Durable AI

Enterprise automation built from requirements

Durable AI turns descriptions of business processes into deployed automations that connect existing systems. Its focus is requirements, integration code and ongoing operation. This is the enterprise automation product at durable.ai; the similarly named Durable website builder at durable.com serves a different purpose.

The intended users are operations, IT and business teams with recurring work across applications. Examples include synchronizing customer records, routing incoming leads and handling onboarding steps. A process owner describes what should happen, while the platform helps clarify the systems, rules and exceptions involved before implementation.

How requirements become working integrations

Durable's product explanation describes a Solution Architect that examines connected systems, asks follow-up questions and produces requirements for review. Those requirements remain readable in plain English. Editing a requirement requests a corresponding code change, with version history helping the team understand what changed.

The platform generates software and uses AI calls within it where interpretation is useful, such as classifying a document. This approach makes the specification an important review surface: business users can discuss the intended behavior without starting from a canvas of workflow nodes. It still requires precise decisions about record matching, exceptions and permissions.

The integration library lists services including Salesforce, Slack and Google Sheets. Check the specific operations and authentication required for each connection. A service appearing in a library does not establish that every custom object or API behavior is covered.

A practical workflow for an operations team

Consider a team that needs a nightly customer-data transfer from its CRM to a warehouse. Begin with the objects to include, the destination fields and the schedule. Ask Durable to draft the requirements, then have the CRM owner check field mappings and the warehouse owner check how updates and duplicates should be handled.

Add an explicit rule for failed records and notification recipients. Review the resulting specification before deployment, then compare a small set of source and destination records and inspect the activity feed. This is a proposed evaluation workflow; it helps make the business rules concrete rather than treating a successful deployment as proof that the data is correct.

Maintenance, oversight and data handling

Durable describes monitoring deployed automations and proposing fixes when errors or API changes occur, with specification updates submitted for approval. Assess that maintenance promise against your actual connected systems and escalation needs. Teams still need an owner for access changes, unexpected writes and recovery decisions.

Its privacy policy covers submitted specifications and integration credentials, and describes isolated execution environments plus temporary processing for monitoring and supervision. Review those terms alongside the permissions you grant. Requirements can themselves contain confidential business information, even before an automation reads operational records.

Access and pricing questions

The public site offers a demo and application sign-in rather than a detailed self-service price table. Discuss deployment scope, supported integrations, maintenance coverage and commercial terms with Durable. Before committing, establish who approves changes, how usage is billed, and what access or export arrangements apply if your team later moves the automation elsewhere.