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[ARTICLE · art-65110] src=dev.to ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

Building an AI résumé scorer with Claude + AWS Bedrock - architecture and lessons learned

ScorAxis has built an AI résumé scorer using Claude Sonnet via AWS Bedrock that provides ATS compatibility scores from 0 to 100. The system uses a hybrid approach: deterministic Python rules for the 0–70 range and LLM-based analysis for the 0–30 range, with graceful degradation if the LLM is unavailable. The architecture ensures structured JSON output through forced tool_choice, resulting in near-zero validation failures in production.

read1 min views1 publishedJul 19, 2026

Most résumés never reach a human recruiter. ATS software filters them out silently, with zero feedback to the applicant.

That's the problem ScorAxis solves. Upload a PDF → get a 0–100 ATS compatibility score with specific issues flagged and actionable fixes.

Deterministic (0–70) — pure Python, no AI: contact info, sections, word count, bullets, date consistency, layout density

LLM (0–30) — Claude Sonnet via tool use: action verbs, quantified impact, keyword coverage, tense, buzzwords

If Sonnet is unavailable, the upload never fails. The system persists a deterministic-only score with a "Limited Mode" banner. The user always gets something useful.

Forced tool_choice made structured JSON output rock solid — almost zero validation failures in production.

Live at scoraxis.com — happy to answer questions about the architecture below.

source & further reading
dev.to — original article I Built an AI App. Eight Months Later, It Became a Skill The Ground Is Moving A Complete Guide to Moonshot's New 2.8T Flagship
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