Case Study - AI Job Application Sidekick
We built Easy-AIpply so you can scan a job listing once and draft many tailored answers with shared context, then get sharper drafts the more you use it.
- Client
- Easy-AIpply
- Year
- Service
- Product Build + AI Agent Architecture
Overview
We built and launched Easy-AIpply as our own product: an AI job-application sidekick for people who are tired of the slow loop. Copy the listing into a chat. Wait. Copy one answer back. Repeat for the next question. Lose the shared context of the role somewhere along the way.
Easy-AIpply flips that. Open a listing, scan it once, and draft answers for many fields in one pass with the full job description kept in shared context. Review and edit in a Chrome side panel. You stay in control: the extension reads on demand and never auto-fills the page, so you submit on your terms.
What we did
- Plasmo Chrome Extension (On-Demand Scan)
- Go + Gemini Agent Core
- Next.js Web Portal
- Continuous Learning / Preference Memory
- Deal-Breaker Checks
- Tailored Resumes & Cover Letters
Results
The product ships as a clean split: a Plasmo + React side panel as the eyes, a Go agent with Gemini as the brain (chosen for cost efficiency and learning speed), and a Next.js portal for profile, billing, and preferences. Users get multi-field answers and tailored documents without the tab-switching grind.
Continuous learning makes it stickier over time. When someone edits an answer or document, we store field-answer preferences, preference text, and per-question instructions, then inject those at prompt time on later scans. No model fine-tuning, just preference memory that nudges drafts closer to their voice and criteria the more they apply.
The result is a sidekick that feels faster on day one and smarter on day thirty: apply smarter, keep your accounts safer, and stop losing context between questions.