## Overview This case study presents Zapier's extensive internal use of AI and LLM-powered automation across their organization. As a workflow automation platform provider, Zapier has implemented numerous production AI systems that demonstrate practical LLMOps patterns at scale. The article was published in January 2025 and showcases how Zapier uses its own platform combined with large language models (primarily ChatGPT and OpenAI's API) to solve operational challenges across diverse departments including customer success, sales, HR, technical support, content creation, engineering, accounting, and revenue operations. The case study is notable for its breadth, demonstrating over a dozen distinct production LLM use cases rather than a single application. While the source material is promotional in nature (being published on Zapier's own blog), it provides concrete examples of how LLMs are integrated into real business workflows with specific technical implementations described through their workflow templates. ## Customer Success Operations Zapier's customer success operations team has deployed several LLM-powered workflows to streamline customer interaction management. One key workflow automates pre-call preparation by having an AI system analyze HubSpot CRM records to gather company information, identify potential automation challenges, and share contextual briefings with team members via Slack before customer calls recorded in Gong. This represents a practical example of using LLMs for information synthesis and contextual preparation. Another production system automatically processes recordings of customer calls from their sales CRM, uses ChatGPT to generate summaries with key points and takeaways, and distributes these summaries to the team via Slack. This enables knowledge sharing and organizational learning without requiring manual note-taking and distribution. The system also performs sentiment analysis on customer interactions, providing comprehensive views of customer sentiment to inform product improvements. This demonstrates practical deployment of LLMs for both summarization and sentiment classification tasks in a customer-facing operational context. ## Sales Operations The sales organization uses AI to automate the labor-intensive process of lead qualification and prioritization. Their production system generates transcripts of sales calls with leads, creates AI-powered summaries of those meetings using ChatGPT, and automatically updates the appropriate lead records in HubSpot with the summarized information. This workflow eliminates manual administrative overhead while providing sales managers with better visibility into deal status and conversion likelihood. The system demonstrates practical integration of speech-to-text, summarization, and CRM integration in a production sales context. ## HR and Onboarding Zapier's HR and onboarding teams deal with substantial survey and form data including onboarding experience surveys, employment feedback forms, and retreat assessments. They have deployed an AI-powered sentiment analysis system that automatically processes survey responses from Typeform, classifies sentiment as positive, neutral, or negative, and aggregates results for analysis. This production system saves considerable manual review time while ensuring no feedback falls through the cracks. The workflow demonstrates practical application of LLMs for classification tasks on free-text survey responses, with integration between survey tools (Typeform), the LLM (ChatGPT), data storage (Google Sheets), and communication platforms (Slack). ## Technical Support Operations The technical support operations team has implemented multiple sophisticated LLM-powered systems. One notable implementation is a ChatGPT-powered support bot integrated into a dedicated Slack channel that helps team members troubleshoot Zap issues, generate automation ideas, and learn about platform features more quickly. This represents an internal-facing chatbot deployment for technical support knowledge. The team also built an automated escalation management system that uses ChatGPT to create formatted daily summaries of the previous day's activities in Slack, including all escalated tickets and resolved issues. This leverages Schedule by Zapier combined with ChatGPT to provide visibility and enhance team communication without manual status reporting. A particularly sophisticated workflow handles status page updates: when support specialists submit incident descriptions via Typeform, ChatGPT generates appropriately formatted status page messages using predefined templates and rules, sends the generated message to Slack for human approval, and then uses webhooks to post the approved update to Zapier's public status page. This demonstrates a human-in-the-loop pattern for customer-facing communications where AI generates draft content subject to human review before publication. Perhaps most impressively, the support team built what they call a "Zapier support sidekick" - a sophisticated RAG (retrieval-augmented generation) system deployed in Slack. The workflow operates as follows: when a support team member reacts to a question in Slack with a specific emoji, ChatGPT analyzes the message and generates a targeted search term; webhooks then retrieve relevant help documentation and blog posts matching that search query; a ChatGPT assistant reviews the entire search history including ticket messages, customer interactions, and troubleshooting notes; finally, the assistant responds in the Slack thread with hyperlinked steps referencing the most relevant documentation and blog posts. This represents a production RAG implementation with automated retrieval, context assembly, and response generation, demonstrating sophisticated LLMOps practices including semantic search integration, context management, and conversational interface deployment. ## Technical Writing and Documentation Zapier's technical writing team maintains an extensive help center that requires constant updates as the platform evolves. They have deployed a sophisticated AI agent (using Zapier Agents) that autonomously handles aspects of release note creation. The agent monitors new help center articles published in Zendesk, verifies whether articles contain product updates according to predefined rules, drafts product updates in Google Docs based on article context, and notifies the team in Slack when drafts are ready for review. This represents deployment of autonomous AI agents for content generation tasks, demonstrating a production agent-based system with verification logic, content generation, and human review integration. The implementation showcases an important LLMOps pattern: agents that work autonomously in the background handling routine tasks while incorporating human review checkpoints for quality assurance before final publication. This balances automation efficiency with