Durable AI

What is Durable AI?

Durable AI converts enterprise problems into production-ready automations by generating, deploying, and maintaining code. It targets teams needing to connect business systems without manual engineering cycles. Access requires booking a demo with the vendor.

What are the features of Durable AI?

• Conversational scoping

  A chat interface asks clarifying questions and investigates connected systems to produce structured requirements before writing code.
• Plain-English requirement editing

  Users modify automation specifications in natural language, and the underlying code updates to match without tickets or sprints.
• Automated code generation

  The platform writes production code rather than agent chains or prompt wrappers, embedding AI only where it adds value.
• One-click deployment

  Moves from approved requirements to production execution instantly, handling testing and infrastructure behind the scenes.
• Self-maintaining automations

  Monitors for API changes or errors, applies fixes automatically, and submits updates to the specification for approval.
• API integrations

  Connects to over 50 systems including Salesforce, Snowflake, Slack, HubSpot, Jira, GitHub, and QuickBooks.

What are the use cases of Durable AI?

• Nightly CRM synchronization

  Extracts Accounts, Contacts, and custom objects from Salesforce and writes them to a Snowflake data warehouse at a scheduled time.
• Invoice processing

  Automates financial document workflows using connected accounting and communication tools.
• Lead routing

  Distributes incoming sales leads across systems based on defined plain-English requirements.

How to use Durable AI?

1. Describe the problem

   Use the chat interface to explain the automation need in plain English while the system investigates connected tools.
2. Review specifications

   Examine the generated human-readable requirements detailing extraction rules, schedules, and destinations.
3. Request modifications

   Click any requirement to describe desired changes in natural language, prompting an automatic code update.
4. Deploy to production

   Initiate deployment to run the automation in real-world environments.
5. Monitor activity

   Track real-time execution steps, view version history, and approve any automatically generated maintenance fixes.