Intelligent Resume Parsing with OpenAI
Transform unstructured resumes into structured JSON with confidence scoring, evidence references, and async worker pipelines for scale
Feature Overview
Comprehensive resume parsing platform with AI-powered extraction and enterprise-grade features
Core Features
- Upload resumes in PDF, DOC, DOCX, and image formats
- Extract text with layout hints and page attribution
- OpenAI-powered structured JSON extraction
- Confidence scoring for each extracted field
- Evidence references back to source text
- Async worker pipeline with retry logic
- Single and bulk processing support
- Idempotency with checksum-based deduplication
Advanced Features
- Human review overlay without destroying AI output
- Multi-tenant isolation with API key auth
- OCR fallback for scanned documents
- Field normalization and validation
- Audit logging for compliance tracking
- Token usage tracking for cost reporting
- Job progress tracking with webhooks
- Export to ATS-compatible formats
System Architecture
Distributed architecture with async workers, multi-tenant data isolation, and enterprise security
React Web App
Upload UI with drag-and-drop, job tracking dashboard, parsed profile viewer, and review editor
FastAPI Backend
RESTful APIs for upload, job management, parsing orchestration, and result retrieval with auth
Worker Service
Celery/RQ workers for async text extraction, LLM parsing, normalization, and retry handling
PostgreSQL
Stores documents, extractions, parses, jobs, reviews, and audit logs with tenant isolation
Object Storage
Local storage for DEV, S3 for PROD with signed URLs and least privilege IAM
Redis Queue
Message queue for async job distribution with retry logic and dead-letter handling
Processing Pipeline
Five-stage async pipeline from upload to structured candidate profile with full auditability
1. Upload & Validation
Validate file type and size, compute checksum, store in object storage, create document and job records
2. Text Extraction
Extract text from PDF/DOCX with layout hints, OCR fallback for images, persist structured text with page attribution
3. LLM Parsing
Build snippet registry, chunk by section for token control, OpenAI extraction to strict JSON schema with confidence
4. Normalization
Normalize emails, phones, URLs, standardize dates to ISO, dedupe skills, detect inconsistencies and overlaps
5. Storage & Audit
Persist parsed JSON with evidence, log token usage, update job status, trigger webhooks, enable human review
Data Model
Postgres schema with tenant isolation, audit logging, and structured JSON storage
documents
File metadata, storage URI, checksum, status, tenant isolation
extractions
Raw and structured text from PDF/DOCX with extraction metadata
parses
Structured candidate JSON with confidence, evidence, and warnings
jobs
Async job tracking with status, progress, retry logic, and error handling
reviews
Human review overlay preserving original AI output with edit tracking
audit_logs
Compliance audit trail for all user actions and system events
Idempotency
Unique constraint on (tenant_id, checksum_sha256) enables cache hits for duplicate uploads
Multi-Tenant
All tables include tenant_id with row-level isolation and indexed queries
JSON Storage
JSONB columns for flexible schema evolution with PostgreSQL indexing support
Key Benefits
Transform your recruitment process with AI-powered automation and enterprise-grade reliability
10× Faster Processing
Automated extraction eliminates hours of manual data entry, processing 100+ resumes per hour with async workers
85-95% Accuracy
AI-powered extraction with confidence scoring and evidence references ensures high-quality structured data
Enterprise Security
Multi-tenant isolation, API key auth, audit logging, and encryption at rest meet compliance requirements
Scalable Architecture
Horizontal worker scaling with Redis queue, idempotency, and retry logic handles enterprise volume
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