Building Virtual Assistants with LLMs

#product #oxlo #ai

Virtual assistants have moved beyond simple chatbots. Modern implementations combine multi-turn reasoning, tool execution, vision understanding, and voice I/O into a single coherent agent. Building one requires an inference backend that supports long conversational contexts, function calling, and multimodal pipelines without forcing you to stitch together multiple providers. Oxlo.ai provides a unified platform with 45+ open-source and proprietary models across 7 categories, fully OpenAI SDK compatible, and flat per-request pricing that removes the cost uncertainty of long system prompts and accumulated chat history.

Architecture of a Modern Virtual Assistant

A production virtual assistant typically needs four things: a reasoning engine, working memory, tool interfaces, and multimodal I/O. The reasoning engine handles user intent and planning. Working memory retains conversation state and retrieved knowledge. Tool interfaces let the assistant act on the world through APIs. Multimodal I/O supports text, images, and audio in the same session.

Oxlo.ai covers each layer through a single API. The chat/completions endpoint hosts general-purpose and reasoning models such as Llama 3.3 70B, DeepSeek R1 671B MoE, and Kimi K2.6. The embeddings endpoint supplies BGE-Large and E5-Large for retrieval-augmented memory. Vision tasks can route to Gemma 3 27B or Kimi VL A3B. For voice, Whisper Large v3 handles audio/transcriptions and Kokoro 82M handles audio/speech. This means you can keep the entire assistant stack on one provider instead of fragmenting infrastructure.

Model Selection for Assistant Workloads

Not every assistant task needs the same model. A fast classification or intent router can use a lightweight option, while deep reasoning or complex coding benefits from a larger specialist.

On Oxlo.ai, the selection includes: