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OpenAI Software / SaaS North America

Proaction

IT Operations Automation · Sales Automation

Proaction needed a way to create highly personalized fleet-management demos and operational follow-ups without consuming scarce engineering time, while also extending automation into day-to-day fleet workflows for customers.

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30 to 45 minutes to build each demo
33 founder hours saved per month
40-60 engineering hours saved per month
60% increase in sales

Reported impact

Business outcome

Proaction increased sales by 60%, saved 40-60 engineering hours a month on custom demos, saved founders 25-33 hours a month on cross-tool work, and began extending voice agents into fleet operations so customers reclaim time as well.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

Proaction turned Codex into the working surface for sales, product, and support operations. After a call, the team feeds Codex the Granola meeting recording, prospect email threads, and shared spreadsheets so it can build an HTML demo environment that mirrors Proaction's product and the prospect's actual fleet. Those demos then double as implementation references once a deal closes, reducing back-and-forth with engineers. Codex plugins connect Gmail, Slack, Linear, GitHub, and HubSpot so follow-ups, issue creation, and sales updates happen in one place. On the customer side, Proaction is extending the same model stack into its Managed Execution Layer, where GPT-Live-1, GPT-6 Astra, and ChatGPT-5.6 Sol support voice agents such as Marty that coordinate maintenance, review documents and images, and help execute fleet operations with humans stepping in only when exceptions require judgment.

Data flow

Sales context from Granola, email threads, and spreadsheets flows into Codex, which creates a prospect-specific HTML demo and writes updates into connected business systems. For customer operations, voice, document, image, and chat inputs route through GPT-Live-1, GPT-6 Astra, and ChatGPT-5.6 Sol so agents such as Marty can coordinate maintenance and other fleet workflows before escalating exceptions to humans.

Solution architecture

6 components · 4 layers
  1. Ingest
    • Granola Supplies call recordings that Codex turns into customized demos and downstream sales actions.
  2. Compute
    • GPT-Live-1 Powers voice agents that execute parts of day-to-day fleet operations for customers.
    • GPT-6 Astra Accelerates the creation of customer experiences and computer-use flows for Proaction's agents.
    • ChatGPT-5.6 Sol Helps identify vehicle damage from submitted photos and supports platform analysis tasks.
  3. Serving
    • Marty Coordinates maintenance conversations, repair-shop calls, approvals, and payments for fleet issues.
  4. Orchestration
    • Codex Builds personalized demos, gathers context from connected tools, and automates sales and operational follow-up work.

Architecture clues

  • Codex plugins connect Granola, Gmail, Slack, Linear, GitHub, and HubSpot
  • Customized HTML demos are reused as implementation references after a deal closes
  • Human review remains in the loop for maintenance and payment exceptions
  • Managed Execution Layer routes voice, text, image, and document work to specialized models

Evidence from the source

"Colin directs Codex to the Granola recording, prospect email threads, and any spreadsheets the prospect has shared."
"Everything that I do is centered around working in Codex. I don't leave it much."
"One agent, Marty, is designed to coordinate vehicle maintenance."
"With Codex, I can easily take a customer conversation and turn it into a custom demo built around their vehicles and workflows."
Transparency Evidence & methodology 100/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Proaction customer story

    First-party
    https://openai.com/index/proaction/
    Source type
    Vendor-published customer story
    Published
    Sep 25, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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