We work on a variety of processes collaboratively and Nanonets is part of our daily flow here. Nanonets has been crucial in helping us scale processes as we grow.
We’ve done a good job of getting a very high percent of our invoices. It remembers everything and so quickly, that's fantastic! Our service representatives love the easy and fast user interface that they were able to use immediately without any special training.
- Adam, CEO
Classic Build vs Buy Dilemma
Significant engineering time was spent on building a traditional data extraction architecture, as well as on experimenting with existing traditional data extraction platforms.
High expense on Manual Invoice Processing
An annual salary of over 100K to simply maintain a manual verification component — unjustified ROI.
Focus on product = global scale
Renewed focus on onboarding more clients - more quickly and with more ease, customer experience and delivering the clients their favorite dream homes!
Seamless Invoice Processing Solution
Automated Accounts Payable Solution improving team's productivity and value creation
Minnesota based Construction Company
Construction and Demolition
Table Extraction, Accurate Automation, Integration with Accounting Software
Renewed focus on customer experience and onboarding more clients
Adam is the CEO of a construction company based in Minnesota. He explains his role in building homes quite simply: “I have to accomplish my short term day to day goals in order to accomplish my long-term goals. I need to be a people person who helps clients build their favourite homes and buildings!” He adds
But don't forget all that paperwork everyday. I suppose, don’t forget about the computer work too.
With over 400 successful projects under their belt, this Minnesota-based construction major has been leveraging world class building solutions along with 50 years of local construction knowledge to consistently rank among the top 100 metal builders nationwide. Involved in planning & design, budgeting, design-build & construction management for clients such as Polaris, AGCO, and Winnebago among others, this family owned construction company has been a regular at Butler Manufacturing’s annual awards program; picking up awards such as the Builder of the Year, National High Performance Builder, and The Butler Club over the years.
Construction is the second-least digitized industry in the United States, yet it accounts for 10 percent of the country’s GDP. A staggering figure! It is to be noted, however, that manual interventions and processes still play a huge role in this largely paper document driven industry. A lot of time and resources are spent in data entry & manual verification.
Embracing automated document processing could therefore optimize processes and save a lot of time and money. Traditional automation solutions have so far been incapable of dealing with unstructured data; documents in the field come in varying shapes & formats and are often difficult to scan accurately due to marks/blemishes, handwriting or background.
So while automation clearly provides benefits, the search is still on for a trustworthy, accurate & secure automation software that can extract and process unstructured data in all its diversity and present it in a convenient manner for further use within an organization (Accounting, HR etc.). And that’s where Nanonets can help!
This construction organization deals with a large volume of invoices that need to be processed and the processed data entered into an accounting software. These invoices present detailed charges for repairs and contractor assignments. The management implemented an automated solution (first with Textract and then with Abbyy) to capture all the information from these invoices in a more efficient manner; avoiding manual processes that are fraught with delays and errors. The automated solution was expected to pull out the following typical fields from the invoices:
“You have to cut different pieces of invoices, and then sew them up well enough to keep financing running, smoothly and efficiently. After about 3 months of struggling with Textract and Abbyy, I feared we’d have to go back to entering data manually. It was a disaster” - Adam, the CEO.
The experiments with Textract and Abbyy were not encouraging.
Finally it was impossible to train the algorithms to rectify these crucial flaws! The company was forced to drop Textract & Abbyy, and look for an alternative.
A quick trial run demonstrated Nanonets’ superior capabilities. In comparison to Abbyy & Textract, Nanonets was both easy and flexible to set up, requiring just about 1 day. It also handled multi-page invoices and identified multi-line items with ease; something that both the previous tools had failed at. Nanonets also customized column headers allowing it to process complex invoices more efficiently. Nanonets’ AI ensured a high accuracy while processing documents requiring minimal rework or revision. The automation handled unstructured data without much difficulty. The AI also extracted information from documents with imperfections & blemishes quite easily.
And verification that would earlier take over 5 minutes for batches of documents could be blazed through in under 30 seconds with Nanonets’ modern user interface!
Any automated solution approved by the company had to address the following four critical problems:
Invoice and Material list data are provided together in PDFs. Both documents are in the same file and they look alike. Can the software recognize the document type and extract the correct data?
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 selectively parse critical documents to extract the required key value pairs.
There are over 40 different suppliers with different invoice formats. Will the software need to be trained separately for each supplier? What if new suppliers are on-boarded every month? Does the AI need to be retrained for each new supplier?
Nanonets offers an AI based solution which is template agnostic. Our AI learns to understand the document based on a large number of features. Once trained on a diversified data set, the model can recognize the information in any new format.
Additionally, the AI retrains itself with the data you process. This ensures that the algorithm functions accurately even if you onboard new suppliers each month; helping you to scale efficiently while maintaining accurate results.
