Multi-agent AI-support facilitating highly customized order completion.

The challenge

The challenges of making AI a printing expert

It is typical for people to get overwhelmed when options are too abundant. The same applies to artificial intelligence, which may struggle if it has to pull options from an excessive variety of components to choose from. Such are the challenges of applying LLMs to specific business needs.

The AI agent for Mixam had to be engineered to create order specifications that would be validated when taking orders in. With challenges like this, engineering expertise is key to blending the indeterministic nature of language models with the rule-based backbone of the final delivered solution. This goes against the common perception that the deployment of AI agents is straightforward — it almost never is in a business context, and it requires an art of engineering and expert knowledge to make it work as a reliable part of any business solution.

This also applies to securing the solution so that it is not thrown off and begins talking garbage, protecting it from a drop in reliability and from increasing hallucinations. Guardrails forbid the agent from picking up topics that are not directly related to printing orders. As a result, the agent refuses to give restaurant recommendations or offer cupcake recipes. When it acts as a self-publishing advisor, it focuses solely on helping the end-user make the right selection of Mixam options so the final print meets the customer's needs.

How it works

From chit-chat to expert advice

The challenge of creating a true agentic AI printing expert required overcoming typical issues related to large language models, such as hallucinations. Specifically, to prevent the bot from playing the guessing game and instead offer what is actually printable requires the narrowing down of its options to concrete product specifications. Vstorm achieved this by having the AI agent use Mixam product options, pulling them from Mixam's publishing systems with API calls.

This grounded the solution in choosing existing combinations of cover, page, and binder options. However, having a simple AI bot use the components of Mixam products and build an order from them was not enough — the solution needed to know how to recommend the right combinations of those components to potential customers. This was accomplished by supplying the agent with a Mixam knowledge base that combines both best practices and the most common choices that users have made and proven happy with.

The Vstorm team built the solution using the PydanticAI Python-centered framework, with FastAPI for inter-application processes and a powerful RAG vector store for matching requests with products based on Mixam's always-up-to-date internal knowledge of their product features.

The choice of this environment was made as it is built with safety as a priority and focuses on strict data validation. As a framework relied upon in the healthcare and finance industries, it was deemed adequate for self-publishing.

Results

What changed for Mixam's first-time publishers

Mixam's approach exemplifies the industry shift from "AI projects" to "AI products." The infrastructure includes sophisticated monitoring, centralized prompt management, and performance optimization.

This operational maturity — treating AI as production infrastructure requiring monitoring, version control, and staged deployment — distinguishes systems built for long-term business value from experimental prototypes. The repository structure itself tells the story of incremental evolution: test logs, performance benchmarks, shop-specific validation scripts, and careful Git management all point to a team iterating based on data, not assumptions.