AI / ML / Data Science — AutoFlow (AI Automations) Client / Industry AutoFlow AI automation Timeline 10 weeks ongoing tuning Team Automation architect RPA developer ML engineer BA Problem Back‑office teams spent hours on repetitive document processing, approvals, and data entry across multiple systems. Solution Tkmetrix delivered an AI automation platform combining OCR, NLP, and RPA to extract data from documents, validate against business rules, and automate downstream system updates. Tech stack Key features delivered Document ingestion pipeline with OCR and NLP entity extraction Business rule engine for validation and exception routing RPA bots to post validated data into target systems and trigger approvals Outcomes 70% reduction in manual data entry time for targeted processes 50% fewer processing errors after validation rules applied ROI achieved within 6 months for pilot processes Project process 1 Idea Process mapping workshops to identify high‑value automation candidates. 2 Scope refinement Define SLAs, exception thresholds, and integration points. 3 Estimation Time & materials with pilot scope and scale estimate. 4 NDA & Agreement NDA + SOW; data handling and retention clauses. 5 Development Build extraction models → RPA bots → pilot run. 6 Launch Pilot on 2 processes, then scale.