Fortune 500 energy management company digitizes multi-format document processing with Nanonets AI
The client relied on a traditional OCR provider that achieved only ~75% accuracy, required weeks of employee training, lacked flexibility for their unique use case, and offered no automation beyond raw extraction — leaving teams to manually verify each document against rules.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Files ingested from email
The automation solution picks files from email as the entry point.
Tools used
Nanonets
Outcome
Nanonets delivered an end-to-end automation solution that picks files from email, classifies document types, extracts data with much higher accuracy via specialized models, checks validation rules, and exports to the client's proprietary software — with users able to start in a few hours.
What failed first
Their previous traditional OCR provider delivered only ~75% accuracy — even lower for certain document types or languages — was difficult to learn, inflexible, and provided no automation capabilities beyond raw extraction.