Scarlett.

Product Introduction

1. Definition: Scarlett is an AI-powered autonomous agent and workflow automation platform that functions as a persistent, Slack-native digital coworker. Technically, it is a cloud-based orchestration layer that connects to external APIs, executes code in isolated environments, and leverages multiple large language models (LLMs) like Anthropic's Claude Opus, Fable, and Claude 5.6 for task-specific reasoning and execution.
2. Core Value Proposition: It exists to automate complex, multi-step business workflows end-to-end, moving beyond text generation to actual task execution. Its primary value is transforming AI from a conversational chatbot into a proactive, tool-using agent that performs real work, thereby increasing operational efficiency and reducing manual, repetitive labor for teams.

Main Features

1. Slack-Native Autonomous Agent: Scarlett operates primarily within Slack, where team members can interact with her via @mentions and direct messages. She maintains context within threads, coordinates tasks across team members, and delivers outputs like reports, documents, and status updates directly into Slack channels. This integration minimizes context-switching and embeds automation into existing communication workflows.
2. Multi-Tool Integration & Execution Engine: The platform connects to over 3,000 business tools (e.g., Salesforce, Stripe, Notion, GitHub, Google Drive). Unlike simple API connectors, Scarlett's core innovation is her ability to write and execute code in a dedicated cloud workspace. She can query databases, manipulate spreadsheets, generate and edit PDFs, update CRM records, and post to social media by programmatically interacting with these connected services.
3. Scheduled & Recurring Workflow Automation: Users can configure Scarlett to own and execute recurring tasks on a defined schedule without manual prompting. Examples include generating a daily team brief at 7:00 AM, compiling a weekly business report every Monday, or triaging support tickets each morning. This feature enables "setting and forgetting" routine operational processes, effectively putting parts of a business on autopilot.

Problems Solved

1. Pain Point: Manual data aggregation and reporting across disparate SaaS tools. Manually pulling data from a CRM, ad platform, support desk, and financial software to create a consolidated report is time-consuming and error-prone.
2. Target Audience: Operations Managers, Founders, Marketing Directors, Customer Success Leads, and Engineering Managers in small to medium-sized businesses who are burdened with operational overhead and seek to automate cross-functional workflows without extensive technical resources.
3. Use Cases: A marketing director needs a weekly performance report comparing Meta Ads and Google Ads spend against Stripe revenue. A founder wants a daily digest of key metrics (signups, churn, revenue) waiting in Slack each morning. A support manager requires incoming tickets from email and Slack to be categorized and summarized for triage.

Unique Advantages

1. Differentiation: Versus general AI chatbots (ChatGPT) or copilot tools (Claude Tag), Scarlett is not a text-completion engine. While others suggest actions or generate drafts, Scarlett executes the entire workflow: planning, tool access, data processing, code execution, and delivery of a finished asset. Benchmarks on the product site show she achieves higher quality outputs than competitors while maintaining faster response times for complex tasks.
2. Key Innovation: The proprietary "cloud computer" architecture. Scarlett is assigned a persistent, isolated workspace where she can run Python scripts, maintain state between tasks, and manage long-running processes. This, combined with a model router that selects the optimal LLM (Claude Opus for reasoning, Claude 5.6 for coding), allows her to perform deterministic, multi-step operations that mimic a human developer or analyst.

Frequently Asked Questions (FAQ)

1. How does Scarlett AI ensure data security and privacy? Scarlett uses isolated cloud infrastructure per workspace, encrypts all data in transit and at rest with AES-256-GCM, and never trains its models on customer data. API keys and credentials are held by a secure proxy and are never exposed to the AI model itself, with sensitive actions requiring manual approval.
2. What is the difference between Scarlett and Zapier or Make for automation? While Zapier and Make are low-code integration platforms for connecting app triggers and actions, Scarlett is a high-level AI agent that understands natural language requests, makes logical decisions, and writes custom code to complete tasks. She handles unstructured workflows that require reasoning, not just predefined "if-this-then-that" chains.
3. Can Scarlett AI build custom software applications or dashboards? Yes, a core capability is writing and executing code. Scarlett can build simple internal tools, generate data visualizations, create dashboards by querying databases, and even deploy small applications, acting as an on-demand junior developer for routine engineering tasks.
4. How long does it take to set up and train Scarlett for my business? Setup is designed to be minimal. After installing the Slack app, users connect their essential tools (e.g., Stripe, Notion) via OAuth, which takes minutes. There is no "training" phase; Scarlett understands workflows through natural language instructions and immediately leverages the connected APIs to perform tasks.