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Custom AI-assistent implementation
Utilising the Dataslab AI Platform's pre-designed tools significantly reduces the time required to develop fully customisable AI assistant logic with complete oversight.
Full control
hallucination-free responses
х3
faster implementation
The AI platform enables the development of AI assistants for different roles, aligned with internal policies and designed to avoid “hallucinations”.
Yuliia Mohylevska
Product owner
Objective

To establish a prepared environment for custom development built on Dataslab AI Platform tools.

This is particularly relevant when a client has a specific search architecture, custom chunking algorithms (text segmentation), or complex AI agents in mind and wants to construct the core of their system independently, using AI Platform services as modular building blocks.

Rersult

We provide a toolset for building custom solutions by the client's own team:

 

  1. Custom logic: The client's developers write their own code (for example, using LangChain, LlamaIndex, and OpenWebUI).
  2. Vectorisation and Inference (Ollama + vLLM): To convert text into vectors, the client's application calls Ollama. For generating the final response to the operator, requests are routed to vLLM.
  3. Unified gateway (LiteLLM): LiteLLM handles routing, load balancing, and enforces strict token usage limits across different technical accounts and microservices.
  4. Full tracking (MLFlow): Since all logic runs on the client's side, developers integrate MLFlow for end-to-end tracing of their GenAI application. They can inspect every step in detail — what context was retrieved from the database, how long each request took, and how accurately vLLM generated the response.
  5. Interface (OpenWebUI): To quickly provide access for operators, developers connect their backend to OpenWebUI (APP #4), gaining a ready-made corporate chat interface without having to build a frontend from scratch.

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