David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · INTERNAL PRODUCT
Internal Product
ATS Job Intelligence & Automation Platform
A browser, desktop, and web platform for harvesting ATS jobs, normalizing job data, scoring fit, tracking applications, and automating career workflows...
Designed and built an AI-powered job intelligence and career automation platform spanning Chrome extension, Tauri desktop app, Next.js web app, Supabase/PostgREST backend, ATS data harvesters, MITM JSON intercepts, job-schema...
Client
Internal Product / Confidential Platform
Engagement Type
Internal product buildout
Period
2025–2026
Role
Principal Architect / Product Architect / AI Automation Engineer
Focus Areas
ATS Harvesting, Job Data Normalization, Chrome Extension Automation, Tauri Desktop Companion
The Research Narrative
Strategic Problem
ATS systems do not share one clean model. Greenhouse, Lever, Ashby, Workable, and custom company pages expose different URLs, identifiers, fields, application states, compensation formats, and workflow...
What David Did
David designed a multi-surface architecture: Chrome extension capture, Tauri sidecar support, Supabase/PostgREST APIs, IndexedDB local cache, normalized ATS schemas, job dossier views, fit...
What Became Clearer
The project demonstrates a full-stack AI automation platform for career intelligence. It connects browser-native data capture, normalized schemas, local-first storage, AI reasoning, and...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
The hiring market is fragmented across ATS platforms, job boards, company pages, recruiters, and personal tracking systems. A serious job intelligence product needs to capture source data, normalize it, score it, track lifecycle, and explain what action to take next.
The Challenge
ATS systems do not share one clean model. Greenhouse, Lever, Ashby, Workable, and custom company pages expose different URLs, identifiers, fields, application states, compensation formats, and workflow patterns. The platform needed canonical records without losing source provenance.
What I Did
David designed a multi-surface architecture: Chrome extension capture, Tauri sidecar support, Supabase/PostgREST APIs, IndexedDB local cache, normalized ATS schemas, job dossier views, fit scoring, and application lifecycle events.
- •Built a Chrome extension surface for observing job pages, ATS application flows, board listings, browser context, and user workflow state
- •Used authorized browser automation and MITM-style JSON intercepts to capture structured ATS data from systems such as Greenhouse, Lever, Ashby, and Workable where available
- •Designed a canonical job schema that normalizes company, role, title, location, compensation, work model, ATS type, application URL, job overview URL, risk signals, freshness, and fit inputs
- •Created a Supabase/PostgREST backend pattern with flattened LLM-friendly views and RPC search endpoints for job dossiers and ranking workflows
- •Used IndexedDB as a local cache for large job corpora, browser-derived signals, application state, and enrichment outputs
- •Designed a Tauri desktop companion architecture to support native data access, local storage, MITM sidecar workflows, and cross-surface sync with the extension and web app
- •Built fit-scoring and prioritization logic around role fit, company fit, psychographic fit, work model, compensation, risk, hotness, and application effort
- •Designed job dossiers that transform raw ATS data into decision-ready summaries, risks, talking points, application strategy, and resume/story alignment
The Outcome
The project demonstrates a full-stack AI automation platform for career intelligence. It connects browser-native data capture, normalized schemas, local-first storage, AI reasoning, and workflow UX into a product that can support both job seekers and recruiting intelligence.
Research Outcomes
Signal Quality
Improved the trustworthiness of operational security signals
Operational Clarity
Translated complex security data into clearer operating views
Stakeholder Visibility
Made technical risk and status easier to explain
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Security Analytics
Signal investigation and event analysis
IAM / Access Control
Identity telemetry and access insights
SIEM Alert Debugging
Noise reduction and signal validation
Dashboard Development
Operational and executive views
Executive Reporting
Security data translated for leadership
Public-Safe Evidence
Shareable insights without sensitive data
Key Deliverables
- •Chrome extension job intelligence surface
- •Tauri desktop companion architecture
- •Next.js web dashboard
- •Supabase/PostgREST job intelligence backend
- •Canonical ATS job schema
- •Greenhouse, Lever, Ashby, and Workable normalization patterns
- •MITM-style ATS JSON intercept workflows
- •IndexedDB local job corpus cache
Tools & Technologies
Consulting Translation
The reusable pattern is not Disney-specific: normalize fragmented security telemetry, debug low-signal alert behavior, build trusted operating views, and give leadership evidence they can act on without exposing sensitive systems.