content quality requirements. ## Content and Video Production The content team has implemented multiple LLM-powered workflows for research and writing assistance. One production workflow uses AI to read and analyze source articles, then summarizes key points and takeaways to accelerate research processes. Writers can trigger this via the Zapier Chrome extension integrated with ChatGPT, demonstrating browser-based workflow integration. The team has also created custom editorial chatbots using Zapier Chatbots that provide on-demand feedback on draft content. These bots can add humor, make introductions more concise, or provide other editorial suggestions based on specific prompts. This represents deployment of task-specific chatbot assistants for content refinement, reducing editorial back-and-forth while helping writers learn through AI-generated suggestions. The video production team uses AI to generate rough scripts for YouTube videos based on article outlines from the blog team. While not used as final products, these AI-generated scripts (sent via Slack for review) provide starting points that the team refines, demonstrating appropriate use of LLMs as assistants rather than autonomous creators for creative work. This shows a measured approach to LLM deployment where AI accelerates but doesn't replace human creativity. The partnerships team built a workflow monitoring changes external developers make to their integrations, uses AI to rewrite updates according to Zapier's style guide, then publishes them in release notes after human review. This demonstrates using LLMs for style normalization and brand voice consistency at scale. ## PR and Social Media The PR team has implemented AI-powered press release generation that takes structured inputs (event details, company information, quotes, key points) and generates professionally styled press releases according to organizational templates. This demonstrates using LLMs for templated content generation where structural consistency is important. The team has also created evaluation bots that analyze work products (documents, designs) and generate constructive feedback based on predefined criteria, demonstrating LLM application for quality assessment and review processes. The social media team uses various bots to automate social media tasks including posting content at optimal times, responding to comments and messages, and analyzing data to understand trends and user behavior. While less detail is provided about these implementations, they demonstrate LLMs deployed for social media management automation. ## User Education and Training The user education team addressed the challenge of making Zapier's English-language video courses accessible to international customers by implementing an AI-powered translation workflow. They store English SRT (SubRip subtitle) files for each video course in Zapier Tables, then use a workflow that pulls these captions and asks ChatGPT to translate them into ten popular languages while preserving timestamps. This demonstrates practical production use of LLMs for localization and translation at scale, with structured data management through Tables. The team also deployed Zapier Chatbots to deliver personalized learning experiences. These bots help customers discover automation ideas and use cases relevant to their specific roles and technology stacks. As customers complete course modules, they can interact with AI to get customized recommendations. This shows LLMs deployed for personalized educational content recommendation based on user context and progress. ## Learning and Development Zapier's L&D team has created dozens of chatbots to enhance internal learning experiences. These include coaching bots that provide employees with coaching tips and bots that generate ice breakers for training sessions and team meetings. The team emphasizes prompt engineering best practices, specifically providing AI with company-specific context about Zapier, role context as a learning designer, and clear purpose statements to ensure outputs are relevant and contextual. This demonstrates attention to prompt engineering as a key LLMOps practice, recognizing that output quality depends heavily on providing appropriate context and instructions. ## Engineering The engineering team has implemented several sophisticated LLM-powered workflows for project management and knowledge capture. One system summarizes Slack messages, transforms summaries into Jira tickets, and adds them to planned sprints, demonstrating automated ticket creation from conversational context. Another workflow analyzes customer interactions within Zendesk tickets, uses AI to categorize conversations, and adds metrics (like reply counts) to Google Sheets for tracking, demonstrating production deployment of LLMs for ticket categorization and analytics. A particularly innovative implementation is the engineering team's "brag docs" system for career development. Team members can add reaction emojis to personal wins or mentions in Slack. These messages are sent to a ChatGPT assistant that, using Zapier's business impact documentation as a framework, classifies the accomplishment and generates a summary demonstrating the impact. These summaries are stored in individual tables for reference during goal cycles, promotion processes, and performance reviews. This represents an interesting use case of LLMs for personal career documentation and impact articulation, with one engineer noting it helps tackle imposter syndrome by systematically capturing achievements. The team also uses automated workflows to collect achievements from multiple systems (Google Calendar, Jira, Slack, GitLab) in Tables, then uses ChatGPT to summarize and organize this information into succinct daily standup updates. This demonstrates LLM application for information aggregation and synthesis across multiple data sources. ## Accounting and Finance The accounting department uses AI integrated with project management workflows to prioritize tasks. Their system pulls task lists from Slack, uses AI to prioritize them by importance, and estimates time slots for completion. This demonstrates LLM application for task prioritization and time estimation. One employee implemented a personal finance tracking workflow (with potential business applications) where email alerts about credit card transactions trigger AI extraction of relevant information (vendor, amount), automatic categorization, and logging to Google Sheets. This demonstrates LLMs for information extraction and classification from unstructured email content for financial tracking and analysis. ## Revenue Operations The Revenue Operations team has implemented sophisticated personalization workflows for email marketing at scale. They created a system that automatically generates trial expiration emails with dynamic copy highlighting Zapier features most relevant to each user. The workflow