Since the rapidly evolving power of generative AI to supercharge content creation raises concerns about what automation can mean for free access to quality material online, Berlin-based firm Spoke AI is preparing to use generative AI in a more constrained (but still noisy) context: Internally, within firms – offering technologies to assist information workers keep on top of inbound communications by automatically summarizing what’s coming at them across a variety of third-party applications.
Spoke AI is a platform that uses artificial intelligence and natural language processing to help organizations streamline their internal communication and collaboration. One of its key features is the use of generative AI to analyze workplace noise and identify signals that are important for decision-making.
Workplace noise refers to the large volume of information and communication that takes place within an organization, such as emails, chat messages, documents, and other data. This can make it difficult for employees to find the information they need, which can lead to inefficiencies and errors.
Spoke AI uses machine learning algorithms to analyze workplace noise and identify patterns and signals that are relevant to specific tasks or projects. For example, if a team is working on a product launch, the platform can identify all the relevant conversations, documents, and other data related to that project and present it in a structured and organized way.
By using generative AI to analyze workplace noise, Spoke AI can help organizations improve their productivity, reduce errors, and make better decisions. The platform can also provide insights into how teams are collaborating and communicating, which can help managers identify areas for improvement and optimize their workflows.
Spoke AI is using generative AI to pull signal from workplace noise
The startup’s long-term goal is to be able to provide AI productivity tools that can benefit employees across the board. But, it is starting with AI-powered aggregation and summary tools aimed exclusively at project managers.
This group of desk workers is thought to use a variety of third-party software tools, such as Slack, Jira, Github, Miro, Figma, and Notion, and may thus have a larger need for support in keeping up with so many decentralized, incoming pings. Later, after the business has worked on improving its technology and creating new training data sets, the objective is to develop products that can serve all types of information workers vertically.
The firm, which was founded in Q1 2021, is announcing a €2 million ($2.1 million) pre-seed round of funding led by the early stage Northern European oriented byFounders fund, with participation from Possibility Ventures. The funding comprises a grant from the European Regional Development Fund administered by the IBB, Berlin’s regional development bank. Previous to the pre-seed, the team had received angel money to help construct their MVP.
“The way that we apply [AI] is to basically reduce the noise that people face in their daily work across many different tools and platforms that they use,” explains co-founder Max Brenssell. “Initially, this is for product managers, who typically work across eight to 10 different tools. We help we help them stay on top of all of that work and communication across those different tools, by using AI to aggregate, prioritize and summarise this communication.”
The startup’s starter package — or “workplace operating system”, as its marketing bills it — consists of a search feature that can pull data across a range of third-party tools, such as conversations or tickets the user has been tagged in, aggregating this inbound into a “smart inbox” experience which layers on AI-generated “contextualized summaries” as well. First, it intends to test this with a few major corporations.
It is also offering AI-powered summarization “in the context of search”, per Brenssell — a feature it refers to as a “Generative Knowledge Base” (or “intelligent search”) — in the form of a browser plug-in. The search feature allows users to search across connected tools in their “smart inbox” to find answers “in summarised form, rather than finding a link to an outdated page”.
Spoke AI is also making its automated summarization available to early adopters as a Slack plug-in, with the goal of providing functionality where its target users already spend a lot of their time, while tapping into existing trusted environments rather than requiring users to upload potentially commercially sensitive content to an unknown platform.
There is a long history of productivity tools that offer integrations and aggregations that promise to pull pertinent but dispersed data into a single, easier-to-manage location. The added benefit here is the use of generative AI to generate contextual summaries on top of that in order to — in theory — restore context that would otherwise be lost as messages are extracted from their native apps and consolidated into a centralized repository.
One point of differentiation is a focus on security and privacy. Spoke AI claims versus older ways of increasing productivity through aggregation.
“The real secret sauce is really in the summarization that works for this specific use case — has to work in a very kind of very concise, reliable way. And also in a data privacy and data security [safe] way. So this is how we are positioning it and how we’re building it as well,” says Brenssell. “We do work with pre-trained language models, like the ones that are at the core of [OpenAI’s] GPT. But we do a lot of pre-and post-processing in terms of, for example, anonymizing data — cleaning the data so that it’s improved from the privacy and safety perspective.”
In addition to selling AI summarization, Spoke AI envisions this component potentially fueling an additional revenue stream — that is, if it can sell data anonymization as a service (through an API) to other organizations that wish to apply AI models like GPT to their own custom data-sets.
Brenssell also suggests that the primary summarizing capabilities be turned into an API as another method of monetizing the technology.
For the time being, it is providing a free version of its summarizing technology in the form of a Slack plug-in. He believes that, at least initially, the smart inbox function will be sold as a SaaS, with tiered pricing based on the number of connectors, security features, and so on.
Accuracy is undoubtedly a key component of the startup’s offering. If the summaries it generates fail to accurately capture the context of the notifications, Spoke AI may end up doing more work for users rather than saving them time.
Brenssell mentions that feedback loops have been implemented into the beta version so that customers can rate the automatic summaries and help it improve over time. It is also emphasizing showing how the automation works — so consumers can go back and figure out which inputs the AI used to create a summary. Establishing transparency is an early priority, he argues.
“This is something that users ask a lot, obviously, how can I trust this?” he notes. “What we tried to do always is to create transparency around where does the data come from that flowed into the summary, and then giving the user kind of a trail, where they can then go deeper if they want to, and really understand where we’re pulling the data from that goes into summary.”