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How Fyxer built an AI executive assistant people trust

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anglais américain
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Transcription de How Fyxer built an AI executive assistant people trust

Transcrit par un modèle vocal à partir du son du film, et non rédigé par la marque. Cliquez sur une ligne pour y amener la lecture.

  1. 0:00We've had real customers say to us,
  2. 0:01this year I think you've saved me from getting a divorce.
  3. 0:08Fixer is an AI assistant for people managing clients,
  4. 0:11colleagues and conversations every day
  5. 0:14to make sure they don't miss anything important.
  6. 0:17The divorce guy, he's real and just awesome.
  7. 0:19He's called Mike.
  8. 0:20He's a commercial real estate manager,
  9. 0:23managing hundreds of different properties,
  10. 0:24hundreds of different people.
  11. 0:25And so all day on the road, can't text while driving.
  12. 0:28and so comes home in the evening
  13. 0:30and has to like catch up on emails
  14. 0:31and that one to two hours a day
  15. 0:33he's unable to like have proper family time.
  16. 0:35We want Mike to get to a place
  17. 0:37that when he opens his laptop
  18. 0:39the prioritizing has already been done for him.
  19. 0:42It's all been read, it's all been sorted
  20. 0:44and all the stuff that actually needs him
  21. 0:46is right there at the top.
  22. 0:48Fix and multiply is across the team now
  23. 0:51meaning everyone can collaborate on anything important
  24. 0:54to avoid anything slipping through the cracks.
  25. 0:58We've been in this office six months, I think.
  26. 1:00We're all in the same floor now.
  27. 1:02We all have lunch together and it's just like a nice vibe.
  28. 1:06We already know that ChatGPT is like the second biggest AI tool that our customers use besides Fixer.
  29. 1:13I mean, their models are obviously just state of the art.
  30. 1:16So when they release a new model, it's really easy to create something really good.
  31. 1:20This is engineering, where all the real work happens.
  32. 1:24This is Joey, Eleonora, David.
  33. 1:27is they work closely with open ai literally my first day at the company i met with math as cto
  34. 1:32and he was like okay your job was going to be to figure out a way to make the drafts that we
  35. 1:37generate for people better various parts of that flow are integrated using open ai so we use open
  36. 1:43ai models for everything from digesting the email so we can break down and understand what the email
  37. 1:49is actually about to pulling in and re-ranking the context that we want to include we've had an
  38. 1:55amazing experience working with a team at OpenAI, getting their thoughts on how we can improve our
  39. 1:59models, how we can improve costs, how we can make things more efficient, and actually getting some
  40. 2:04of that technical input as well. So it was the release of GPT-3. It was the moment where I just
  41. 2:10got exactly the answer back that I expected. So my brother called me and he was like,
  42. 2:15it's actually working. We'd been building an executive assistant agency together, but
  43. 2:19it was OpenAI that actually made Fixer possible.
  44. 2:22It allowed us to learn from the 500,000 hours of EA workflows
  45. 2:27in just a matter of months.
  46. 2:29And the result of that was in our first year,
  47. 2:31we went from zero to 30 million in ARR.
  48. 2:34One of the real keys for Fixer succeeding as a business
  49. 2:38and delivering a better product than anyone else
  50. 2:40has literally just been about doing the hard work
  51. 2:42of learning our customer really well.
  52. 2:44And what OpenAI was then really pivotal for us in doing
  53. 2:47was being able to take that learning
  54. 2:49and successfully incorporate it into how the models work.
  55. 2:53I can see the YC mentality is absolutely coded into the OpenAI team.
  56. 2:59At any moment, I can drop a question in Slack,
  57. 3:02and I've got an answer coming out pretty quickly.
  58. 3:05I've got what feels like a very kind of entrepreneurial and dynamic team.
  59. 3:09Honestly, it feels like a five-star hotel.
59 lignes · 597 mots

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