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How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared

プラットフォーム
YouTube
長さ
3:34
話されている言語
アメリカ英語
再生数
310
カット間隔の中央値
4.51s
顔の映る割合
59%
暗いフレーム
56%
Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 0:00corner inset
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 0:35close presenter
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 0:35medium presenter
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 0:46
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 0:49
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 0:58
SkydioHow Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 1:33medium presenter
SkydioHow Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 1:34medium presenter
SkydioHow Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 1:35close presenter
SkydioHow Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 1:36corner inset
SkydioHow Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 1:42close presenter
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Skydio How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared at 1:59
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How Winston-Salem PD Collapsed Emergency Response Times with Skydio and Prepared の文字起こし

ブランドが書いたものではなく、映像の音声から音声モデルが書き起こしたものです。行をクリックすると、その瞬間に移動します。

  1. 0:00If you look at it as a whole, we are now arriving on scene in what it used to take to just ingest the call.
  2. 0:05That's wild.
  3. 0:15The essence of DFR requires getting there quickly and having the right context for the operator to team with the people on the ground,
  4. 0:22which is why we are incredibly excited to announce our partnership with Prepared,
  5. 0:28which is really the future of 911.
  6. 0:30To tell you more about it, I'm very excited to be joined by Michael Chime, the CEO of Prepared.
  7. 0:35Yeah, thanks so much, Adam.
  8. 0:36What we do is when a 911 call comes in, we have assistive AI that helps that operator,
  9. 0:42helps that person handling the call, process it as fast and as high quality as they possibly can.
  10. 0:49The second that we transcribe it, that information is accessible,
  11. 0:52whether that's in the field, whether that's for DFR, that could be in your real-time crime center platform like
  12. 0:57Afusis, that information is there and we save those seconds or we save those minutes.
  13. 1:02Prepared, I think, is really the single most valuable source of information for DFR operators because it can get the call data with very low latency,
  14. 1:09shows up instantly, and then it provides all the context they need to do their job.
  15. 1:14It tells them what they're heading into, they can get the live audio, they can get the call transcription,
  16. 1:18They can get the AI summary and that gives them everything they need to do their job as effectively as possible
  17. 1:24And we're very fortunate to be joined today by Assistant Chief Manny Gomez who oversees criminal intelligence for Winston-Salem Police Department
  18. 1:32So thank you for joining us here. Can you describe the before situation?
  19. 1:36So before you had prepared and and DFR integrated, what was the time response and then what does that look like now?
  20. 1:42Yeah, so that was that was call comes in this call taker ingest the call into the CAD system and then sends it over to dispatch
  21. 1:50The dispatcher then sends that call to our patrol personnel
  22. 1:53That was a workflow that we had to go through to get that call for service to get an officer to even begin to respond to that call for service
  23. 1:59So now what we're doing is as soon as the call for service comes in that this
  24. 2:04Pilot can go ahead and make a determination that he needs to respond to the location
  25. 2:07By the time he hits the launch now feature within the Skydio command platform, we're
  26. 2:11averaging anywhere between 5 to 10 seconds before the aircraft is airborne.
  27. 2:17We are sending that aircraft now, or the Skydio X10, to the call for service.
  28. 2:22Upon arrival, we're averaging about 120 seconds to the call for service after it launches.
  29. 2:27So if you look at it as a whole, we are now arriving on scene in what it used to take
  30. 2:33to just ingest the call.
  31. 2:34So it's streamlined our operations.
  32. 2:36we can arrive to the scene faster, whereas before we were having to wait through that whole process,
  33. 2:42so two minutes plus the additional 120 seconds.
  34. 2:45You're talking about now four minutes for your average call response,
  35. 2:48where now we're responding within the time it used to take to ingest that call.
  36. 2:52Yeah, I mean, it's so powerful.
  37. 2:53I mean, it's worth saying, again, basically with the prepared and DFR integration,
  38. 2:58the drone is on scene before dispatch in the normal case would have even alerted the officers that they need to send.
  39. 3:03So it's collapsed all of that into effectively zero.
39行 · 602語

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