Invoice processing · Production

Tapi automates property maintenance invoice processing with Nanonets Invoice OCR

The problem

Tapi manually processed large volumes of property maintenance invoices each month across hundreds of agencies, which was a major bottleneck in turnaround time. An outsourced data entry model proved unscalable and could not keep up with Tapi's rapid growth.

First attempt

Tapi's outsourced data entry model was unscalable and unable to keep pace with the company's rapid growth.

Workflow diagram · grounded in source
1
Invoice OCR capture
Trigger
Nanonets Invoice OCR captures information from invoices and sends it to Tapi.
▾ source quote
“Nanonets's AI tool- Invoice OCR captures information from invoices and sends it to Tapi”
2
AI field extraction
Ai action
The AI extracts required information from invoices, pulling the required information correctly 94% of the time.
▾ source quote
“Tapi trained the AI to extract required information from the invoices, post which they perform checks to ensure if all fields are populated correctly and in line with expectations. The AI is highly efficient and pulls the required information correctly …”
3
Field population checks
Validation
Tapi performs checks to ensure all fields are populated correctly and in line with expectations.
▾ source quote
“they perform checks to ensure if all fields are populated correctly and in line with expectations”
4
Continuous model retraining
Feedback loop
Accuracy is expected to improve over time by continuing to train the Nanonets platform with new invoices.
▾ source quote
“this is expected to improve over time by continuing to train Nanonets platform with new invoices”
Reported outcome

Nanonets Invoice OCR extracts required invoice fields correctly 94% of the time, the integration was operational within a week, is maintained by a non-technical staff member, and Tapi reports massive gains in savings and productivity.

Reported metrics
AI extraction accuracy94%
Integration deployment timea week
Issue resolution time~15 mins
Savings and productivity gainsmassive gains in savings and productivity
Show all 5 reported metrics
AI extraction accuracy94%
integration deployment timea week
issue resolution time~15 mins
savings and productivity gainsmassive gains in savings and productivity
monthly invoice volume processed100,000+
Reported stack
NanonetsInvoice OCR
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Source
https://nanonets.com/customer-success-story/tapi-automates-property-maintenance-invoice-using-nanonets
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Nanonets Invoice OCR extracts required invoice fields correctly 94% of the time, the integration was operational within a week, is maintained by a non-technical staff member, and Tapi reports massive gains in savings…

What tools did this team use?

Nanonets, Invoice OCR.

What results were reported?

AI extraction accuracy: 94%; Integration deployment time: a week; Issue resolution time: ~15 mins; Savings and productivity gains: massive gains in savings and productivity (source-reported, not independently verified).

What failed first in this deployment?

Tapi's outsourced data entry model was unscalable and unable to keep pace with the company's rapid growth.

How is this invoice processing AI workflow structured?

Invoice OCR capture → AI field extraction → Field population checks → Continuous model retraining.

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