AI-Powered Intelligent Workflow Automation
Engineered an asynchronous document extraction and verification pipeline with strict JSON schema validation.
Operational & Architectural Constraints
An operations-heavy workflow required staff to manually review hundreds of unstructured multi-page PDF documents, extract key financial line items, cross-check against internal databases, and manually type values into ERP records. The manual process was slow, error-prone during peak periods, and difficult to scale without proportional headcount growth.
System Design & Implementation
We engineered an intelligent automated ingestion pipeline using Python, FastAPI, and asynchronous Celery workers backed by Redis. Documents are ingested via secure endpoints, converted to standardized text/image representations, and processed using LLMs with strictly enforced Pydantic JSON schemas. A hybrid search vector pipeline validates extracted entities against enterprise master records, and a human-in-the-loop review interface flags low-confidence extractions for manual operator sign-off.
FastAPI + Celery/Redis
Returns instant HTTP 202; converts multi-page PDFs to sanitized OCR text streams.
LLM Schema Validation Gate
Forces LLM inference into deterministic JSON matching strict Pydantic V2 models.
Confidence & Verification
≥90% confidence auto-commits to ERP; <90% routes to human-in-the-loop review.
End-to-End System Data Flow
System Data Flow: AI-Powered Intelligent Workflow Automation
Documents arrive via API, enter an async Celery task queue, undergo OCR & structured LLM extraction with Pydantic validation, and route to master systems or operator review.
Accepts PDF/image uploads, verifies file signatures, stores raw files in secure storage, and dispatches job ID.
Strict tenant isolation and atomic data boundaries enforced at both gateway validation and persistence/table tiers.
Every stage emits structured trace contexts (OpenTelemetry / correlation IDs) for distributed debugging and latency profiling.
Key Engineering Decisions
Pydantic V2 schema enforcement over unconstrained prompt outputs
Guaranteed 100% deterministic JSON structure, preventing parser crashes in downstream ERP systems.
Requires retry wrapper with error-feedback prompts when an LLM fails schema conformance on first pass.
Asynchronous task queue over synchronous HTTP handling
Allowed the ingestion API to return instant HTTP 202 Accepted, protecting upstream clients from LLM inference timeouts.
Clients must poll job status endpoint or subscribe to webhook completion callbacks.
Measured System Results & Stability Guarantees
In accordance with our engineering verification policy, we document verified technical benchmarks and architectural milestones rather than unverifiable marketing claims.
AI Integration & Intelligent Automation
Explore our full deliverables, tech stack, and engagement models for this capability.
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