Proaction（在新窗口中打开）为管理车队的企业构建软件，涵盖从汽车、卡车到工程机械的各种车辆。由于每个车队的运营方式各不相同，向潜在客户展示 Proaction 平台如何契合其业务，是销售该平台的关键环节。
但个性化演示需要工程时间，而团队抽不出这些时间。创始人不得不依赖对话和幻灯片来讲解平台能实现什么。Codex 改变了这一切。
“作为非技术人员，我以前想做个演示都得拉工程师进来，”Proaction 联合创始人兼首席运营官 Colin Knudsen 说。“现在我在 Codex 里自己就能做。”Colin 每月创建四到六个定制化交互演示，每个耗时 30 到 45 分钟。他估计这避免了 40 到 60 小时的工程工作。
“我们没有足够的工程能力为每个感兴趣的潜在客户都做一个演示。有了 Codex，我可以轻松地把一次客户对话变成围绕他们的车辆和工作流构建的定制演示。”
——Colin Knudsen，Proaction 联合创始人兼首席运营官
用 Codex 定制演示并促成更多交易
销售电话结束后，Colin 会让 Codex 读取 Granola 录音、潜在客户的邮件往来，以及对方分享的任何电子表格。Codex 利用这些上下文，定制出一个 HTML 演示环境，既模仿 Proaction 的产品，也贴合客户自己的车队。
当 Colin 共享屏幕时，潜在客户看到的是他们自己的汽车、卡车或设备，并按照他们的工作方式组织呈现。他们可以指出需要调整的地方，并参与构建解决方案。“你们一起生成最终方案，完全不需要工程团队介入，”Colin 说。
向潜在客户展示带有他们自己数据的演示，给了他们一个具体的理由继续推进。Colin 估计，借助定制演示，从初次接触进入解决方案开发阶段（而非培育阶段）的交易比例提高了 50% 到 60%。
一路节省工程时间，直到生产环节
据 Colin 说，如果由 Proaction 的工程师制作同等水平的演示，每个大约需要 10 小时。由于 Proaction 每月大约做四到六个演示，这意味着每月节省 40 到 60 小时的工程工作。
演示在后端也节省了时间。当潜在客户成为客户后，Colin 会把定制演示交给工程师作为视觉参考。这减少了关于要构建什么的疑问和反复沟通。
使用 Codex，Proaction 还构建了一个客户解决方案中心，潜在客户可以登录、探索为其业务量身定制的工作流，并查看销售材料。这使客户能够说明自己的需求，也让非工程团队的成员能够把这些对话转化为更清晰的需求。等工程师介入时，他们对要构建什么已经有了更具体的认识。
在一个地方完成更多工作：Codex
Colin 的日常工作横跨销售、客户支持和产品管理。通过 Codex 针对 Granola、Gmail、Slack、Linear、GitHub 和 HubSpot 等工具的插件，Colin 把客户上下文汇集起来，并在 Codex 内采取行动。
他调取通话记录和邮件历史来准备跟进，创建 Linear 问题，并更新 HubSpot 商机。他还设置了一个定时自动化，用于查看近期通话并为团队准备销售更新。
以前，繁重的工作量意味着他要在多个标签页之间跳转，把信息从一个工具复制到另一个工具。现在，Colin 可以描述自己的需求，用 Codex 收集上下文并执行下一步。“我做的所有事情都围绕在 Codex 里工作。我很少离开它，”Colin 说。他估计自己每天有 15 到 20 项不同的任务，并认为 Codex 每月为他节省 25 到 33 小时。
“我很难想象没有 Codex 怎么当创业公司创始人。”
——Colin Knudsen，Proaction 联合创始人
用 GPT‑Live‑1 和 GPT‑6 Astra 将语音智能体引入车队运营
Proaction 还在其整个平台中使用 OpenAI 模型。当客户提交带有车辆问题报告的照片时，ChatGPT‑5.6 Sol 帮助识别损坏情况。借助 GPT‑Live‑1，Proaction 正在构建能够处理更多车队日常运营工作的智能体。公司将其称为“托管执行层”。
“我们正在让 Proaction 具备为客户执行工作的能力，而不仅仅是帮助他们管理和跟踪。OpenAI 语音技术的进步是我们能做到这一点的重要原因。”
——Colin Knudsen，Proaction 联合创始人
客户可以要求专门的智能体处理通行费或维修等事务，也可以设置工作流，让合适的智能体自动投入工作。这些智能体使用 OpenAI 模型，包括 GPT‑Live‑1 和 GPT‑6 Astra，来拨打电话、查看文档和图像、分析文本，并在聊天中回复。
其中一个智能体 Marty 旨在协调车辆维护。Marty 与司机讨论问题，致电维修店，安排服务，并帮助估算获得批准和付款。当工作需要人工审核或干预时，Proaction 团队会介入。
GPT‑6 Astra 还使 Proaction 能够更快地为客户构建这些体验。产品负责人 Danny O’Halloran 说：“Astra 的计算机使用运行更简洁。用 GPT‑5.6 Sol 时，我要跑长得多的时间才能完成同样的工作。”
Codex 和 GPT‑6 Astra 让 Proaction 有更多时间销售、支持客户并推动业务向前发展。而借助基于 GPT‑Live‑1 构建的智能体，Proaction 也为车队经理争取了更多时间。
Proaction(opens in a new window) builds software for businesses that manage fleets of vehicles, from cars and trucks to construction machinery. Because every fleet operates differently, showing prospects how Proaction’s platform fits their business is an essential part of selling it.
