In the previous blog post we have talked about basic principles of building AI assistants. Let’s take them for a spin with a product case that we’ve worked on: using AI to support enterprise sales pipelines.

So you are building an AI assistant for the business?

This is a popular topic in the companies these days. Everybody seems to be doing that. While running AI Research in the last months, I have discovered that many companies in the USA and Europe are building some sort of AI assistant these days, mostly around enterprise workflow automation and knowledge bases.

There are common patterns in how such projects work most of the time. So let me tell you a story...

This article discusses five questions you should ask when hiring an AI. These questions are inconvenient for providers of AI products, but they are necessary to ensure that you are getting the best product for your needs. The article also discusses the importance of testing the AI system on your own data to see how it performs.

Beginning with the September Trustbit LLM Benchmarks, we are now giving particular focus to a range of enterprise workloads. These encompass the kinds of tasks associated with Large Language Models that are frequently encountered in the context of large-scale business digitalization.

In this first part, we explain the basics of machine learning pipelines and showcase what they could look like in simple form. Learn about the differences between software development and machine learning as well as which common problems you can tackle with them.

As the logistics industry becomes increasingly complex, businesses need innovative solutions to manage the challenges of supply chain management, trucking, and delivery. With competitors investing in cutting-edge research and development, it is vital for companies to stay ahead of the curve and embrace the latest technologies to remain competitive. That is why we introduce the Trustbit Logistics Simulator Framework, a revolutionary tool for creating a digital twin of your logistics operation.

For a long time, there was a consensus that Kafka and Event Sourcing are not compatible with each other. So it might look like there is no way of working with Event Sourcing. But there is if certain requirements are met.

To address common questions concerning the integration of Large Language Models, we have created an LLM Product Leaderboard that focuses on building and shipping products.

This blog discusses the rapid advancements in large language models, particularly highlighting the impact of OpenAI's GPT models.

DSML projects can be quite different from the software projects: a lot of R&D in a rapidly evolving landscape, working with data, distributions and probabilities instead of code. However, there is one thing in common: iterative development process matters a lot.

Process pipeline is a system to break down complex repeatable work into digestable steps that lead to concrete results.Processes, unlike projects, don't have a defined completion time and goal. Their goal is to keep running and moving forward at a steady pace.

I hate distractions. They can easily ruin my day when I'm in the middle of working on a cool project. They do that by overloading my mind, buzzing around inside me, and just making me tired. Even though we can think about several things at once, we can only do one thing at a time.

Black Friday is coming, so let’s talk about warehouse management and event sourcing. When developing a system for event retrieval with aggregates, several very different concepts are possible. If you think of an aggregate as a transaction boundary, then each decision has its own implications.The aggregate can also be a lifecycle boundary - events in a global uniform stream can often only be discarded by the aggregate stem.

Let's talk about celebrating our successes. Our active memory can be like a cache of recently used data. Fresh ideas and frustrations supersede older ones. The older the thought, the fuzzier it becomes. Details are lost.
There is also negativity bias. When good and bad things happen at the same intensity, the bad ones have a greater effect on us.

So far in the series we have built a trivial logistics simulation runtime. At this point it is only capable of finding the fastest route between two locations. This is implemented as a form of A* algorithm that uses predefined travel times.
Let’s extend the implementation and demonstrate how we can “plug” different models into the simulation runtime.

In the previous article (Part 1: Trustbit Logistics Hackathon - Behind the Scenes) we talked about organizing our Logistics Hackathon on Sustainability - how it came to be.
Let’s get to the technical details now.

A look behind the scenes of our Hackathon on Sustainable Logistic Simulation in May 2022. This was a hybrid event, running on-site in Vienna and remotely. Participants from 12 countries developed smart agents to control cargo delivery truck fleets in a simulated Europe.

One of the questions most asked by potential employees in their initial engagement with Trustbit is about 20% time for learning and sharing.

In 2020 we started an Innovation Incubator. It is a learning and sharing process that tries to connect people from the different teams together. This is especially important for a remote company with people working all across Europe. This is the retrospective of Round 1 of the incubator.

Trustbit has a policy that we could spend 10-20% of our weekly time on learning new things. We are starting a new experimental process, called "Innovation Incubator".