AI-Powered Careers Chatbot

The idea

Recruiting teams spend a lot of time on early-stage phone screens — calls that often end quickly because the candidate simply isn’t a fit. This prototype explores a different model: what if a careers page could do that first-pass qualification conversationally, while simultaneously making candidates feel helped rather than filtered out? The framing matters. This isn’t a bot that replaces human contact — it’s a career coach that helps candidates put their best foot forward before they ever talk to a recruiter.

How it works

The chatbot lives as an overlay on the careers page — a glassmorphism chat panel in the bottom-right corner, with a real-time data console in the bottom-left showing exactly what’s being collected. The conversation flow collects name, email, skills, salary expectations, and location through natural dialogue. An OpenAI API integration handles skill extraction and job matching, with graceful keyword-based fallbacks if the LLM isn’t available.

The real trick is the Lever API integration. When a candidate completes the conversation, their profile is formatted into Lever-compatible data — skills become structured tags (skill:javascript, category:design), experience levels are extracted, salary ranges are preserved in custom fields, and every submission is tagged source:ai-chatbot so recruiters can filter by acquisition channel. A Netlify serverless function handles the API proxy to avoid CORS issues, and a duplicate-check endpoint prevents the same email from creating multiple records.

The returning candidate flow

One of the more interesting moments in the demo is what happens when a known email is entered. The chatbot recognizes the candidate and surfaces what it already knows about them — a check-in moment that feels genuinely personal. “Hey, I recognize you, welcome back” hits differently than a static FAQ page. That recognition layer, pulling from existing Lever records, opens up use cases around candidate re-engagement and pipeline warm-up that most careers pages don’t even attempt.

Why it matters

This is a proof of concept for how recruiting infrastructure can be designed, not just configured. The same pattern applies well beyond hiring: onboarding flows that read from and write to a CRM in real time, waitlist tools that qualify leads conversationally before a sales call, or intake forms for agencies that capture structured client data through chat. The key architectural insight — LLM for understanding, serverless functions for secure API proxying, and a structured data layer for downstream tooling — is reusable across a lot of contexts. The chatbot just happens to be the most human-feeling entry point into what is otherwise a data pipeline.