Invoice processing · Production

Nanonets automates construction invoice processing, delivering 10x speed increase and 7,200 work hours reprioritized

The problem

A Minnesota-based construction company processed a large volume of invoices from over 40 suppliers with different formats, spending significant time and resources on manual data entry and verification before data could be entered into accounting software.

First attempt

The company first tried Textract and then Abbyy; both failed to handle multi-page invoices, multi-line fields, and unstructured documents with imperfections. Accuracy was poor requiring excessive rework, the verification UI took up to ~5 minutes per invoice, and it proved impossible to retrain either algorithm, forcing the company to abandon both tools.

Workflow diagram · grounded in source
1
Invoice submission via API
Trigger
The AP team at the client site sends invoices to Nanonets using a simple API integration.
▾ source quote
“The AP team at the client site sends invoices to Nanonets using a simple API integration”
2
Document type classification
Ai action
The Nanonets algorithm filters, recognizes, and parses different document types such as invoices, material lists, packing lists, and purchase orders simultaneously.
▾ source quote
“The Nanonets algorithm can filter, recognise and parse different types of documents such as invoices, material lists, packing lists, purchase orders etc. all at the same time! The Nanonets AI can segregate critical documents from non critical ones, and can …”
3
Real-time invoice data extraction
Ai action
Nanonets AI automatically processes invoices in real time and populates a CSV stored in the AP team's Drive.
▾ source quote
“Nanonets AI automatically processes these invoices in real time and populates a csv that gets stored in the AP team's Drive”
4
Data validation and formatting
Validation
Custom validation rules reorganize extracted data into convenient output layouts and formats for further processing.
▾ source quote
“Custom validation rules allow you to reorganize data into convenient output layouts and formats that are easier for further processing”
5
CSV upload to accounting software
Integration
The AP team downloads the CSV at end of day and uploads it to their accounting software.
▾ source quote
“The AP team, at the end of the day, downloads the csv and uploads it to their accounting software”
6
Continuous model retraining
Feedback loop
The AI retrains itself with processed data to maintain accuracy even as new suppliers are onboarded each month.
▾ source quote
“the AI retrains itself with the data you process. This ensures that the algorithm functions accurately even if you onboard new suppliers each month”
Reported outcome

After switching to Nanonets, the company automated the most labor-intensive steps of document processing, achieving a 10x increase in processing speed and reprioritizing 7200 work hours.
Invoice verification that would earlier take over 5 minutes for batches of documents could be blazed through in under 30 seconds.

Reported metrics
Invoice processing speed10x increase in processing speed
Work hours reprioritized7200
verification time before Nanonets (per invoice)~5 minutes per invoice
batch verification time before Nanonetsover 5 minutes
Show all 8 reported metrics
invoice processing speed10x increase in processing speed
work hours reprioritized7200
verification time before Nanonets (per invoice)~5 minutes per invoice
batch verification time before Nanonetsover 5 minutes
verification time with Nanonetsunder 30 seconds
Nanonets setup timejust about 1 day
prior tool struggle durationabout 3 months
number of supplier invoice formats handledover 40 different suppliers
Reported stack
NanonetsTextractAbbyyNanonets OCR API
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Source
https://nanonets.com/customer-success-story/construction-invoice-processing
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After switching to Nanonets, the company automated the most labor-intensive steps of document processing, achieving a 10x increase in processing speed and reprioritizing 7200 work hours.

What tools did this team use?

Nanonets, Textract, Abbyy, Nanonets OCR API.

What results were reported?

Invoice processing speed: 10x increase in processing speed; Work hours reprioritized: 7200; verification time before Nanonets (per invoice): ~5 minutes per invoice; batch verification time before Nanonets: over 5 minutes (source-reported, not independently verified).

What failed first in this deployment?

The company first tried Textract and then Abbyy; both failed to handle multi-page invoices, multi-line fields, and unstructured documents with imperfections.

How is this invoice processing AI workflow structured?

Invoice submission via API → Document type classification → Real-time invoice data extraction → Data validation and formatting → CSV upload to accounting software → Continuous model retraining.

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