But personalized demos required engineering time the team couldn’t spare. The founders had to rely on conversations and slide decks to explain what was possible. Codex flipped the script.
“As a non-technical person, I used to have to loop engineers in if I wanted a demo,” says Colin Knudsen, co-founder and COO of Proaction. “Now I do it myself in Codex.” Colin creates four to six customized, interactive demos a month, with each taking 30 to 45 minutes to build. He estimates that this avoids 40 to 60 hours of engineering work.
“We didn’t have the engineering capacity to build a demo for every interested prospect. With Codex, I can easily take a customer conversation and turn it into a custom demo built around their vehicles and workflows.”
—Colin Knudsen, Co-founder and COO, Proaction
Customizing demos and closing more deals with Codex
After a sales call, Colin directs Codex to the Granola recording, prospect email threads, and any spreadsheets the prospect has shared. Codex uses that context to customize an HTML demo environment that mirrors Proaction’s product and the customer’s own fleet.
When Colin shares his screen, prospects see their own cars, trucks, or equipment, organized around how they work. They can point to what needs adjusting and help build the solution. “You work together to generate the end solution without getting engineering involved at all,” Colin says.
Offering prospects a demo with their own data gives them a concrete reason to move forward. Colin estimates that the percentage of deals moving from initial contact into solution development, rather than nurture, has increased by 50% to 60% with the custom demos.
Saving engineering hours all the way to production
If Proaction’s engineers produced comparable demos, it would take about 10 hours each, according to Colin. Since Proaction does about four to six demos monthly, that represents 40 to 60 hours of engineering effort spared every month.
The demo also saves time on the back end. When a prospect becomes a customer, Colin gives engineers the customized demo as a visual reference. That reduces questions and back-and-forth about what to build.
Using Codex, Proaction also built a customer solution center where prospects can log in, explore workflows tailored to their business, and review sales materials. This empowers customers to explain what they need and gives non-engineering teammates a way to turn those conversations into clearer requirements. By the time engineers get involved, they have a more concrete picture of what to build.
Getting more done, all in one place: Codex
Colin’s daily work spans sales, customer support, and product management. Through Codex plugins for tools including Granola, Gmail, Slack, Linear, GitHub, and HubSpot, Colin brings together customer context and acts on it within Codex.
He pulls call transcripts and email history to prepare follow-ups, creates Linear issues, and updates HubSpot opportunities. He also set up a scheduled automation that reviews recent calls and prepares sales updates for the team.
Previously, his heavy workload meant jumping between tabs and copying information from one tool to another. Now, Colin can describe what he needs and use Codex to gather the context and execute the next step. “Everything that I do is centered around working in Codex. I don’t leave it much,” says Colin. Estimating he has 15 to 20 distinct tasks a day, Colin believes Codex saves him 25–33 hours a month.
“I can’t really imagine being a startup founder without Codex.”
—Colin Knudsen, Co-founder, Proaction
Bringing voice agents to fleet operations with GPT‑Live‑1 and GPT‑6 Astra
Proaction also uses OpenAI models across its platform. When customers submit photos with vehicle issue reports, ChatGPT‑5.6 Sol helps identify damage. And with GPT‑Live‑1, Proaction is building agents that handle more of the day-to-day work of running a fleet. The company calls this its Managed Execution Layer.
“We’re building the ability for Proaction to execute work for our customers, beyond helping them manage and track it. The advances in OpenAI voice are a big reason we can do it.”
—Colin Knudsen, Co-founder, Proaction
Customers can ask specialized agents to handle things like tolls or service, or set up workflows that put the right agent to work automatically. The agents use OpenAI models, including GPT‑Live‑1 and GPT‑6 Astra, to make voice calls, review documents and images, analyze text, and respond in chat.
One agent, Marty, is designed to coordinate vehicle maintenance. Marty talks with a driver about a problem, calls repair shops, arranges service, and helps get the estimate approved and paid. Proaction’s team steps in when the work needs human review or intervention.
GPT‑6 Astra also enables Proaction to build these experiences for customers faster. Danny O’Halloran, Head of Product, says, “Astra’s computer-use runs are more succinct. With GPT‑5.6 Sol, I had a much longer run to execute the same work.”
Codex and GPT‑6 Astra give Proaction more time to sell, support customers, and move the business forward. And with agents built on GPT‑Live‑1, Proaction gives more time to fleet managers, too.
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