Time-to-value and the maturation of AI Agents. From the release cycle to a curve that keeps climbing
How a structured release cycle for an AI Agent system is built, how broad scopes are managed, and how maturation is governed once the system is live
The best stories start with great conversations. Automate them with your AI agents

Our architecture coordinates multiple specialized Agents. Each Agent manages a specific domain, while an orchestration layer determines which Agent intervenes, when, and how. The result is automated end-to-end processes with full traceability for every decision


Our AI Agents are not static. Every interaction becomes a learning opportunity. The system identifies gaps, suggests improvements, and evolves the knowledge base. This is how we break through the automation plateau and move from 70% to 90% without having to rewrite workflows from scratch. This is what we call Self-improving Agents


Our AI Agents deploy effortlessly across any touchpoint, from the phone contact center to web live chats, from WhatsApp to voice assistants


Our platform connects to your existing systems - CRM, ERP, ticketing, analytics tools - via native connectors and supports the Model Context Protocol (MCP), the emerging standard - introduced by Anthropic - for interoperability between AI Agents and enterprise applications. Automate workflows without rewriting your infrastructure


Our AI Agents speak on the phone just like they would in a contact center. Natural-sounding voice, real-time understanding, IVR management, and voice assistants. All of this is made possible by a proprietary Voice Router that orchestrates speech recognition, language models, and synthesis with the low latency required for a natural and empathetic conversation
Discover how we support complex organizations in automating millions of interactions, improving operational efficiency and customer experience, with full compliance

How a structured release cycle for an AI Agent system is built, how broad scopes are managed, and how maturation is governed once the system is live

The automation rate is the metric used to sum up the value of an AI Agent system. It is a credible figure only when its definition is known, what it measures, on which requests, and with what method

How to translate GDPR, ISO 27001 and the AI Act into verifiable architectural choices, and why the real dividing line isn't the list of acronyms but what happens to your customers' data
Founded in 2016 by a group of innovators to transform the way companies communicate with their customers, indigo.ai has evolved from an ambitious vision into a solid reality, alongside large companies. With a team of over 40 professionals, we work every day with companies in complex, contact-intensive sectors to improve operational efficiency and customer experience







