Back to Case Study
Nowy testowy case study
Mixam is a self-publishing company that primarily provides printing and fulfillment services for independent authors, publishers, and creators on a global scale. They specialize in high-quality print production, including books, magazines, and other printed materials.
Mixam’s services are designed to make it easier for individuals and small publishers to produce and distribute their works without the need for large-scale traditional publishing houses.
Industry
Printing
Headquarters
United Kingdom
Company size
51-200 worldwide employees
Mixam was established in 2007 in the United Kingdom but operates on a global scale, expanding its services to meet the needs of the global market. One of the key aspects of the expansion is the usage of AI in accordance with the user-friendliness of their self-publishing platform.
Vstorm’s impact, the TL;DR:
- 10,000k users now use Mixam’s custom tailored AI agent each day, processing 100k custom orders per month
- Improved customer conversion to final sale from ~20% to ~40% overall
- Implemented specially tailored three agent system to guide 70% of new customers
- Agents access 15 distinct tools to act as fully informed Mixam product consultants
- Validation processes and constrained generation eliminate system hallucinations
- Agents maintain the flexibility of natural language interactions while ensuring outputs remain 100% accurate to Mixam’s offer
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 in such a way that it’s not thrown off and begins talking garbage, protecting it from the reduction of its reliability and from increasing hallucinations. Setting up what are called ‘guardrails’ forbids 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 in the role of a self-publishing advisor, it focuses solely on helping the end-user make the right selection of Mixam options to have the final print meet the customer’s needs. And it does that with excellent results!
Vstorm designed and implemented an AI agent designed to help Mixam’s customers navigate the company’s complex printing offers, smoothing the customer experience in navigating complex publication processes.
Cooperation with Vstorm began when Mixam had already begun using AI elements and AI-based platforms in various operations and services. However, the company’s ambitious goals required reaping the full potential of AI in increasingly demanding and complex processes.
The initial Vstorm project was centered around creating a satisfying experience for new users who were just starting their self-publishing journey and beginning to explore the range of options available to them. From book format to paper thickness and structure, it’s easy for any non-publishing professional to get lost in the variety of choices that need to be made before their first publication materializes in the desired form. That is why Mixam sought a way to improve the experience with AI agents, aiming to support user ordering choices to meet their desired result. Enter AI conversational agent you need to get your first order just the way you want it without having to be an expert in printing processes.
Mixam realized that 70% of new users need help in choosing their way through a plethora of options that newcomers might not know anything about. The gutter, the bleed, the paperweight, and its coating are not things that book authors generally think about. Nor should they. Yet when self-publishing their first book, all of them arise as questions that need real answers in order to print hard results that meet expectations.
Historically, these were the types of decisions that either required users to educate themselves or to reach out for help by contacting Mixam’s support professionals. Now, with the help of Agentic AI, they can let the system work out the details by simply stating the intended purpose of the publication and answering a few prompting questions that the chatbot asks for clarity.
An expressed desire to have a cookbook in a typical format results in the AI agent deducing the most appropriate order specification for users to onboard or change to best suit their desired outcome. Similarly, users can ask for the most common format or sets of options for non-fiction or self-published novels, as well as a myriad of other use cases.
The difference between a plain-vanilla chatbot and Mixam’s Agent is in the knowledge that is available for agentic AI solutions to suggest options and make necessary alterations. While generic bots draw their replies from internet sources, Mixam’s AI Agent works by using exact product specifications from a catalogue to create orders that can be fulfilled by Mixam in the user’s location, taking into consideration any differences in units and product offerings across Europe, the Americas, and other locations.
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’s actually printable requires the narrowing down of its options to concrete product specifications. Vstorm achieved this by having 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; our solution needed to know how to recommend the right combinations of those components to potential customers. This was accomplished by supplying our agent with a Mixam knowledge base that combines both best practices and the most common choices that users have made and have 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.
The broader picture: 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—exactly the approach Lucian advocates in the narrative.
Meet directly with our founders and PhD AI engineers. We will demonstrate real implementations from 30+ agentic projects and show you the practical steps to integrate them into your specific workflows—no hypotheticals, just proven approaches
The LLM Book
The LLM Book explores the world of Artificial Intelligence and Large Language Models, examining their capabilities, technology, and adaptation.
Read it now
