Be it marketing, sales, or even security, most departments in large organizations today have a range of SaaS tools at their disposal to help make their work more efficient. But people in corporate finance and accounting have been underserved in that respect. Their days are still spent on routine, mundane tasks that keep them away from more stimulating work. Auditoria.AI aims to change that by automating those functions with AI and natural language technology.

We strive to improve the lives of finance and accounting professionals by automating the routine, repetitive, and laborious parts of the finance function, such as copy-and-pasting data, validating documents, and checking for errors on spreadsheets, freeing these teams to focus instead on providing valuable, strategic insights to the business. 

To that end, the problems we’re solving affect three major finance functions:

• Accounts Payable, responsible for sending money out of the company, such as bill payments. 
• Accounts Receivable, responsible for bringing money into the company, such as invoicing for services. 
• General accounting, a broader term consisting of functions of the general ledger team and the CFO, including closing books. 

Historically, making these processes more efficient entailed dedicating more personnel to them. But this didn’t necessarily mean more work was done faster and to the highest standards. Many finance professionals are often overworked, dedicating extra hours, weekends, and sometimes holidays to process invoices, collect payments, and close the books on time. 

We created solutions for these three finance functions, with our SmartBots taking care of the back-and-forth communications between finance teams, vendors, and customers. In large companies, these micro-transactions add up to thousands per day, resulting in finance professionals spending entire days reading inquiries, interpreting requests, looking for relevant information, and answering as many as possible. But with our AR helpdesk, for example, accounts receivable tasks, such as a request for a copy of an invoice, get automated. Our technology reads emails and attachments to understand what is being requested. Then it connects to the Enterprise Resource Planning (ERP) software to grab the relevant information and attach it to the email, so the recipient gets a response within 60 seconds. 

Building the smart assistant that finance teams need

In our automation flow, we constantly handle different types of documents, from invoices and tax forms to receipts and email messages. But processing the interaction between computers and human language is complex. You must detect intent and facts, and understand the context before finding the specific slots of information extraction that may be relevant to specific processes and requests. Our solution adds value by extracting the right information in the right context, from the right document, for the relevant finance function. Instead of building everything from scratch, we turned to Google Cloud’s Document AI to support extracting data from unstructured documents to understand and analyze them. 

Document AI comes with pre-built models that help analyze specific parts of our post-production lifecycle. For example, Invoice Parser extracts text and values from invoices, including invoice number, supplier name, invoice amount, tax amount, and invoice due date, all of which are necessary for our SmartBots to execute an extraction workflow. These out-of-the-box features significantly accelerate our own product development process and time-to-market, which are critical for the performance of a startup such as ours. 

To ensure a high quality of information extraction, we used to do document readings in-house. Having automated some of that with Document AI, we’re at 85% accuracy extracting files, and with some additional customization efforts, we will achieve 95%+ extraction accuracy.

Meanwhile, we have now streamlined internal processes, which ultimately translates into faster services for our customers. For example, assuming all the information has been provided, it generally took up to 15 minutes to process a tax form. We now do that in seconds. 

The value of automation doesn’t stop there. Using DocumentAI to automate structured data extraction from documents, we have managed to:

• Speed up the collection of general ledger entries by 90%+
• Reduce errors and omissions by 85%+
• Close books 20% faster
• Improved the productivity of full-time employees by 60%+
• Reduce process workload by 75%+
• Improve vendor serviceability by 75%+
• Reduce vendor risk and fraud by 50%+

Leveraging automation to focus on more innovation

Automating some of our processes with Document AI also means we have more time to focus on developing new features and further improving our solution. 80-90% of the time used for extracting custom fields from documents has now been automated with an OCR metadata library. 

With Document AI taking care of standard extraction, we focus on the intelligence we add to post-extraction. For example, when an invoice comes in from a vendor, our application needs to figure out which vendor it is to match it to the correct records in the ERP. But variations in the documents could interfere with that extraction process. The vendor’s trading name might be slightly different from the company’s name registered in our internal system, delaying the process. With more time on our hands, we’re now working on features enabling our models to leverage logos and other elements extracted from documents to swiftly match them to the correct company registered in our systems.  

