Internal AI assistant and business chatbot: reliable answers, controlled cost (LLMOps)
IASWITCH takes internal AI assistants and business chatbots to production: they answer from your documents, cite their sources and cost what they should. Behind it: LLMOps expertise (RAG, continuous evaluation, guardrails, cost control) that delivered up to 40% savings on AI infrastructure.
In brief
- An assistant that answers from your documents and cites its sources
- Quality measured continuously: ungrounded answers get caught
- Cost per answer tracked and optimised (up to –40% AI infrastructure)
- Data protected: on-premises, EU hosting or a contracted API
What is an internal AI assistant for?
Your staff spend a lot of time looking for a procedure, a contract clause, a product sheet or the right person to ask. An internal AI assistant answers in seconds from your documents and shows where the information comes from.
On the customer side, the same approach powers a business chatbot that answers common questions at any hour and hands over to a human when needed.
Why so many chatbots never get past the demo
A ChatGPT demo takes an afternoon. A reliable tool takes real method: answers must be correct, data protected, costs predictable and quality monitored over time. That is what LLMOps (Large Language Model Operations) is about.
How we take an AI assistant to production
We use a RAG (Retrieval-Augmented Generation) architecture: the assistant first retrieves the relevant passages from your documents, then writes the answer citing them.
- Continuous evaluation on a set of business questions, to measure quality before and after every change
- Guardrails: filtering, answer validation, refusing when the information doesn't exist
- Multi-model routing: a small, cheap model by default, a more powerful one only when needed
- Observability: cost per request, response time, rate of ungrounded answers
- Reproducible deployment (vLLM, Kubernetes, Infrastructure as Code) or via API, depending on your needs
What you get
An assistant used every day, with tracked quality and cost, documented and handed over to your team. On an inference platform, this approach cut AI infrastructure costs by 40%.
Frequently asked questions
What is LLMOps?
LLMOps (Large Language Model Operations) is the set of practices for deploying, monitoring and evolving language models in production: RAG, quality evaluation, guardrails, security, cost and latency control.
How do you stop a business chatbot from making things up?
By forcing it to rely on your documents (RAG), citing sources, adding guardrails that make it decline when information is missing, and continuously measuring quality against a set of business questions.
How do you reduce the cost of an AI assistant?
Through multi-model routing (small model by default, large model when needed), caching, quantization and automatic capacity scaling. This approach cut AI infrastructure costs by 40% on one engagement.
Do our documents stay confidential?
Yes. Depending on your requirements, the model can run on your premises, in a European cloud or through an API provider committed not to reuse your data. Document access can follow each user's permissions.
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