Case Study: An LLM Resume-Screening Pipeline That Signed Its First Client in Week 3

Client: ApplicoAI Build: AI recruitment platform: LLM resume parsing, candidate scoring, full screening pipeline Timeline: 14 days to customer demo Stack: Next.js, MongoDB, LLM APIs, AWS Outcome: Demoed to customers on Day 14; first paying client signed the same week

"Dhruv built our AI recruitment platform - LLM-powered resume parsing, candidate scoring, the whole pipeline. We demoed to customers on Day 14 and signed our first paying client the same week." — Founder, ApplicoAI

The problem

Recruiters drown in unstructured documents. Every resume is a differently formatted PDF holding the same underlying facts: experience, skills, education, trajectory. Screening them manually is slow, inconsistent, and expensive. ApplicoAI's founders knew an LLM could read resumes the way a human does, at machine speed, but they needed a production system, not a demo notebook.

The build

Fourteen days, one frozen scope, three hard requirements:

Parsing that survives real documents. Real resumes are messy: two-column layouts, tables, scanned PDFs, inconsistent headings. The pipeline extracts structured candidate data from whatever formats recruiters actually receive, with validation on every field rather than trusting the model blindly.

Scoring that can be explained. A score with no reasoning is useless in hiring. Every candidate score traces back to specific evidence in the source document, so a recruiter can see why the system ranked someone and override it when the context demands.

A pipeline, not a parlor trick. Upload, parse, score, rank, review: the full workflow in a real UI, deployed on AWS, ready to put in front of paying customers.

The outcome

Customer demo on Day 14. First paying client signed the same week. The difference between a startup with an idea and a startup with revenue was one sprint.

Why this matters beyond recruiting

Resumes are just documents your business needs to understand at scale. The identical architecture applies to invoices, contracts, support tickets, product catalogues, compliance documents, and policy manuals: LLM extraction grounded in your real documents, with validation, explainability, and human override built in.

That is the Knowledge AI Pilot: a working AI system on your company's own documents, live in 14 days, at a fixed $12,000. Book a scoping call to scope your document workflow.