With the benefits we’ve seen thus far, we look forward to accelerating our international growth. We’ll be relying on Google Cloud’s Document AI to automate operations, potentially in different languages, as we continually remove friction from the work lives of finance and accounting people worldwide.

You’re working on a new machine learning problem, and the first environment you use is a notebook. Your data is stored on your local machine, and you try out different model architectures and configurations, executing the cells of your notebook manually each time. This workflow is great for experimentation, but you quickly hit a wall when it comes time to elevate your experiments up to production scale. Suddenly, your concerns are more than just getting the highest accuracy score.

Sound familiar?

Developing production applications or training large models requires additional tooling to help you scale beyond just code in a notebook, and using a cloud service provider can help. But that process can feel a bit daunting.

To make things a little easier for you, we’ve created the Prototype to Production video series, which covers all the foundational concepts you’ll need in order to build, train, scale, and deploy machine learning models on Google Cloud using Vertex AI.

Let’s jump in and see what it takes to get from prototype to production!

Getting started with Notebooks for machine learning

Episode one of this series shows you how to create a managed notebook using Vertex AI Workbench. With your environment set up, you can explore data, test different hardware configurations, train models, and interact with other Google Cloud services.

Storing data for machine learning

When working on machine learning problems, it’s easy to be laser focused on model training. But the data is where it all really starts.

If you want to train models on Vertex AI, first you need to get your data into the cloud. In episode 2, you’ll learn the basics of storing unstructured data for model training and see how to access training data from Vertex AI Workbench.

Training custom models on Vertex AI

You might be wondering, why do I need a training service when I can just run model training directly in my notebook? Well, for models that take a long time to train, a notebook isn’t always the most convenient option. And if you’re building an application with ML, it’s unlikely that you’ll only need to train your model once. Over time, you’ll want to retrain your model to make sure it stays fresh and keeps producing valuable results.

Manually executing the cells of your notebook might be the right option when you’re getting started with a new ML problem. But when you want to automate experimentation at scale, or retrain models for a production application, a managed ML training option will make things much easier.

Episode 3 shows you how to package up your training code with Docker and run a custom container training job on Vertex AI. Don’t worry if you’re new to Docker! This video and the accompanying codelab will cover all the commands you’ll need.

CODELAB: Training custom models with Vertex AI

How to get predictions from an ML model

Machine learning is not just about training. What’s the point of all this work if we don’t actually use the model to do something?

Just like with training, you could execute predictions directly from a notebook by calling model.predict. But when you want to get predictions for lots of data, or get low latency predictions on the fly, you’re going to need something more than a notebook. When you’re ready to use your model to solve a real world problem with ML, you don’t want to be manually executing notebook cells to get a prediction.

In episode 4, you’ll learn how to use the Vertex AI prediction service for batch and online predictions.

CODELAB: Getting predictions from custom trained models

Tuning and scaling your ML models

By this point, you’ve seen how to go from notebook code, to a deployed model in the cloud. But in reality, an ML workflow is rarely that linear. A huge part of the machine learning process is experimentation and tuning. You’ll probably need to try out different hyperparameters, different architectures, or even different hardware configurations before you figure out what works best for your use case.

Episode 5, covers the Vertex AI features that can help you with tuning and scaling your ML models. Specifically, you’ll learn about hyperparameter tuning, distributed training, and experiment tracking.

CODELAB: Hyperparameter tuning on Vertex AI
CODELAB: Distributed Training on Vertex AI

We hope this series inspires you to create ML applications with Vertex AI! Be sure to leave a comment on the videos if you’d like to see any of the concepts in more detail, or learn how to use the Vertex AI MLOps tools.

