Azure OpenAI
Azure OpenAI provides enterprise-grade reasoning, while Planhat provides the customer context and workflow orchestration required for execution. By embedding Azure-hosted models directly into your custom Automations, teams transform unstructured conversation data into actionable records and automated follow-through. This integration ensures that AI-generated insights land directly in your system of record at the exact business moment they are required.
Unlock the power of Azure OpenAI with planhat
Improve net revenue retention
Identify at-risk accounts earlier by using Azure OpenAI to analyze email sentiment and support transcripts for subtle frustration signals. This enables commercial teams to execute proactive retention plays before qualitative issues impact quantitative renewal targets.
Shorten time to value
Accelerate the transition from sales to implementation by automatically generating implementation briefs the moment a deal closes. Synthesizing months of pre-sale call transcripts with AI ensures onboarding teams bypass manual discovery and start with complete context.
Increase process governance
Maintain enterprise security standards by confining AI processing to rule-based Automation steps within a Microsoft-hosted environment. Using structured JSON outputs ensures that AI-generated data updates are predictable, governed, and formatted for immediate downstream use.
Improve commercial predictability
Ground executive reporting in objective evidence by using Azure OpenAI to analyze messy meeting notes and objectives across the portfolio. Translating raw reasoning into consistent account summaries provides leadership with a reliable view of expansion potential and renewal risk.
how it works
Flow & configuration
Secure authentication via App Center
Authorized administrators link Planhat to your Microsoft Azure OpenAI Service through the Custom Connections area in the App Center. You must provide the API token and the Azure base URL containing your specific resource name to establish the secure data bridge. This "bring-your-own-LLM" model ensures that your data remains subject to your own enterprise security and compliance agreements with Microsoft.
Define prompt and chat endpoints
Establish the connection logic by configuring prompt or chat endpoints that define how Planhat communicates with your specific deployments, such as GPT-4o. This setup allows Planhat to pre-fill the necessary authorization headers for secure model invocation. Teams often prefer this route in Microsoft-heavy environments to satisfy strict procurement and privacy requirements.
Embed the "Use AI" step in Automations
Add a dedicated AI step to any Planhat Custom Automation to trigger model requests at specific lifecycle moments, such as a status change or a closed deal. Planhat sends the defined prompt along with real-time customer data, including email history, chat transcripts, and account fields, to Azure OpenAI for processing. This turns a standalone model endpoint into an operational workflow step.
Map structured JSON or narrative outputs
Define how the model response is handled by requesting either unstructured narrative text for notes or structured JSON for branching logic. Structured results allow the Automation to update specific records, enrich Company data, or branch the workflow to notify the Customer Success Manager via Slack based on the AI's reasoning.
Execute operational follow-through
Planhat turns the model response into immediate action, such as creating a Customer Success handover note or updating an at-risk label. This ensures that AI reasoning is recorded directly into the customer record, allowing teams to act on insights without manual data entry or manual review of long conversation chains.
