OfferLens: A Privacy-First Job Offer Reality Checker for Candidates

Finding employment in today's market is a challenging endeavor. For one of my friends, the process has stretched over eight months, filled with numerous applications, ghosted recruiters, take-home projects, and countless rounds of interviews. As the job hunt neared its end, securing an offer was a cause for celebration. However, upon reviewing the fine print, their excitement rapidly transformed into anxiety.

The recruiter had promised an unconditional $165,000 base salary, 100% remote flexibility, and unlimited paid time off via email and Zoom. The actual 14-page contract, transmitted through email and Zoom, presented a stark contrast: the base salary was $132,000 with a discretionary bonus contingent upon board approval. It also stipulated mandatory in-office attendance for four days a week, an 18-month non-compete clause, and a very tight 48-hour signature deadline.

The sheer volume of information and the emotional toll of this experience underscored a critical issue: job seekers in extended job searches are at a significant information and emotional disadvantage. To address this, I developed OfferLens, a privacy-first job offer reality checker.

OfferLens is not a generic summarizer or chatbot but an evidence-based investigation assistant grounded in four key principles. First, it utilizes Multi-Document Dossier Ingestion, allowing candidates to upload or paste all their fragmented offer materials, including contracts, recruiter emails, LinkedIn and WhatsApp messages, salary breakdowns, and interview guidelines.

Second, the Cross-Document Contradiction & Delta Engine cross-references the promises in the offer against the actual contract, highlighting side-by-side discrepancies. For instance, it could detect a base salary disparity, a downgrade in remote work policies, or changes to paid time off. Third, the Calibrated Signal & Risk Detector employs objective language to describe potential concerns without presuming wrongdoing, such as "The document contains a request for payment," or "Potential concern detected."

It emphasizes that the final decision rests solely with the candidate. Lastly, the Missing Information & Blind-Spot Audit flags hidden contractual omissions, such as undisclosed stock option strike prices, omitted health insurance effective dates, or the absence of protections for personal side projects outlined in Prior Inventions Exhibits.

It also provides tailored negotiation scripts for every discrepancy, available in three tones: Polite & Enthusiastic, Direct & Professional, and Firm Due Diligence.

OfferLens prioritizes privacy and security by implementing a Local-First Privacy Shield. When candidates upload their documents, the app runs an in-browser PII sanitizer that automatically redacts sensitive information like names, social security numbers, addresses, and phone numbers locally before any analysis occurs. This ensures that no personal data leaves the user's device, with a visual audit trail available for transparency.

The application is accessible as a live local and web demo, where users can test with pre-loaded sample scenarios. These scenarios include a remote bait-and-switch, equipment check red flags, an opaque startup equity trap, and a clean and transparent offer. The app generates printable PDF reports and formatted Markdown dossiers to help candidates discuss their findings with mentors, partners, or employment attorneys, enhancing their negotiation power and ensuring they make informed decisions about their future employment.