Deepgram Acquires Pipecat Maker Daily, Pledges to Keep Framework Open Source
Deepgram announced Thursday that it is acquiring Daily, the company behind the popular open source voice AI framework Pipecat, as voice AI players move to strengthen their positions through...
Deepgram announced Thursday that it is acquiring Daily, the company behind the popular open source voice AI framework Pipecat, as voice AI players move to strengthen their positions through partnerships and acquisitions. The deal amount was not disclosed, but both companies said Pipecat will remain open source.
Pipecat manages voice and video streams alongside pipeline components such as speech-to-text, large language models and text-to-speech models while reducing latency. Daily, which provides hosted infrastructure for voice and video calls, has raised more than $62 million to date from investors including Tiger Global, Highfield Capital, Nat Friedman, Lachy Groom and Kevin Weil, according to PitchBook data. Deepgram reached unicorn status earlier this year with a $130 million Series C round; both companies were in the same Y Combinator batch in 2016.
Daily co-founder Kwindla Kramer said the company began experimenting with AI-to-human communication over voice and text around the time GPT-4 launched, which led to Pipecat. He said that while there had been considerable acquisition interest, he favored Deepgram CEO Scott Stephenson's commitment to keeping Pipecat open source forever, and that the engineering cultures of both organizations matched.
Stephenson said Deepgram had long wanted to work in open source and saw an orchestration framework for agents as a promising area. "We want to open up the ecosystem so everybody can build voice agents," he said, adding that developers should be able to use the best models for their use case, including from partners.
Deepgram is also launching the Real-Time Context Standard, an open protocol for voice agents co-developed with the Pipecat team. It defines metadata such as audio context like a whisper, instructions for how a language model can use those signals, and guidance on how a response should sound.
Source: TechCrunch
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