How Smaller Digital Workplace Vendors Are Integrating Generative AI
The rapid push into generative AI in the few short months since San Francisco-based OpenAI launched ChatGPT has rewritten the AI agenda. Organizations large and small have scrambled to launch their own solutions, rolling out proprietary large language models or building on existing ones.
What follows is a look at some of the smaller players in the space trying to solve workplace problems — and the latest entry from one of the giants. First, the giant.
Amazon Enters Generative AI Ring with Amazon Bedrock
Last week with little fuss, Amazon joined the ranks of the other major tech companies charging into the generative AI space, albeit in a far different way than the other big players in the space.
In his annual letter to shareholders published April 14, Amazon president and CEO Andy Jassy shared the company's vision and investments in large language models (LLM) and generative AI. "We have been working on our own LLMs for a while now, believe it will transform and improve virtually every customer experience, and will continue to invest substantially in these models across all our consumer, seller, brand, and creator experiences,” he wrote.
The company unveiled Amazon Bedrock on the same day. Bedrock is an AI platform which provides a way to build generative AI-powered apps via pre-trained models from startups. Bedrock is currently in preview and available to a very select number of Amazon customers. With it, customers can build chatbots, generate and summarize text and classify images based on a prompt.
In sum, it provides AWS cloud customers a range of AI models, including what the company terms “foundation models,” ranging from AI21 Labs’ translation LLM, Stability AI’s image LLM and Titian, a group of AI models trained by AWS itself.
This contrasts with Microsoft, which has depended on its partnership with OpenAI to meet generative AI demands, or Google, which offers a home-grown generative AI. Meta has also been diving in to the generative AI space, in what appears to be another attempt to realize its metaverse ambitions.
Why Should the Tech Giants Have All the Fun?
Smaller vendors feeding into the digital workplace have been quick to recognize and jump on the opportunity here too. Dozens of companies have already developed products aimed at tapping generative AI to increase productivity. Here's a quick look (in alphabetical order) of some of the players making regular appearances in discussions about work, productivity and generative AI:
1. Erudit AI: Gauging Employee Sentiment With LLMs
Miami-based Erudit AI promises anonymity and protection while giving companies easy access to daily workforce insights. The AI relies on real-time anonymized business communications data from platforms like Slack, G Suite and Teams to analyze sentiment and provide insights on employee well-being.
Using deep learning models trained on psychologists’ insights, the AI detects burnout risk, engagement and turnover risk from key phrases in employee communications. The software then reports the levels of each metric by team or department. HR teams can use this information to determine what kind of initiatives to pursue, and for which teams, while protecting individual anonymity.
2. Findem: ChatGPT for Recruitment
Using AI in hiring is nothing new, but Redwood City, Calif.-based Findem is applying AI to the candidate search process in a unique way by enabling attribute-based people search, solving the hiring process as a data problem.
Recruiting for niche and hard-to-fill roles is an extraordinarily time-consuming and difficult task. CEO Hariharan Kolam told Reworked: “We’ve implemented ChatGPT into our platform and coupled it with our attribute data — this is key and unique to Findem — to make the automated candidate nurturing process as personalized as you can get, which generates more interest from candidates and leads to higher conversions.”
The ChatGPT feature pulls candidate attribute data from each enriched profile and incorporates it into a highly curated, personalized email. Instead of telling a candidate that they’d simply be a good fit for a software engineer role, Findem’s platform provides added context to the statement, so managers can cite specific experience or skills that make them the right candidate for the job.
“Without the context provided by a rich data pool, using ChatGPT for candidate outreach backfires because it’s only generating generic emails that add noise and drop response rates," said Kolam.
3. Iterate.ai: Low-Code App Development
San Jose, Calif.-based Iterate.ai's Interplay platform now includes integrations with Stable Diffusion, ChatGPT and other OpenAI tools across the generative AI landscape. Paired together, Interplay’s low-code environment and generative AI give customers the ability to rapidly implement AI capabilities, a task that would otherwise require significant resource investments and complex coding.
“Like low-code itself, generative AI promises to multiply organizations’ productivity and eliminate even more of the traditional roadblocks to harnessing powerful technological capabilities,” said CTO Brian Sathianathan. "The combination of the Interplay low-code platform and generative AI now allows developers (including those without any AI expertise) to rapidly build and iteratively perfect applications that deliver the features and experiences that set their companies apart.”
