Driora was built by an operations leader who spent 9+ years at Meta, hiring and scaling teams across very international candidate pools. The vantage point was the hiring side, where about fifteen seconds decides whether a resume keeps reading or gets filtered.
Built from the hiring side. Ex-Meta operations leadership, 9+ years scaling teams from 4 to 40+ across very international candidate pools, hiring manager on 100+ interviews. Contact Driora.
After enough hiring loops, the pattern is unmistakable. Three candidates look similar on paper. The interview goes to the one whose resume speaks results in real numbers and whose story connects directly to what the role is actually about, every time.
What wins is specificity. A hiring manager has about fifteen seconds to decide whether a resume keeps reading or moves on, and in those seconds the question is simple: did this candidate do the work to understand what this team actually needs, or did they send the same generic application to fifty other roles?
The existing tools were optimizing for the wrong thing. They counted applications per hour. The candidates who actually got read were doing the opposite work: fewer applications, deeper specificity per role, no AI tells in the writing. Volume tools could not produce that.
It started as job_bot, a personal pipeline that scanned real job boards, scored each role with Claude under hard filters, tailored a resume verbatim from source bullets with zero fabrication and wrote cover letters with every AI tell stripped before delivery. It worked.
Driora is that pipeline productized. The same principles, packaged for any candidate, with company intelligence and an interview playbook on top. The tool that worked, made available to anyone.
Each report is generated for one carefully chosen role, with the depth a hiring manager would want to see.
Every fit score comes with per-factor reasoning the candidate can audit, including which parts of the resume earned the score.
Executive names, team names and recent events come with sources. When it cannot verify, Driora uses generic phrasing and says so explicitly.
Every output passes a typography and AI-tell quality gate before delivery: em-dashes, Oxford commas, the buzzword cluster, parallel-construction monotony.
Cookieless analytics. PII redacted before any AI call. Data minimization is the default.
Every deferred decision is documented in a public ADR with the trade-off articulated.
A few hard commitments shape every decision. They are the lines that protect the candidate even when crossing them would make the product cheaper to build. Each one is a deliberate choice.
If any of these starts to look like the right call to relax, an ADR will explain the reasoning before it ships, with the trade-off documented and open for public questioning in the GitHub commit thread. Until then, the report you receive is the one Driora's makers would want to receive themselves. That is the bar.
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