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Decagon taps generative AI tech to respond to customer questions — and more.Image Credits:Decagon
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One red - hot family in the procreative AI space is customer financial backing , which is n’t surprising , really , when you consider the technical school ’s potential to cut liaison center cost while increasing exfoliation . Critics argue that procreative AI - powered customer backing tech could depress wage , conduce to layoffs and ultimately deliver a more computer error - prone end - user experience . Proponents , on the other hand , say that generative AI will augment — not replace — worker , while enabling them to focus on more meaningful tasks .
Jesse Zhang is in the proponents summer camp . Of course , he ’s a petty biased . Along with Ashwin Sreenivas , Zhang atomic number 27 - foundedDecagon , a generative AI political platform to automate various aspect of client support channels .
Zhang is well cognisant of how stiff the competition is in the mart for AI - power customer support , which spans not only tech giants like Google and Amazon but startups such asParloa , Retell AIandCognigy(which recently raised $ 100 million ) . By one estimate , the sector could be worth $ 2.89 billion by 2032 , up from $ 308.4 million in 2022 .
But Zhang thinks that both Decagon ’s engineering expertness and go - to - market place approach give it an reward . “ When we first begin , the persist advice we received was to not pursue the client support blank , because it was too crowded , ” Zhang told TechCrunch . “ Ultimately , the thing that act upon for us was to sharply prioritise what customers wanted and keep laser focus on what client would get time value from . That ’s the difference of opinion between a real business and a flashy AI demo . ”
Both Zhang and Sreenivas have technical background , having work at both startups and larger tech orgs . Zhang was a software package engineer at Google before becoming an interne trader at Citadel Securities , the hedgerow fund , and foundingLowkey , a social gambling platformthat was acquire by Pokémon GO maker Niantic in 2021 . Sreenivas was a deployment strategist at Palantir before co - foundingcomputer visual sense startup Helia , which he sell to unicorn Scale AI in 2020 .
Decagon , which sell primarily to enterprises and “ high - ontogenesis ” startups , develops what amount to customer support chatbots . The bot , drive by first- and third - party AI models , are fine - tunable , capable of ingesting a businesses ’ knowledge foundation and diachronic customer conversations to make greater contextual understanding of issues .
“ As we started work up , we realized that ‘ human - similar bots ’ fee-tail a lot , since human agents are capable of complex reasoning , take actions and dissect conversations after the fact , ” Zhang say . “ From talking to client , it ’s percipient that while everyone wants greater functional efficiency , it can not get along at the expense of client experience — no one likes chatbots . ”
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So howaren’tDecagon ’s bot like traditional chatbots ? Well , Zhang enjoin they learn from past conversation and feedback . Perhaps more significantly , they can mix with other apps to take action on behalf of the client or agent , like process a repayment , categorize an incoming message or helping write a support clause .
On the back end , companies get analytics and controller over Decagon ’s bots and their conversation .
“ Human agents are able to dissect conversation to acknowledge trends and find improvement , ” Zhang say . “ Our AI - power analytics dashboard automatically review and tags customer conversations to name themes , flag anomalies and suggest additions to their knowledge base to better address customer inquiries . ”
Now , generative AI has a reputation for being , well , less than double-dyed — and , in some cases , ethically compromised . What would Zhang say to companies wary that Decagon ’s bots will tell someone toeat glueor write anarticle full of plagiarise content , or that Decagon will train its in - house models on their information ?
Basically , he says , do n’t worry . “ allow for customers with the necessary guardrails and monitoring for their AI agents has been important , ” he said . “ We optimize our modeling for our customer , but we do this in a way which ensures that it is unacceptable for any datum to be inadvertently exposed to another customer . For illustration , a simulation that generates an answer for customer A would never have any vulnerability to data from customer B. ”
Decagon ’s technical school — while subject to the same restriction as every other generative artificial intelligence - powered app out there — has been attracting name - brand clients as of late , like Eventbrite , Bilt and Substack , helping Decagon to reach intermission - even . famed investor have mount aboard the speculation , too , admit Box CEO Aaron Levie , Airtable CEO Howie Liu and Lattice CEO Jack Altman .
To date , Decagon has raised $ 35 million across seed and Series A rounds that had participation from Andreessen Horowitz , Accel ( which led the Series A ) , A * and entrepreneur Elad Gil . Zhang says that the cash is being put toward mathematical product growing and expand Decagon ’s San Francisco - based workforce .
“ A key challenge is that customers equate AI federal agent to previous generation chatbots , which do n’t in reality get the job done , ” Zhang said . “ The customer financial support market is saturated with older chatbots , which have eat away lose consumer reliance . Modern solutions from this generation must cut through the noise of the incumbent . ”