pulls user information from their marketing automation system and uses ChatGPT to generate personalized content, eliminating the need for complex if/then logic and personalization tokens traditionally required for email personalization. This demonstrates LLM application for dynamic content generation with user-specific context, enabling personalization at scale without traditional templating complexity. The team also built a Slack bot that answers common questions from internal sales representatives about lead stages, definitions, and sales processes. This bot uses Zapier's internal documentation to provide answers, ensuring responses are current and accurate. This represents another RAG-style implementation where documentation is used as context for LLM responses, demonstrating knowledge base integration for internal support. ## LLMOps Patterns and Considerations This case study reveals several important LLMOps patterns deployed at scale across Zapier: **Integration Architecture**: Nearly all implementations follow a similar architectural pattern: trigger events from operational systems (Slack, email, CRM, forms) → LLM processing (primarily ChatGPT) → output to destination systems (Slack, Google Sheets, CRM, Google Docs). This demonstrates a consistent integration approach using Zapier's platform as orchestration layer with LLMs as processing components within larger workflows. **Human-in-the-Loop**: Multiple workflows incorporate human review checkpoints, particularly for customer-facing content (status page updates, press releases, video scripts). This represents mature LLMOps practice recognizing that while AI can generate drafts efficiently, human oversight remains important for quality and appropriateness, especially in public-facing contexts. **RAG Implementations**: Several systems (support sidekick, internal sales bot) demonstrate retrieval-augmented generation patterns where relevant documentation is retrieved and provided as context for LLM responses. This shows practical deployment of RAG architectures for knowledge base applications. **Task-Specific Bots and Agents**: The proliferation of specialized chatbots (editorial bots, learning bots, coaching bots) and autonomous agents (documentation agent) demonstrates an approach of creating focused AI assistants for specific tasks rather than general-purpose systems. This suggests benefits from task-specific optimization and prompt engineering. **Prompt Engineering**: Multiple teams emphasize providing detailed context, role definitions, and clear instructions to LLMs. The L&D team explicitly mentions providing company context, role context, and purpose as key to output quality. This demonstrates organizational awareness that prompt engineering is critical to LLMOps success. **Multimodal Workflows**: Some workflows chain multiple LLM calls (analysis → search term generation → retrieval → response generation) or combine LLMs with other processing (sentiment analysis, categorization, summarization, translation). This demonstrates comfort with complex multi-step AI workflows rather than simple single-shot LLM calls. **Internal vs. External**: Most implementations are internal-facing (employee productivity, internal support, knowledge management) rather than customer-facing. Customer-facing applications generally include human review steps. This suggests a measured approach to LLM deployment based on risk assessment. **Data Management**: Several workflows use Zapier Tables for structured data storage (video subtitles, achievements, tasks), demonstrating attention to data management as part of LLMOps infrastructure. **Evaluation and Feedback**: While the case study doesn't explicitly discuss evaluation methodologies, the emphasis on human review loops and iterative refinement suggests informal evaluation through human feedback rather than automated metrics. ## Critical Assessment While this case study provides valuable examples of production LLM deployments, several caveats merit consideration: **Source Bias**: The content is published on Zapier's blog as promotional material for their platform and AI capabilities. Claims about time savings, efficiency gains, and impact are not quantified with specific metrics or independently verified. The article serves both informational and marketing purposes. **Limited Technical Depth**: Most workflows are described at a high level without detailed technical specifications. Critical LLMOps concerns like error handling, fallback strategies, cost management, latency, prompt versioning, and model selection are not discussed. The simplicity of descriptions may understate actual implementation complexity. **Evaluation and Quality Metrics**: The case study provides no information about how LLM output quality is measured, monitored, or improved over time. There's no discussion of accuracy rates, user satisfaction metrics, or formal evaluation frameworks. This is a significant omission from an LLMOps perspective. **Failure Modes**: No mention is made of failures, limitations, or challenges encountered with these LLM implementations. Real production systems inevitably face issues with hallucinations, inconsistent outputs, or inappropriate responses, but these aren't discussed. **Cost Considerations**: No information is provided about the operational costs of running these numerous LLM-powered workflows, including API costs, compute resources, or maintenance overhead. **Model Selection**: The case study primarily mentions ChatGPT and OpenAI without discussing why these models were chosen over alternatives, whether multiple models are used for different tasks, or how model selection decisions are made. **Prompt Management**: While prompt engineering is mentioned, there's no discussion of how prompts are versioned, tested, or updated over time - critical concerns for production LLMOps. **Security and Privacy**: For a company handling customer data across many integrations, there's no discussion of how sensitive information is handled when passed to LLM APIs, what data governance policies are in place, or how privacy is maintained. **Scalability**: While many use cases are described, there's no information about scale (number of workflows executed, volume of LLM calls, data processed) or whether these systems have scaled smoothly or encountered challenges. Despite these limitations, the case study provides valuable insight into practical LLM applications across diverse business functions and demonstrates Zapier's own extensive use of AI automation internally. The breadth of use cases, emphasis on human-in-the-loop patterns, and focus on task-specific implementations over general-purpose systems offer useful patterns for organizations implementing their own LLMOps practices. The case study is particularly valuable for demonstrating how LLMs can be integrated into existing operational workflows rather than requiring entirely new systems or processes.