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Case study · Digitization

Automation of Invoice Processing Workflow

A consulting company needed field employees to access equipment invoices, locate service centers, and repair damaged devices faster. iBoss digitized invoices and deployed high-performance intelligent document OCR systems to extract data, store it centrally, and automate lookup.

Client
A consulting company with field teams across India
Disclosure
Anonymised

Headline metrics

3 quarters of a minute
Rejected extraction time
Less than 5 seconds
Final extraction time

Additional context

Automating invoice extraction and service-center lookup for field equipment repairs.

01 / Challenge

The problem in front of us.

Employees and consultants traveled across regional and international territories with DSLR cameras, laptops, tablets, scanners, printers, GPS devices, and other equipment. When gadgets were damaged, original invoices were unavailable, physical copies were hard for the office to locate, remote employees needed scanned invoices, and unfamiliar remote locations made authorized service centers difficult to find.

02 / Approach

How we set the work up.

iBoss designed and deployed high-reliability intelligent document OCR systems, extracting details and storing them in a centralized cloud repository. For extraction, iBoss tested object detection over a labeled invoice dataset, but that approach took three quarters of a minute. The team moved to OCR followed by Named Entity Recognition, reducing extraction time to less than 5 seconds.

03 / Solution

What we built.

The web application stores invoice information centrally and locates the nearest authorized service center based on the remote employee's geolocation, with options to connect by call or WhatsApp. The workflow uses OCR, NER, and a Selenium-trained model to find contact details and service-center locations.

04 / Outcome

What it has held up to.

The workflow automated the asset repair process, reduced the need for manual invoice searching, saved productive hours and days, stored extracted information in a common database, and enabled the client to use the data for critical decision-making insights.

Stack

What it runs on.

  • OCR
  • Named Entity Recognition
  • Selenium
  • Cloud repository
  • Geolocation
  • Web application
  • Intelligent Document OCR Systems

Tell us what you're trying to ship.

We'll start with a two-week diagnostic. No slides, no promises we can't keep.