Story copilot

Product Introduction

1. Definition: Story copilot is an advanced AI-driven chat interface integrated directly into the Tines storyboard, functioning as an agentic automation assistant. Technically, it is a Large Language Model (LLM)-powered layer that sits atop Tines' no-code automation engine, enabling users to interact with the platform’s underlying logic, API connectors, and workflow nodes using natural language processing (NLP).

2. Core Value Proposition: Story copilot exists to democratize complex automation by removing the technical barrier of manual API configuration and logic mapping. By transforming natural language prompts into functional, intelligent workflows, it enables security and IT teams to achieve rapid "time-to-value." Its primary objective is to transition organizations from manual, deterministic task execution to an agentic model where AI assists in building, optimizing, and troubleshooting mission-critical processes. Key focus areas include cutting build time from hours to seconds and enhancing the reliability of cross-functional automation.

Main Features

1. Natural Language Workflow Generation: Story copilot utilizes generative AI to interpret user intent and automatically assemble workflow components on the Tines Storyboard. When a user describes a process—such as "Create a workflow that detects a suspicious login and disables the user in Okta"—the copilot selects the appropriate API actions, configures the necessary JSON transforms, and links the nodes logically. This feature leverages Tines' extensive library of API integrations to ensure that the generated actions are compatible with the specific third-party tools in the user's stack.

2. Automated Workflow Optimization: Beyond initial creation, Story copilot analyzes existing storyboards to identify redundancies and inefficiencies. It suggests technical improvements, such as consolidating multiple transform actions into a single step or optimizing data flow to reduce "AI credits" and execution latency. This feature acts as a continuous refactoring engine, ensuring that high-volume workflows remain performant and cost-effective within the Tines AI credit framework.

3. Contextual Troubleshooting and Explainability: When a workflow action fails or a complex logic branch yields unexpected results, Story copilot provides technical diagnosis. It parses error logs and API responses to explain in plain language why a failure occurred (e.g., authentication token expiry or schema mismatch) and suggests immediate remediation steps. For complex, legacy storyboards, users can ask the copilot to "explain how this workflow works," and it will generate a high-level summary of the logic, data dependencies, and outcomes.

Problems Solved

1. Pain Point: The Automation Skills Gap: Traditional SOAR (Security Orchestration, Automation, and Response) and automation platforms often require deep knowledge of Python, JSON, and specific API documentation. Story copilot solves the "blank page" problem where non-developers struggle to start building, allowing them to describe their needs rather than code them.

2. Target Audience:

• Security Operations Center (SOC) Analysts: Who need to automate alert enrichment and incident response rapidly.
• IT Operations Managers: Seeking to streamline employee onboarding, offboarding, and credential management.
• DevOps and Infrastructure Engineers: Looking to connect disparate cloud tools without writing custom middleware.
• Non-Technical Business Users: Who require access to automated data flows without waiting for engineering resources.

3. Use Cases:

• Incident Response: Automatically parsing phishing emails, extracting IOCs (Indicators of Compromise), and searching for those IOCs across an EDR (Endpoint Detection and Response) platform.
• Identity Management: Automating password resets or MFA (Multi-Factor Authentication) prompts via Slack or Microsoft Teams based on triggers from an identity provider like Okta or Azure AD.
• Vulnerability Management: Pulling data from scanners (e.g., Nessus, Snyk) and automatically creating tickets in Jira for high-risk assets.

Unique Advantages

1. Differentiation: Unlike traditional automation tools that are either purely deterministic (rigid and manual) or purely agentic (unpredictable and black-box), Story copilot provides a hybrid approach. It offers the flexibility of AI-led building while maintaining the "human-in-the-loop" guardrails of the Tines Storyboard. Tines is vendor-agnostic; if a tool has an API, Story copilot can interact with it, unlike ecosystem-locked AI tools provided by single-vendor stacks.

2. Key Innovation: The primary innovation lies in the "Agentic Framework" integrated with a secure, reliable integration layer. Story copilot doesn't just suggest code snippets; it builds functional, visual logic that is easy to audit and modify. Furthermore, its integration with Tines' "Cases" and "Workbench" ensures that AI-driven automation remains grounded in real-world governance and monitoring standards.

Frequently Asked Questions (FAQ)

1. How does Story copilot reduce the time required to build an automation? Story copilot eliminates the need for manual API research and drag-and-drop node configuration. By converting a natural language description into a structured workflow, it handles the technical heavy lifting of mapping data fields and setting up logic gates, reducing build cycles from hours of manual labor to seconds of AI generation.

2. Can Story copilot work with internal or niche tools that aren't mainstream? Yes. Since Tines is built on a "connect to anything with an API" philosophy, Story copilot can be used to build workflows for any tool with a documented API. Users can provide the technical context or documentation to the copilot, which then helps configure the custom API requests needed for niche or proprietary internal systems.

3. Is the AI used in Story copilot secure for enterprise environments? Tines prioritizes security by providing governance, guardrails, and monitoring across the platform. Story copilot operates within the Tines AI credit framework, and the platform is designed with "born in security" principles, ensuring that AI-generated workflows adhere to the organization's compliance and data privacy standards while maintaining full auditability of every action taken by the AI.