4. Hugging Face: NLP for Creating Conversational AI
New York City-based Hugging Face is a French startup that's been making a big splash in the AI community. Founded by Clément Delangue and Julien Chaumond, Hugging Face specializes in natural language processing (NLP) and provides a powerful open-source platform for creating cutting-edge conversational AI.
Its library, called Transformers, has been widely adopted by researchers and developers across the globe. According to Delangue, their goal is to democratize NLP and make it more accessible to a broader audience.
The Hugging Face Hub itself is a platform where users can share pre-trained models, datasets and demos of machine learning projects.
The Hub contains GitHub-inspired features for code-sharing and collaboration, including discussions and pull requests for projects. It also hosts Hugging Face Spaces, a hosted service that allows users to build web-based demos of machine learning apps using Gradio or Streamlit.
Learning Opportunities
5. Perceptyx: Workplace Insights and Coaching for Managers
Temecula, Calif.-based Perceptyx’s Cultivate Intelligent Coaching platform is an AI-powered leadership development product within its People Insights Platform. The offering leverages behavioral data to deliver much-needed guidance to managers navigating today’s challenging work environment.
Its new AI model is built on its current ability to determine theme and sentiment. The intent model helps leaders accurately analyze employees’ intents from hundreds of thousands of comments. With it, leaders can quickly surface and act on employee suggestions, which often go ignored or are difficult to detect.
The system combines feedback from Perceptyx employee surveys with AI recently acquired from Cultivate, which detects managers’ blind spots and identifies areas for coaching — for instance, if managers are stressing out employees by sending late night emails or scheduling multiple meetings. A hyper-personalized report with action recommendations is shared with managers via nudges, as is a metrics-rich personal dashboard.
Receiving relevant coaching within the daily flow of work allows managers to put their newfound skills into practice immediately, track their progress, remain engaged in the coaching process, and deliver faster impact to their team.
6. Seyhan Lee: Build Immersive Experiences in 'Minutes'
Boston-based Seyhan Lee is a creative-led AI production studio founded in 2020 by Pinar Seyhan Demirdag and Gary Lee Koepke. It develops AI solutions and tools for the film, broadcast and entertainment industries, supporting the creation of immersive experiences and brand activations.
The company claims that the newly launched Cuebric is the first comprehensive generative AI tool for filmmaking. Where it once took months for filmmakers to go from concept to set, Cuebric creates camera-ready environments in minutes.
Cuebric’s virtual environments are automatically segmented in 2.5D, allowing filmmakers the freedom to make changes on set with each new iteration outputting in camera-ready resolution, fully rotoscoped at the push of a button, and ready to be used in storyboarding, pre-visualization or filming.
It was launched in collaboration with XR Studios, a technology-focused production company which is known for its immersive tech workflow solutions and Extended Reality (XR) which pulls together live and virtual content.
7. Vianai Systems: AI Research Assistant for Investors
Palo Alto, Calif.-based Vianai Systems recently unveiled a beta of hila, its AI-powered chat assistants created specifically for finance professionals to answer questions about publicly traded companies.
Using verified, reliable datasets for S&P 500 companies and the NIFTY 50, hila ingests lengthy documents such as earnings transcripts to surface actionable insights. Hila's results are governed by a patent-pending technology based on Vianai’s zero-tolerance approach to hallucinations — a common byproduct of many of the generative AI tools — driven by confidence scores and transparency.
Hila is currently free to users as part of the research phase. The company claims thousands of users, including many from well-known financial institutions, are already making extensive use of the system.
Vianai will be rolling out more advanced, premium capabilities for hila, including the ability to expand its dataset with user-uploaded documents and data.
Are We Already Approaching the Generative AI 'Trough of Disillusionment'?
As a final note, Bern Elliot, vice president and distinguished analyst at Gartner noted in a company blog post that ChatGPT will soon emerge from its beta phase into an early trial and pilot phase, at which time adoption is likely to increase. However, he cautioned that once the initial shine has worn off, we can expect there will be a negative response to a range of issues, including privacy concerns, misuse of information and bias in much the same way that many new technologies go through the "trough of disillusionment." But as we've seen in the past, that is unlikely to dampen the enthusiasm amongst the vendors in the space.
About the Author
David is a European-based journalist of 35 years who has spent the last 15 following the development of workplace technologies, from the early days of document management, enterprise content management and content services. Now, with the development of new remote and hybrid work models, he covers the evolution of technologies that enable collaboration, communications and work and has recently spent a great deal of time exploring the far reaches of AI, generative AI and General AI.
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