What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What does on-premise AI agent deployment mean?

On-premise AI agent deployment means the agent software runs on your own servers — either in your physical data center or on private cloud infrastructure you control. Your data never leaves your environment. This is distinct from SaaS, where agents run on the vendor's servers, and from public cloud, where agents run on AWS/GCP/Azure infrastructure.

Who needs on-premise AI agents?

Organizations with regulatory requirements (HIPAA, GDPR strict interpretation, FINRA, ITAR), intellectual property concerns (proprietary research, trade secrets), contractual obligations (attorney-client privilege, NDA requirements), and organizations in sensitive government or defense contexts. Also: any organization where a data breach would cause catastrophic reputational damage.

Is on-premise AI more expensive than cloud?

On-premise AI agents have higher upfront infrastructure costs but lower long-term per-use costs. At moderate to high usage volumes, on-premise typically achieves lower total cost of ownership than SaaS within 12–18 months. The crossover point is usually 50+ active users with daily usage.

Can on-premise AI agents use the latest models?

Yes. On-premise deployments can use hosted API models (OpenAI, Anthropic) through HTTPS calls — only the prompt/response data leaves the network, not your underlying data. Alternatively, fully air-gapped deployments use self-hosted open-source models (Llama 3.3, Mistral, Qwen) with zero external network calls.

On-Premise AI Agents: Why Data-Sensitive Companies Are Leaving the Cloud

Why data-sensitive companies choose on-premise AI agents over cloud SaaS. HIPAA, GDPR, and data sovereignty explained. Learn what it takes to deploy AI agents on your own infrastructure.

Frequently Asked Questions

What does on-premise AI agent deployment mean?: On-premise AI agent deployment means the agent software runs on your own servers — either in your physical data center or on private cloud infrastructure you control. Your data never leaves your environment. This is distinct from SaaS, where agents run on the vendor's servers, and from public cloud, where agents run on AWS/GCP/Azure infrastructure.
Who needs on-premise AI agents?: Organizations with regulatory requirements (HIPAA, GDPR strict interpretation, FINRA, ITAR), intellectual property concerns (proprietary research, trade secrets), contractual obligations (attorney-client privilege, NDA requirements), and organizations in sensitive government or defense contexts. Also: any organization where a data breach would cause catastrophic reputational damage.
Is on-premise AI more expensive than cloud?: On-premise AI agents have higher upfront infrastructure costs but lower long-term per-use costs. At moderate to high usage volumes, on-premise typically achieves lower total cost of ownership than SaaS within 12–18 months. The crossover point is usually 50+ active users with daily usage.
Can on-premise AI agents use the latest models?: Yes. On-premise deployments can use hosted API models (OpenAI, Anthropic) through HTTPS calls — only the prompt/response data leaves the network, not your underlying data. Alternatively, fully air-gapped deployments use self-hosted open-source models (Llama 3.3, Mistral, Qwen) with zero external network calls.