Free
$0Free plan available.
Indigo is an AI copilot designed for high-performance teams, enabling them to build custom AI tools and workflows to supercharge productivity. It allows users to deploy their own prompt library, build customized AI workflows without coding, automate tasks with natural language commands, and capture/summarize meeting insights. Indigo also provides features like vision-based workflow execution and team data structuring for AI-powered memory.
Use Indigo to build custom AI workflows by defining parameters and behaviors. You can deploy specialized AI agents, automate tasks using natural language commands, capture meeting insights, and transform scattered data into a searchable team memory across your workspace.
Indigo is a unified AI platform that enables powerful generative workflows and assistants while capturing insights across your team's communications.
Core features include a builder for customized AI workflows, AI assistant deployment, natural language command automation, vision-based workflow execution, meeting insights capture, and team data structuring.
Indigo provides free productivity tools, including a GitHub Profile Analyzer, a YouTube Video Post Generator, and a Blog Article Image Generator.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Indigo AI.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 1.0 Pro Deprecated and Gemini 2.5 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.0 Flash Lite and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare Gemini 2.5 Flash and Gemini 2.0 Flash across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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