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.
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.
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.
Frequently asked questions
What did this team achieve with this AI workflow?
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…
What tools did this team use?
Nanonets.
What results were reported?
Previous OCR accuracy: ~75%; Data extraction accuracy with Nanonets: much higher accuracy; Time to onboard users (Nanonets): a few hours; Employee training time (previous solution): weeks (source-reported, not independently verified).
What failed first in this deployment?
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 extrac…
How is this data entry ops AI workflow structured?
Files ingested from email → AI document type detection → Specialized model data extraction → Validation rules check → Export to proprietary software.