LLM Automation Suite
End-to-end AI workflow automation that replaced 40+ hours/week of manual processing
01 The Problem
A business operations team spent 40+ hours per week manually parsing documents, extracting structured data, routing tasks through approval workflows, and updating CRM records. The process was error-prone, slow, and entirely dependent on human availability. Backlogs built up during peak periods, costing revenue and frustrating clients.
02 The Solution
Designed and built a multi-stage LLM automation pipeline using Python, GPT-4, and LangChain-style orchestration. The system automatically ingested documents, extracted structured fields using custom prompt chains, validated outputs against business rules, routed tasks to appropriate queues, and updated Salesforce records via API — all without human intervention for standard cases.
03 What I Built
- ▸ Custom document ingestion pipeline (PDF, email, form data)
- ▸ LLM extraction chain with structured output validation
- ▸ Business rules engine for routing and exception handling
- ▸ Salesforce API integration for automated CRM updates
- ▸ Real-time monitoring dashboard for queue status and error tracking
- ▸ Fallback workflow for edge cases requiring human review
04 Business Value
The automation suite replaced the equivalent of two full-time analyst roles while simultaneously improving accuracy and throughput. ROI was achieved within the first 60 days of deployment.
Measured Outcomes
- ✓ Reduced manual document processing time by 87%
- ✓ Eliminated 40+ hours/week of repetitive analyst work
- ✓ Processing accuracy improved from ~78% (manual) to 96% (automated)
- ✓ CRM records updated in real-time vs. 24–48 hour lag
- ✓ System handled 3× volume during peak periods without additional staff
Technology Stack
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