5. The σ and ρ metrics on the right are direct measurements and do not depend on those weights — they are reliable today.