Overdue update: The launch of MindLattice
An overview of MindLattice, a platform addressing data engineering challenges in AI transformation and serving underserved industries.
MindLattice — Bridging the gap between data engineering and AI for businesses.
So much has happened in the first few months of the year that I have found myself back to my startup roots working all the daylight hours in order to get things moving on my personal projects and tie up loose ends from the past few years.
This is a short update on the launch of my new business MindLattice.
MindLattice emerged from a year of developing an AI agent framework focused on AI analytics for organisations.
It is much more than a standard “Text-to-SQL” AI framework and instead focuses more on the Data Engineering challenges that are a missing part of the AI transformation instead.
The pace of AI evolution
It’s incredible
The speed of advancement in the AI industry is unlike anything I’ve seen. Even the blockchain boom felt slow by comparison. Code I wrote just a month ago can become outdated, and documentation changes faster than search engines can index it. AI models often have knowledge cut-off dates, which means we need new approaches just to keep up with the latest information. In my 20+ year development career, I’ve never experienced such rapid evolution.
To manage this breakneck pace, we designed MindLattice as a modular system. This architecture lets us address data engineering needs separately from the AI (MLOps/AIOps) components.
What does MindLattice do?
In short, we decouple the heavy data-lifting from the machine learning operations.
Our platform acts as an engine between your data sources (APIs, databases, files, etc.) and the results you need (dashboards, chat interfaces, custom reports, or guided knowledge bases).
By deploying MindLattice, any product that we can access data from can be enhanced with AI-driven intelligence and efficiency. In other words, if we can read a product’s data, we can make that product smarter.
We believe that every product can, (and should), become an intelligent product
However, the rapid churn of new AI tools can become a hindrance. It often forces teams to rewrite or swap out parts of their tech stack as new solutions emerge that solve problems more efficiently. For example, the LangChain library updates so frequently that its documentation quickly becomes outdated, making it hard to follow.
We aim to solve this by adding smart abstraction layers. For instance, we use LiteLLM to balance multiple language models, and we treat all data sources uniformly as databases. Our core platform is kept simple and custom-built. We only incorporate external frameworks like LangChain or LangGraph when absolutely necessary (e.g., for memory management or connecting systems).
How is MindLattice different?
Our focus on less-served industries sets us apart. Many AI companies chase the most popular “shiny” use cases, but that leaves a lot of sectors behind. Countless organisations still rely on outdated, legacy software and feel left out of the AI boom because integrating modern AI is impractical or too time-consuming for them.
As a team of Data Engineers who have regularly solved these problems for large organisations it is a clear gap in the current market offerings that we already have first mover advantage in.
Next steps:
Although the company is new, the tech has been in progress for over a year, it has evolved several times since its inception already but has always been focused on the verticals we have targeted since the start.
We’ll continue to share updates as we progress, and we’re excited about the road ahead. Our mission is to bring AI value to companies that have been underserved by the tech revolution. If you’re interested in MindLattice or have any questions, please reach out or follow our updates. We’d love to connect and explore how we can make your data work smarter.
