AI THAT LEARNS AI THAT LEARNS
YOUR BUSINESS YOUR BUSINESS

We're delivering data-driven digital transformation, We're delivering data-driven digital transformation, helping to build the next-generation quantified workforce helping to build the next-generation quantified workforce

We're delivering data-driven digital transformation, helping to build the next-generation quantified workforce.

The old way

HIRE A FLEET OF CONSULTANTS AND WAIT MONTHS FOR HIGH-LEVEL PROCESS ASSESSMENTS

  • Expensive resources tied up in countless user interviews
  • Results with incomplete process coverage and questionable accuracy
  • Even more time required for detailed documentation once assessments are complete

Way of the future

OUR VIRTUAL PROCESS ANALYST DISCOVERS AND DOCUMENTS PROCESSES IN WEEKS

  • No log, database, or API access necessary, delivering a zero-integration experience
  • Universal compatibility, whether thick-client, thin-client, web-based, green screen or proprietary
  • Reduce the effort to produce level 5 process documentation by up to 90%

TEACHING MACHINES TO LEARN THROUGH IMITATION

One of the most common ways we learn something new is by watching an expert and imitating their actions. This basic concept is at the heart of imitation learning, a form of meta-learning in which a neural network is trained to learn new activities. The behavioral policies thus learned are much more flexible than traditional rules-based systems, and allow us to adapt to and filter out the noise that is inherent in human-based activity.

TEACHING MACHINES TO LEARN THROUGH FEW-SHOT DETECTION

Another form of meta-learning, few-shot detection allows us to recognize the content of an image without seeing the multitude of training images generally associated with standard convolutional neural networks. This allows us to rapidly adapt to new, custom, or updated software that we see, a common occurrence when working with a global enterprise.

TEACHING MACHINES TO LEARN THROUGH COMPUTER VISION

We've built our computer vision infrastructure to closely resemble how humans understand software screens, with convolutional neural networks that recognize each individual control, along with its corresponding label. This intelligence allows us to define a unique interaction model per control type, and understand the difference between clicking on a button or clicking in a checkbox. By truly interpreting the screen at the field level and not just as a screen of X/Y coordinates, our AI can track noisy human interactions and transcribe them cleanly.

TEACHING MACHINES TO LEARN THROUGH NATURAL LANGUAGE UNDERSTANDING

Natural language models provide a deeper understanding of a process that transcends traditional sequence matching. Many processes involve free-form text, which often shows up as random noise inside of a larger sequence. By leveraging word-embeddings, parts-of-speech tagging, and named-entity recognition, we add structure to what looks random, filling in the blanks that often come from working with unstructured text.

TEACHING MACHINES TO LEARN THROUGH REPLICATION

Our Virtual Process Analyst combines visual data acquisition, imitation learning, natural-languge understanding, few-shot learning and data mining into a new discipline we call replication learning. Humans are multi-sensory, which allows for the rapid learning and adaptation we see in everyday life. By leveraging joint models containing visual, time-series and language information, our replication learning models can quickly learn and adapt, uncovering patterns that are hidden under the surface.

Virtual Process Analyst

Time-to-value

Integrations with legacy systems are one of the largest areas of friction in enterprise projects. Our technology uses only computer vision for data acquisition, negating the need for costly, complex, and expensive data integration and mapping. In addition, we've automated the data science—so after your IT group deploys our agents you can start receiving insights in weeks.

Ubiquity

Most of today's processes don't exist within a single monolithic application, but span across multiple systems: off the shelf, proprietary, desktop, and cloud. Any discovery solution dependent on transaction logs or database access will have to be specifically mapped for each one of these applications. Cloud-based systems and desktop applications present unique challenges to log-based solutions, which may not even produce the types of logs necessary for transaction mining. Our computer-vision-based approach offers 100% compatibility across legacy systems, custom applications, thin or thick clients, and green screens. If a human can see the software screen, our software can interpret it.

Accuracy

Process discovery via interview-based methodologies or recording software that produces screen-level granularity suffers from an inability to ensure complete process coverage. Process permutations can be missed and nuance can be lost, leading to poor quality documentation and inadequate planning. Our computer vision-based solution perfectly captures every activity at the field level, saving hours of transcription, tracing, and validation.

Transforming the digital workforce through security, insight and governance

We believe algorithms and bots work best when they’re managed like employees - given the tools they need to succeed and held to a common set of standards that help set expectations for success while managing the risks inherent in a complex and rapidly changing environment. Our passion is helping this new digital workforce thrive within the enterprise, addressing the scale problems endemic to transformation programs today, and the security, compliance and data problems that stifle their adoption.

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