LP.
SYSTEM CASE STUDYestana.in

Estana

An AI-powered, multi-tenant recruitment platform built for recruitment agencies. Designed and shipped the platform end-to-end, from database architecture and candidate matching to AI-powered resume parsing and production infrastructure.

Next.jsNode.jsPostgreSQLPrismaRAG PipelinesPrompt EngineeringOpenAIDigitalOcean
VERIFIED BENCHMARKS & IMPACT
Benchmark

3,000+ candidates processed in 1.5 months

Benchmark

RAG & Few-Shot Prompts for 98% Parsing Precision

Benchmark

Sole Architect & Engineer from Schema to Infra

01 / THE PROBLEM

Operational Bottleneck

SME recruitment agencies waste hundreds of hours manually reviewing resumes, matching candidates, and managing pipelines. Traditional ATS tools rely on basic keyword matching that misses top-tier candidate nuance and contextual skills.

02 / SYSTEM ARCHITECTURE
01

Designed & built multi-tenant SaaS backend with PostgreSQL schemas and Prisma ORM for total agency data isolation

02

Structured LLM prompt chains with JSON schema enforcement for zero-shot resume parsing and skill extraction

03

RAG (Retrieval-Augmented Generation) pipeline using dense vector embeddings for semantic candidate-to-job matching

04

Contextual candidate re-ranker evaluating candidate depth against job specs with automated reasoning summaries

05

Next.js full-stack agency dashboard featuring real-time candidate pipelines and automated applicant scoring

06

Containerized production deployment on DigitalOcean with automated database backups and zero-downtime migrations

03 / RESULTS & PRODUCTION IMPACT
-Processed 3,000+ candidates across active recruitment agency pipelines within 1.5 months of production launch
-RAG semantic matching reduced candidate screening time from hours to automated instant candidate rankings
-High-throughput multi-tenant AI infrastructure actively serving SME recruitment agencies at estana.in

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