Tackling the Turing Test: Automation Quickly Creates Video Content for Customer Success

Customers of B2B SaaS products depend on videos as their preferred vehicle for customer success information. Many companies maintain vast video libraries that offer a wealth of narrated information in easy-to-follow screen recordings, and customers often prefer this more dynamic format to written instructions. Unfortunately for the enterprises, keeping this library up-to-date is a highly manual process: video recordings take time to produce, are costly to translate into other languages, and – most plaguing of all – can become quickly outdated with subsequent product release cycles. It is not unusual for companies to accumulate ‘video debt’, or a glut of outdated videos that do not accurately represent the latest product version. 

We at Tensility understand the importance of Customer Success for B2B SaaS companies, and are excited to announce our investment in Videate’s seed round. Based in Austin, Texas, Videate’s technology uses AI to quickly transform written documentation into finished videos related to support, on-boarding, or training. The manual tasks of recording, adding voiceover, and editing video content are completely automated with an easy-to-use platform that leverages browser automation, NLP, and text-to-speech technology. Videate’s AI eliminates the hassle of software video creation, offering special effect solutions and pronunciation tools to ensure the videos look, feel, and sound like they are human-made. Videate's automated language translations can transform a single piece of content into a globally-relevant resource. With an initial focus on B2B SaaS companies, we are very impressed with the team’s early traction and customer feedback that likens the previously burdensome process to magic. 

The management team is made up of a seasoned cohort of leaders and innovators. Co-founders Dave Gullo and Mark Hellinger are former C-suite executives with decades of experience in online video and enterprise application construction. They are highly motivated to tackle challenging problems, and their early traction speaks to their success at addressing a common pain point for SaaS companies, product managers, and B2B customers. We are thrilled by their product roadmap that demonstrates a deep understanding of how to apply AI to delight customers. We look forward to partnering with Videate to transform Customer Success and help B2B companies meet their customers’ needs with ease!

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We are excited to announce our investment in Agnostiq’s seed round. Based in Toronto, ON, Agnostiq Labs (“Agnostiq”) is developing a suite of applications that seamlessly enable financial institutions (FIs) to leverage QC to power their trading algorithms, all without the need of expensive quantum programming resources. Agnostiq’s applications will allow banks to securely transpile, optimize, and deploy quantum machine learning (QML), quantum Monte Carlo and quantum neural networks in portfolio optimization models. This breakthrough will lead to development of new algorithms at a faster rate and better market predictions.

Financial institutions (FIs) have realized that classical computing is approaching the limit of Moore’s law and there is a gap between the amount of new unstructured data coming in and their ability to process that data to make better decisions. As such, FIs and other organizations are looking to quantum computing (QC) to resolve these issues and deliver the high performance computing they need to maintain their edge. Whether developing the better algorithm to price options or conducting trades in one 64 millionth of a second to capitalize on the arbitrage opportunity, FIs need the compute power to analyze hundreds of millions of data points in real-time to execute their strategies.

While initially focused on banks, we see tremendous opportunity to apply their unique quantum AI workflows and best-in-class security applications across several industries, such as pharmaceuticals, healthcare, and energy. Additionally, we believe Agnostiq can become the platform-of-choice that accelerates enterprise adoption of QC and transforms data scientists into quantum scientists.

We are incredibly impressed with the management team and talent. Collectively, the founders Oktay Goktas, PhD,  and Elliott MacGowan possess the unique combination of deep technical knowledge in QC, sales acumen and operational expertise needed to succeed in this nascent industry. Agnostiq has also proven its ability to attract top tier QC talent with backgrounds in quantum physics, cryptology, mathematics, and computational physics. Given the dearth of available and qualified talent, Agnostiq’s ability to attract, hire, and retain top scientists will solidify its market position and competitive moat.

We believe in Oktay, Elliott, and Edwin’s collective vision on quantum computing and are excited to partner with them in their quest to help enterprises harness the power of AI in quantum computing!

Today companies are driving workforce productivity using 3rd party SaaS applications like Salesforce, Jira, Dropbox which form the backbone of critical functions across the organization. According to a 2020 Devsquad study, companies deploy, on average, 34 different SaaS tools, and that number increases dramatically with company size (reference 1 below).

