Kindo Docs

Kindo is an AI native automation platform for enterprise technical operations, including SecOps, DevOps, and ITOps teams. It uses intelligent AI agents and a domain-tuned large language model (LLM) to offload repetitive toil and execute operational workflows end-to-end at high speed. Kindo helps eliminate the brittle scripts and siloed tools that have plagued ops teams, by providing a unified agentic system that can run your runbooks, enforce security policies, and respond to incidents in real time. Kindo is model agnostic, and compatible with 26+ LLMs. Our agents can secure infrastructure and respond to threats at GPU speed, all while keeping your data private and compliant. Everything you do in the UI is available through a public API so you can create agents, trigger runs, manage approvals, fetch logs, and wire Kindo into your own apps and pipelines.

Kindo is a full agentic automation platform. Most chatbot-based tools simply give you an AI assistant that can answer questions or summarize information, but they stop short of taking real action. Kindo, by contrast, was built AI-first for enterprise operations: its agents don’t just chat, they autonomously make decisions and take actions in your systems. Unlike basic bots that might alert a human to an issue, Kindo can actually orchestrate multi-step workflows across your tech stack to resolve the issue, with appropriate approvals. It’s like moving from a passive advisor to an active team member. Instead of scripting data integrations and normalization, just connect to integrations and prompt in natural language.

An AI native agent in Kindo is essentially an intelligent software assistant that can understand natural language, reason through a task, and perform actions on your behalf. These agents encapsulate the routines or playbooks that ops teams normally follow. What makes Kindo’s agents unique is that they can read and interpret human language instructions or documents, then translate that intent into programmatic actions (API calls, scripts, etc.). When you deploy an agent, you might give it a goal or let it trigger off an event. The agent will then plan the steps needed, call the right tools via integrations, and adapt as needed to achieve the goal. A Kindo agent could detect a new vulnerability report, look up the affected servers, cross-check if those servers are impacted, and then automatically apply a patch or open a ticket, all in one continuous, AI driven flow.

Deep Hat is Kindo’s proprietary LLM tailored specifically for DevSecOps, SecOps, and infrastructure tasks. Unlike a general-purpose model, Deep Hat has been trained on thousands of real-world IT incidents, cybersecurity scenarios, code logs, and CLI syntax, so it speaks the language of cloud infrastructure and security teams. This tuning means it excels at tasks like reading firewall configs, demonstrating an exploit, or explaining root causes in a deployment failure. Deep Hat delivers faster inference and deeper infrastructure awareness than standard models, giving more accurate and relevant answers for technical work. It’s also designed for adversary simulation and red teaming, which helps in security workflows.

Yes, Kindo supports on-premise deployments with no internet connection required. You can deploy Kindo entirely in your own environment (your data center or private cloud) so that all data, models, and operations stay behind your firewall. In this self-managed mode, Kindo doesn’t rely on any external cloud services; even the AI models (like Deep Hat or other supported models) can run locally on GPUs or your infrastructure. A lot of customers choose this option for security or compliance reasons. In practice, once you install Kindo on your internal network, it can operate completely offline, the agents will use your internal data sources and the platform won’t call out to the internet. (Of course, Kindo also offers a cloud-hosted version for those who prefer SaaS, but on-prem is fully available.)