# AI Platform FAQ

You need an active Base LLM API subscription before purchasing the Chat Dedicated plan. Once subscribed, access depends on your plan: the free Evaluation plan or the Chat Dedicated plan with a dedicated single-tenant environment. If your company already has a paid subscription, your administrator adds you as a user and shares your access URL (for example, chat.your-company.ch). Paid plans use enterprise SSO, you sign in through your company identity provider and never type a password directly into PHOENIQS Chat.

No. Your data is never used to train the underlying models, and model vendors never see your data. Session history is kept locally, and you retain full ownership and control of your data. This applies across both Evaluation and Chat Dedicated plans.

The Evaluation plan is free and lets you try PHOENIQS Chat on Swiss-hosted infrastructure with RAG, MCP integrations, web search, and 1 million tokens per month, with no Base LLM API subscription required. The Chat Dedicated plan (CHF 260/month excl. VAT) adds a dedicated single-tenant instance per company, the full catalogue of air-gapped Swiss-hosted models, multimodal capabilities (image interpretation, transcription, speech-to-text), Microsoft 365 and Atlassian integrations, the Agent Builder for reusable assistants, custom branding, enterprise SSO (OpenID Connect), 10 GB of RAG storage, and a Basic SLA. Chat Dedicated does not include AI credits: model usage is billed through your Base LLM API key, and additional credits can be purchased at any time from the PHOENIQS Portal. See Subscriptions for details.

A regular conversation is a one-shot exchange: you ask, the model answers, and context from that session does not carry forward unless you provide it again. A custom AI assistant (agent) is a reusable, no-code configuration you build once in the Agent Builder, with a name, instructions, model, knowledge files, and connected tools. The assistant remembers its role and sources every time you open it, so repeatable tasks like policy Q&A, document review, or training programme design do not require re-explaining context or re-attaching files. You can share assistants and conversations with colleagues, and connect them to company tools such as Microsoft 365 and Atlassian via MCP.

The most common causes include:

• Excessive context passed through MCP (Model Context Protocol)
• Tool-calling loops, where each tool result is injected back into the prompt
• Long conversation history that is continuously appended without truncation
• Large prompts or system instructions
• Token estimation mismatches due to tokenizer differences or fields added late in the request pipeline

When combined, these factors can quickly exceed the model's context window.

A 400 Bad Request error related to tokens typically occurs when the total estimated input tokens exceed the model's context window.

Phoeniqs' AI gateway (LiteLLM) performs a protective pre-check before sending the request to the model. If the request would exceed the model's allowed context window, LiteLLM rejects the request early to avoid unnecessary compute usage. This situation can result in zero or negative tokens remaining for the model's response, which triggers the error.