Company tech stacks are also becoming increasingly decentralized and thus harder to manage. Communication tools such as Slack and Microsoft Teams have made it easier than ever to collaborate across teams, but have also led to sensitive information moving unchecked across the enterprise, increasing the risk of data loss and leakage. Last year, over 165 million records were either lost or exposed within the US alone (reference 2 below), all the while increased regulation (e.g., GDPR and the California Consumer Privacy Act) has levied significant fines and put additional pressure on businesses to revamp their information security and data loss prevention (DLP) strategies.

As organizations continue to grapple with this changing landscape, we are thrilled to announce our investment in Polymer Solutions’ (“Polymer”) seed round. Cyber security is one of our key investment areas, and continues to be important as more work moves to the cloud. We are proud to support Polymer’s vision to become the preeminent platform that manages DLP and redaction across the enterprise technology stack. While most of the DLP market has focused on encryption-based solutions for at-rest data, Polymer is unique in its approach by applying Natural Language Processing (NLP) on unstructured, in-motion data to redact Personal Identifiable Information (PII) and sensitive corporate information. This solution will be critical to companies that collect sensitive customer information, such as financial or health data.

Polymer provides an easy-to-use solution that allows enterprise Information Technology departments to create access controls as well as monitor, secure and redact sensitive data across dozens of collaboration apps, including Slack, Github, Dropbox and Zapier. Polymer’s product has already gained significant traction with several large enterprise clients.

Polymer was founded by Yasir Ali and Usman Malik, who are supported by a strong technical team of cyber security, big data and machine learning experts who are excited and well-positioned to tackle this complex data redaction problem. We are thrilled to partner and support Yasir, Usman, and the Polymer team as they continue to expand their data governance platform and grow into a leading security company!

1 ”60 SaaS Statistics and Trends for 2020”, Industry study via Devsquad, 2020
2 “Annual number of data breaches and exposed records in the United States from 2005 to 2019,” Identity Theft Resource Center, 2019

Today, businesses are generating a massive amount of data every second across hundreds of disparate data sources.  As organizations attempt to capitalize on new ML/AI capabilities, each new system or tool disaggregates the journey from data to business value even further, creating costly, slow, and labor-intensive data preparation. The situation creates challenges for the data engineering teams to keep up with business and technical demands.

In machine learning, a "feature" is an input variable, similar to the explanatory ("x") variable in simple linear regression. A machine learning project might use hundreds or even millions of features, and each feature must be paired with labels (similar to the dependent "y" variable) to train a model. A robust and scalable ML/AI development program requires improving the feature extraction process, an early step in ML that prepares data for analysis by abstracting complex schemas and their data into basic objects and attributes.

Relying on reference architectures for feature re-use can help, but this introduces issues of latency, complexity, and another data silo to be managed. To simplify and speed up feature extraction and re-use, a new technology has arisen, called feature stores, which assists with the demanding data preparation required for effective ML. No longer unique to the capabilities of large firms like Uber and Airbnb, feature stores can transform raw data into feature values, store those features, and serve those features for training and analysis in the future.

Importantly, feature stores do not remove the need for Snowflake or similar cloud solutions. Feature stores easily overlay with existing data infrastructures, enabling almost instantaneous updates, reducing risk, and scaling quickly with an enterprise. The graphic below compares feature stores to current approaches.

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In short, all of an organization’s data can be converted to reusable features and analyzed with full fidelity, regardless of format or source location, for immediate analytics. Not only does this speed up a data scientist's development timeline, but it brings economies of scale to ML organizations by enabling collaboration. When a feature is registered in a feature store, it becomes available for immediate reuse by other models across the organization.

One example of a leading provider of feature store capabilities is Molecula, who recently completed a $17.6 million Series A round with Tensility as a participant. Molecula leaves data at its source and continuously extracts and updates only features into a centralized feature store. This process eliminates the need to copy, move, or pre-aggregate data, reduces the data footprint by 60-90 percent, and provides a secure data format for sharing.

Whether through Molecula or another organization, feature stores pair with current systems to enable prescriptive analytics while reducing complexity, costs, and risk. We are excited at the capability they provide to data engineers and data scientists to improve the data pipeline and leverage all of a company's data for better business outcomes.