Deep Analysis | Information Management | Vendor Vignettes

Rule14


Founded 2011 | HQ Santa Monica, CA | 40 employees | >$10 million annual revenue

Rule14 is among the new breed of disruptive AI software providers that have figured out how to make deep learning work to solve practical business problems. Any company or government agency that wants to build and maintain an internal AI practice should put it on the shortlist.


The Company

Rule14 is an AI-enabled process automation and data mining company that offers a suite of solutions and services powered by predictive analytics. The company launched its AI platform in 2011 and focused on developing actionable intelligence for the government sector using NLP-based sentiment analysis and decisioning across the published and dark web.

In 2013, Rule14 launched machine learning (ML) models to forecast energy demand by monitoring weather stations, historical data, and smart meter data every 15 minutes, processing several trillion transactions a year. It now has more than 50 direct and indirect customers in business and government.

Until recently, a typical project for Rule14 was an enterprise with a critical process that needed AI but didn’t have the internal resources to handle it. Rule14 provided the technology and the services. Now, the company plans to launch its R14.dev platform this summer to expand its AI offerings into broader markets including intelligent document processing.

The company is fairly new to the content management and cognitive capture markets. We learned of it during a briefing from Exela Technologies, a business process automation (BPA) company that uses Rule14 to deliver intelligent document processing solutions. The HandsOn Global Management Fund portfolio includes controlling interest in both Exela and Rule14, and their partnership is a good example of cross-portfolio synergy. For example, Rule14’s relationship with Exela gives it a clear market advantage in gathering sample sets to train deep learning models. Rule14 told us that for one healthcare claims project, Exela provided 50,000 sample claims to train on.

The Technology

The R14.dev platform is designed for customers to develop and deploy their own AI applications (see Figure 1). The platform consists of three core modules:

nQUBE AI Workflow Builder is used to build, train, test, and deploy ML models including neural nets. AI support is available for all user types, from business analysts and other non-programmers to expert data scientists. Workflow Builder can quickly connect reusable modules to assemble complex AI-powered workflows, and it comes with pre-built connectors to standard data stores such as AWS, Azure, and Google Cloud. The pre-trained library of AI models and ML services includes computer vision, NLP, OCR, and deep learning-based classification models for object detection, form recognition, and forecasting.

R14 Development Studio is an integrated development environment (IDE) for developers and data scientists to create and modify custom ML models. It supports over 60 computing languages such as Javascript, Java, C++, and Python.

DevOps Manager is the hub for managing all cloud and on-premises computing resources. It has simplified controls for business analysts or skilled developers to manage utilization across service providers (AWS, Azure, GCP, etc.). The health monitoring dashboard is a real-time summary of the application status down to the feature level and has user-configurable dashboards, event discovery, script automation, and alerts.

Exela’s EON RPA Platform augments the R14.dev platform by recording workflows into packaged bots and integrating them with R14’s nQUBE AI workflow engine. The combination of EON, nQUBE, and the R14 DevOps platform can automate complex workflows. EON provides automation for both attended and unattended deployments.

The R14.dev platform pricing should be competitive with other offerings in the industry. Individual component single-user deployments start at $9 per month. Enterprise-level deployment pricing will vary based upon a number of factors such as the components selected, number of users, deployment scope, transactions, and term length. Rule14 is also offering limited, try before you buy, free versions of the platform.

Of particular interest to us is Rule14’s Intelligent Document Processing (IDP) on Demand, a new cognitive capture cloud service to classify, extract, summarize, and route documents. The company claims to already have 40 direct and indirect customers using its healthcare claims processing portal (launched November 2020) to process over 20 million transactions per month.

The IDP on Demand service includes neural network classifiers pre-trained on an average of 60,000 images. The ever-growing model library supports recognition and extraction across thousands of healthcare, legal, and financial document types. ML-based parsers identify and extract key information of interest, which is sent to validation modules to ensure field extractions meet expectations.

Figure 1
Modifying a Neural Net Document Classifier from an nQUBE-hosted Workflow

Our Opinion

We were impressed by Rule14’s deep experience with neural networks and diverse, patented AI technologies. Rule14 is among the new breed of disruptive AI software providers that have figured out how to make deep learning work to solve practical business problems. We think any company or government agency that wants to build and maintain an internal AI practice should consider Rule 14 on their shortlist of potential vendors.

Rule14 is about to compete against deep learning giants such as Amazon, Google, Microsoft, and IBM. While it cannot match their sheer scope and reach, the company can move quickly to specialize in AI models tuned for vertical applications such as the healthcare claims example.

Advice to Buyers

Rule14 and Exela have an exclusive partnership for the BPO/BPA services market. Buyers from other industries interested in the R14.dev platform may choose to work directly with Rule14 or buy through Exela. Rule14 is financially sound under the HGM Funds umbrella and should have the necessary funds to support a rapidly growing customer base. It can also provide blue chip customer references.


SOAR Analysis

Strengths

  • Expertise in applying neural networks to business problems
  • Access to large unstructured data sets for pretraining models

Aspirations

  • Become a leading specialist AI platform provider
  • Build an unmatched neural network library

Opportunities

  • Could “own” two or three vertical applications
  • Create partnerships with managed service providers, other RPA vendors, and RPA customers

Results

  • More than 50 customers already using its core AI technology
  • Fast-growing healthcare claims portal