01 / The problem
What was getting in the way
A serious job search quickly spreads across job boards, documents, browser forms, email, and personal notes. Each opening still needs to be checked for fit, matched to truthful career evidence, prepared, tracked, and followed up.
We needed to handle a large number of openings without creating duplicate work, weak priorities, unsupported claims, missed follow-up, or automation that acted on my behalf.
The system handles private career information and real employment decisions. We needed to save effort while protecting accuracy, privacy, a clear record of what happened, and my control over every application and message.
02 / Our approach
The decisions we made first
My roleI set the operating goals, workflow stages, fit rules, approval boundaries, evidence standards, and exception policy. We designed and tested the system around those decisions. I reviewed the results and kept final authority over every application.
Use one pipeline
We put discovery, review, application preparation, outreach, interviews, and closure into one workflow instead of maintaining separate checklists.
Score before we draft
We check location, role fit, compensation evidence, hard disqualifiers, and duplicate listings with versioned rules before AI helps prepare any writing.
Keep final actions with me
We let automation collect, organize, score, draft, and reconcile. I still approve packets, submit applications, send messages, confirm sensitive answers, and make interview or offer decisions.
Show failures instead of hiding them
We record decisions, retries, failures, and fallback behavior so a missed run cannot silently disappear or create the same work twice.
03 / What we built
How the solution came together
Bring openings into one queue
We turn public ATS listings and safe public URLs into consistent opportunity records, remove duplicates, score them, and place them in my review queue.
Build materials from approved facts
We combine approved career evidence, the right resume, verified answers, writing rules, and job requirements into one application packet with a clear source record.
Put the next action in one place
We show one role at a time with the correct resume, cover letter, evidence, open questions, outreach draft, and submission checklist.
Keep tracking tied to real actions
We create follow-up tasks, outreach drafts, and later pipeline stages from confirmed actions instead of copying status across separate trackers.
Surface work that needs attention
We monitor source runs, packet recovery, follow-up, email, and durable failures. A daily digest shows exceptions without giving the scheduler approval authority.
04 / How it works
The process from start to finish
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01
Discover
We collect and standardize listings from approved public sources.
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02
Qualify
We apply fit rules, hard gates, career evidence, and owner review.
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03
Prepare
We build an evidence-backed packet and private application documents.
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04
Apply
We assist with safe form work while I review and submit.
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05
Track
We record the application, follow-up work, outreach, and pipeline stage.
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06
Improve
We review results and failures without changing policy automatically.
We use AI to interpret listings, select approved evidence, draft materials, prepare safe answers, and explain context. Fixed rules control scoring, workflow state, authorization, validation, and retries. AI cannot submit an application, send a message, invent a claim, or change the pipeline on its own.
05 / The result
What improved
Business value
- We created one accountable workflow from finding an opening through follow-up.
- We made priorities, evidence, open questions, and next actions visible in one workbench.
- We improved consistency and made duplicate work and exceptions easier to control.
- We automated repetitive preparation and reconciliation while I kept final control.
Safeguards we kept
- We limit access to the owner and enforce row-level controls.
- We do not expose a public write endpoint.
- We version the scoring, evidence, writing, and automation rules.
- We preserve owner decisions and the sources behind each packet.
- We give source runs, packets, confirmations, and follow-up stable identities to prevent duplicates.
- We use a fixed fallback packet when AI output fails validation.
- I approve every external submission and message.
- We keep private documents and contact information outside the public site.
What this demonstrates
Commercial judgment backed by operating depth.
Capabilities
- Operating-system design
- Workflow orchestration
- Evidence-based prioritization
- Implementation judgment
- Responsible AI governance
- Exception and retry design
Systems used
- Public ATS APIs
- Supabase
- PostgreSQL
- OpenAI
- Gmail API
- Chrome extension