Industry-aware resume coaching that shows job seekers what is weakening their application—and what to improve next.
The challenge
Most resume tools produce a score or a rewrite without showing candidates what the system actually found. Greenlit needed to make ATS feedback legible, actionable, and trustworthy for people already navigating a stressful job search.
The system
- Parse the uploaded resume into a structured document model.
- Classify the candidate's likely industry and apply the relevant scoring rubric.
- Score structure, evidence, keywords, credentials, clarity, and industry fit deterministically.
- Use AI for grounded coaching and verified rewrites, then carry approved material into the resume builder.
Important decisions
- Keep the score itself deterministic so model variance cannot change the baseline diagnosis.
- Give the coaching layer explicit score bands and harvested resume facts instead of asking it to improvise context.
- Separate ATS-safe templates from a visual portfolio template whose sharing tradeoffs are stated plainly.
How the work moves
- Step 1
Upload and parse
- Step 2
Classify and score
- Step 3
Explain the gaps
- Step 4
Verify targeted rewrites
- Step 5
Build and export
Current evidence
- The free scan is live at greenlit.cv and does not require an account.
- The product includes an industry-aware scoring system, coaching pipeline, and 13 resume templates.
- A broader profile and product-design makeover is underway; the portfolio labels that work honestly rather than presenting it as finished.
What it taught me
- AI is more useful when it explains a stable rubric than when it invents the rubric itself.
- Job-search products earn trust by naming uncertainty and showing evidence, not by overstating precision.
- Document rendering is product infrastructure: typography, pagination, and export fidelity all affect whether the coaching is usable.
Continue exploring
Return to selected work