AI product · Full-stack delivery
RoleVerity: Career Intelligence SaaS
An end-to-end career intelligence workspace that connects job discovery, explainable fit analysis, resume tailoring, ATS review, application tracking, and subscription operations.
The problem
Job seekers often move between disconnected job boards, resume tools, ATS checkers, spreadsheets, and application trackers. Every handoff loses context and makes it harder to understand why an opportunity is—or is not—a good fit.
RoleVerity brings those activities into one user-owned workflow with transparent scoring, source attribution, and explicit control over personal career data.
The product
The platform connects a protected Career Profile to:
- Multi-provider and direct-company job discovery
- Saved and scheduled searches
- Explainable qualification and relevance filtering
- A Job Fit Analyzer
- An ATS Scanner
- An AI-assisted Resume Studio with PDF and Word exports
- An application Pipeline and activity history
- Usage, entitlement, and subscription management
My role
I carried the work from problem framing through product architecture, interface design, implementation, security hardening, production verification, and operational documentation.
This was not a single-feature prototype. It required coordinating application behavior, external services, private user data, scheduled work, billing state, document generation, deployment, monitoring, and support procedures as one system.
The hardest design decisions
Make relevance explainable
Provider data is incomplete and inconsistent. The product normalizes multiple schemas, preserves source attribution, enforces geographic eligibility, merges likely duplicates, and separates returned jobs from qualified results so users can understand the funnel.
Keep private data owned by the user
Career profiles, resumes, generated materials, saved jobs, and application history sit behind authenticated ownership boundaries. Database policies, private storage, export controls, and cross-user access checks are treated as product requirements—not launch cleanup.
Treat AI as one part of the workflow
AI supports analysis and document generation, but the experience keeps criteria, source data, user choices, and editable outputs visible. The design favors useful assistance over an unexplained answer.
Build for operations, not just the demo
The launch process includes usage controls, spending safeguards, provider reliability handling, scheduled-search recovery, privacy-safe error reporting, alerting, billing lifecycle verification, backups, and rollback documentation.
Verification evidence
As of the current production-handoff record:
- The primary authenticated workflows are implemented.
- Live Stripe checkout, billing portal access, entitlement changes, and webhook processing were verified end to end.
- Production ownership isolation and private resume-storage boundaries were tested.
- A 142-test local regression gate, typecheck, lint, and production build passed during launch hardening.
- GitHub-hosted required checks were proven to block merging when a check failed.
- Production error reporting and operational alert delivery were verified without retaining sensitive request content.
What the work demonstrates
- Translating a fragmented real-world workflow into a coherent product system
- Moving between user needs, interface decisions, backend behavior, and production operations
- Integrating external providers while preserving attribution and failure visibility
- Applying security, privacy, testing, documentation, and rollback as part of delivery
- Learning unfamiliar systems quickly enough to ship and verify a working product
Evidence note: This case study reflects the RoleVerity repository and production-handoff records. The approved production link is now available from the project card; public screenshots and usage outcomes remain pending publication review.