If you’d like try all the code for yourself, check out the following codelabs:

• Training custom models with Vertex AI
• Getting predictions from custom trained models
• Hyperparameter tuning on Vertex AI
• Distributed training on Vertex AI

Editor’s note: Today’s post is by Mike Francomb, Senior Vice President of Technology, RecruitMilitary and a U.S. Army Veteran. RecruitMilitary is a wholly owned subsidiary of Bradley-Morris, Inc. (BMI), the largest military-focused recruiting company in the United States. RecruitMilitary uses Google Cloud Talent Solution to power its job search experience and connect more organizations with veteran talent.

For seven years, I served in the U.S. Army as a Field Artillery Officer, Military Occupation Code 13A. My time in service included a deployment to Operation Desert Shield / Desert Storm with the 24th Infantry Division out of Fort Stewart, GA, and a variety of front line artillery leadership roles, serving as a logistics officer for my unit and as an instructor teaching new officers how to be professional artillerymen. My day-to-day entailed leading teams of highly trained soldiers and managing logistics and materials to help those soldiers perform at a high level in stressful, fast-paced environments. It was my job to ensure we were ready to handle any circumstance. 

The hardest part about transitioning out of the Army in May 1996 as a highly trained artillery veteran was the fact that, though I felt prepared for any challenge ahead, I wasn’t sure I was making the right choice. I made a common mistake of transitioning veterans, I jumped right into an entrepreneurial venture. Looking back, I wish I’d had access to resources that displayed career options that would help translate my skills for the corporate world, it would have helped me be better prepared and know what my options were. I wasn’t ready to jump from the Army into running a business, and it was a long two years. 

Though my first job out of the Army was challenging, it taught me that I loved the start-up environment, and I joined RecruitMilitary in October 1998 when it was five months old. For the past 21 years, I have been fortunate enough to play an important role in helping RecruitMilitary grow to what it is today, the industry leader in connecting military veterans with organizations.  

RecruitMilitary connects organizations with veteran talent through over 30 products and services, all of which are fueled by our job board. Our job board, with over 1,400,000 members, is core to our business. In fact, if we don’t have an active and growing job board population, we don’t have the supply of veteran talent we need to deliver to our clients across our suite of services. 

With veteran unemployment at a 50-year low, it became increasingly challenging for RecruitMilitary to grow our veteran job seeker database and keep those veterans actively applying to client jobs. Being a data-driven company, we saw our existing search functionality was no longer producing the desired results for clients and began to receive client feedback about decreased candidate activity.

It was clear to us that we needed to begin adopting machine learning and more advanced search capabilities into our products and operations. The HR Tech space is shifting that way fast, and we want to be at the forefront. As we researched paths to take and learned of Google’s operating philosophy leading with AI, and that they were developing a tool for veteran job search, it made a lot of sense to go with a leader.

When Grow with Google announced its commitment to support veterans, we learned that we could add their military occupation code (MOS) translation feature to our job board through Cloud Talent Solution. This feature lets transitioning service members enter their military occupation codes (MOS, AFSC, NEC, or rating) directly into our search bar to see relevant civilian jobs available at client companies. We’re also using Cloud Talent Solution’s remote work functionality to provide an improved job search experience that allows our customers to make remote work opportunities in the U.S. more discoverable on their career sites. We’re excited about this feature, as it enhances our ability to deliver meaningful jobs to important members of our military community, military spouses, and veterans with limited mobility. 

The results of Cloud Talent Solution compared to our previous search are tremendous. Our job seekers are getting a much better experience, and to us that means more veterans are connected to jobs with our clients. We know this because our number of daily job applications has grown by 78 percent. And knowing that we now have a tool that’s going to learn and get better as more of our job seekers use it means that we will reap benefit for work done over time, and so will our clients and veterans who use our job board. That’s tremendous ROI to receive for a lean development staff. 

These are just a few of the types of tools I wish I’d had access to when I was considering my transition in 1996. With the help of technology and resources, like those from RecruitMilitary and Grow with Google, people in the military community, including veterans like myself, can prepare for and build meaningful careers.