Nearly all of corporations struggle to extract value from their data. Several years ago, Forrester reported that between 60% and 73% of information belonging to the common business goes unused for analytics. That’s because the information’s siloed or otherwise pigeonholed by technical and security considerations, making it difficult — if not inconceivable — to use analytical tools.
Anna Pojawis and Tyler Maran, engineers who previously did stints at Y Combinator-backed startups Hightouch (a data-syncing platform) and Fair Square (a medical insurance tool), were inspired to try their hands at solving the information value problem after discovering that many corporations had been “locked out” of analytics strategies as a result of the engineering roadblocks.
“We’ve found that a big a part of the market, especially those in regulated industries like healthcare and finance,” have struggled with data analytics, Maran told TechCrunch. “Nearly all of corporate data doesn’t fit right into a database today; it’s sales calls, documents, Slack messages and so forth. And, given the size of those corporations, off-the-shelf data models are typically not sufficient.”
So Pojawis and Maran founded OmniAI, a set of tools that transform unstructured enterprise data into something that data analytics apps and AI can understand.
OmniAI syncs with an organization’s data storage services and databases (e.g., Snowflake, MongoDB, etc.), preps the information inside and allows corporations to run the model of their alternative — for instance, a big language model — on the information. OmniAI performs all of its work in the corporate’s cloud, OmniAI’s private cloud or on-premises environments, delivering ostensibly improved security, in response to Maran.
“We consider that enormous language models will grow to be essential to an organization’s infrastructure in the subsequent decade, and having all the pieces hosted in a single place just is sensible,” Maran said.
Out of the box, OmniAI offers integrations with models, including Meta’s Llama 3, Anthropic’s Claude, Mistral’s Mistral Large and Amazon’s AWS Titan to be used cases like mechanically redacting sensitive information from data and customarily constructing AI-powered applications. Customers sign a software-as-a-service contract with OmniAI to enable management of models on their infrastructure.
It’s early days. But Omni, which recently closed a $3.2 million seed round led by FundersClub at a $30 million valuation, claims to have 10 customers already, including Klaviyo and Carrefour. Annual recurring revenue is on target to succeed in $1 million by 2025, Maran said.
“We’re an incredibly lean team in a fast-growing industry,” Maran said. “Our bet is that, over time, corporations will go for running models alongside their existing infrastructure, and model providers will focus more on licensing model weights to existing cloud providers.”