The Accounting Software can ingest the API or CSV only if the fields are presented in a certain format. Can the output be customized accordingly?
Nanonets is not bound by the template of the document at all. Custom validation rules allow you to reorganize data into convenient output layouts and formats that are easier for further processing. You can export the appropriately organized fields/data as an xml, xlsx or csv file.
Sometimes, the Product Description cell in the Invoice table also contains the Article Number and Weight associated with the Product Name. Can those be pulled out as a separate column?
With Nanonets, you can pull out specific fields under each row in the table as separate columns.
"Instead of spending hours and hours engineering a workflow to sync key information from thousands of invoices to our software, we're able to automate this accurately and spend more time on highly strategic initiatives."
Extracting data from invoices provided by over 40 different suppliers (and growing) can be a challenging problem. Nanonets’ AI refined its models by using the client’s existing data; picking out the particular needs of the business. Unlike most document processing APIs out there that are quite rigid on the type of data they can work with, Nanonets isn’t bound by the template of the documents!
Adapting Nanonets for the specific business rules required by the client was easy and straightforward. Nanonets can capture as many fields of text/data that you like and present it in any desired fashion. For instance, translating the date format to ISO 8601 or extracting the currency of the total invoice, before populating the csv was easily enabled by Nanonets’ plug and play webhooks. Captured data was presented in tables or line items with custom validation rules.
Businesses often face dynamically changing requirements; for example, the company brings on board a new vendor or is presented with a new invoice format. Nanonets OCR API has enabled Adam and his team to tackle such situations with ease. Nanonets can easily re-train existing models with new data. Adam and his team have since been able to:
After moving to Nanonets, the construction company automated the most labor intensive steps of the document collection process; resulting in a 10x increase in processing speed & 7200 work hours being reprioritized. Nanonets’ expertise in working with construction companies and familiarity with industry standards regarding documentation made it an ideal fit for Adam’s company!
The benefits of using Nanonets go just beyond better accuracy, experience and scalability. Here are 8 reasons that highlight the Nanonets advantage:
Training & working with custom data - Most document processing APIs out there are quite rigid on the type of data they can work with. Nanonets isn’t bound by such limitations. Nanonets uses your own data to train models that are best suited to meet the particular needs of your business.
Easy to use & flexible - Adapting Nanonets for your specific business needs is easy and straightforward. From creating custom OCR models & retraining them to adding new fields & handling integrations, Nanonets can handle it all.
Learns & retrains continuously - Businesses often face dynamically changing requirements and needs. To overcome potential roadblocks, Nanonets OCR API allows you to easily re-train your models with new data. This allows your OCR model to adapt to unforeseen changes.
Customise, customise, customise - Nanonets can capture as many fields of text/data that you like and present it in any desired fashion. Captured data can be presented in tables or line items or any other format of your choice with custom validation rules. Always remember that Nanonets is not bound by the template of your document!
Requires almost no post-processing - While most document processing APIs simply grab and dump data, Nanonets extracts only the relevant data and automatically sorts them into intelligently structured fields making it easier to view and understand. This does away with a lot of time spent in revision and verification.
Handles common data constraints with ease - Nanonets leverages deep learning & object detection techniques to overcome common data constraints that greatly affect text recognition and extraction. Nanonets AI can recognize and handle handwritten text, images with low resolution, images with new or cursive fonts and varying sizes, images with shadowy text, tilted text, random unstructured text, image noise, blurred images and more. Traditional OCR APIs are just not equipped to perform under such constraints; they require data at a very high level of fidelity which isn’t the norm in real life scenarios.
Works with non-English or multiple languages - Since Nanonets focuses on training with custom data, it is uniquely placed to build a single model that could extract text from documents in any language or multiple languages at the same time.
Requires no in-house team of developers - No need to worry about hiring developers and acquiring talent to personalize Nanonets API for your business requirements. Nanonets was built for hassle-free integration. You can also easily integrate Nanonets with most CRM, ERP or RPA software.
Levie, Adam’s business associate, weighs in on one aspect of this project:
“The ‘location of the job site’ data is a new field in the contracts. Earlier, I would pull up all invoices individually or email the clients to add this info; which was a horrible experience! Instead now, Nanonets automatically pulls out this field and feeds it into my Contractor template using Zapier.”
With a streamlined system in place to process documents, the company is now implementing Nanonets for other projects. Next up is an efficient way to auto-populate Contract Agreements with Nanonets through a Zapier integration. The goal is to organize fields extracted by Nanonets in an appropriate manner and create an official agreement for potential customers.
Automating the process of providing invoices, quotes or agreements is an effective way to optimize the sales funnel and plug leaks. Automation helps convert more Prospects into Sales by removing unnecessary delays, reducing turnaround times, and avoiding human errors.
Adam’s construction company now spends less time worrying about paperwork and more on their core competency: building their customers’ dream projects!