Merge is expanding the role of generative AI across its organization by deploying GPT-6 Astra alongside ChatGPT and Codex to support both product development and business operations. In a customer story published on OpenAI’s site, Merge details how these models are applied across engineering, marketing and internal workflows, demonstrating a cross-functional approach to AI adoption.
According to the company, Codex and GPT-based models are embedded into code-related tasks and product development reviews. These tools assist engineering teams in reviewing product code and supporting development activities. At the same time, Merge uses ChatGPT and GPT-6 Astra for marketing work and routine business outputs, allocating conversational and planning capabilities where they fit best.
Merge’s deployment spans technical and non-technical use cases. On the product side, engineers rely on Codex and GPT-based models to streamline code review and related development processes. The company frames these applications as part of its product development workflow, where automated review and assistance support engineering productivity and quality assurance efforts.
On the business side, ChatGPT and GPT-6 Astra are used for marketing and internal communications. Merge reports that teams apply these models to prepare routine business outputs, which includes marketing support and other standard documentation. The company also highlights specific operational uses such as planning executive dinners and preparing sales updates, illustrating that its AI usage extends beyond customer-facing features into internal coordination and planning.
Merge’s account underscores a deliberate allocation of models to roles that match their capabilities. Codex and GPT-based tools are positioned where code expertise is needed; ChatGPT and GPT-6 Astra are tasked with producing conversational, creative and planning-oriented outputs. This separation of responsibilities reflects an intent to match model strengths with particular functions across the organization.
The case offered by Merge exemplifies how organizations can apply multiple generative models in tandem rather than relying on a single system. By using Codex for code tasks and GPT-6 Astra and ChatGPT for marketing and business workflows, Merge demonstrates a modular approach to AI deployment that treats models as specialized tools within a broader operational toolkit.
Merge’s description also highlights the practical breadth of applied AI. Beyond technical reviews and marketing content, the company lists everyday operational tasks—such as executive dinner planning and sales update preparation—that benefit from automated support. These examples indicate an interest in leveraging generative models to reduce routine work and to standardize output across teams.
Why this matters, according to the report, is that Merge’s use of GPT-6 Astra, ChatGPT and Codex signals a trend in which organizations integrate generative AI across both engineering and business processes. The combination of developer-focused tools and conversational models offers a pathway for companies to embed AI into diverse functions without treating it as a one-size-fits-all solution.
Merge’s account is presented as a customer story on OpenAI’s website and provides a snapshot of how one company organizes model responsibilities across teams. The approach emphasizes functional alignment—assigning Codex and GPT-based models to code review and product development, while deploying ChatGPT and GPT-6 Astra for marketing and internal business workflows.
As more companies explore generative AI, Merge’s experience highlights an operational pattern: selecting different models for different tasks and applying them across both customer-facing and internal workflows. The company’s use cases—ranging from product code review to executive planning—illustrate the potential for AI to touch multiple layers of an organization, supporting technical rigor and routine business operations alike.
Merge’s integration of these models offers a concrete example of cross-functional AI adoption. By aligning tools with task types and reporting practical applications across teams, the company demonstrates one roadmap for organizations seeking to incorporate generative models into day-to-day work without conflating distinct responsibilities or capabilities.
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