About the project
MAIstr grew out of a real problem in our family business. The knowledge of experienced people lives in their heads, in notes and in e-mail threads, and when someone leaves, the way things are done leaves with them. MAIstr lets know-how be captured in four ways: upload a document, record it by voice, film a video or simply write it down. The AI processes the input, extracts what matters and stores not only the information itself but also its connections to what is already in the system. That is what makes answers accurate and relevant rather than merely similar to the question. An employee then asks in plain language and gets an answer immediately, at any time. Data security is treated as a priority, because what goes into the system is the most valuable thing a company has. The project is being built under my direction and is in a pilot phase, onboarding its first customers.
Project information
Client
Own product
Platform
Web application
Status
In development
Development time
Ongoing, pilot phase
Year
2026
Technologies used
Check out the stack I used for this project
Frontend
Backend
Database
Key features
Knowledge captured four ways: document, voice, video, text
AI processing of inputs and automatic knowledge base creation
Connections between pieces of information kept, not isolated snippets
Questions in plain language, answers immediately and at any time
Specialised agents for individual areas of the company
An overview of unanswered questions as input for filling gaps
Vector search across the knowledge base
A focus on data security and control over where data goes
Challenges
- 1
Knowledge is scattered across a company and often exists only in people's heads
- 2
Capturing know-how without costing an expert hours of writing
- 3
Similarity search returns fragments stripped of their context
- 4
The most valuable company data goes in, so security cannot be an afterthought
- 5
Combining several models and data stores into one working whole
Solutions
- 1
Four capture paths including voice and video: describing a procedure out loud is enough
- 2
The AI processes, sorts and prepares the input into the knowledge base itself
- 3
Storing connections between pieces of information, so answers keep their context
- 4
Data security designed in as a product priority from the start
- 5
Qdrant for vector search and PostgreSQL for structured data
Results
Know-how stays with the company even after an experienced employee leaves
Onboarding relies on answers instead of digging through documents
Answers keep connections across sources rather than text similarity alone
The product is running in a pilot with its first customers
Development timeline
From analysis to deployment - how the project evolved
Validating the problem
The problem named in the family business, interviews confirming other companies face it too
Knowledge base prototype
AI processing of inputs, designing how connections are stored, vector search in Qdrant
Application and agents
React and Vite interface, Python backend, specialised agents and the training workflow
Pilot operation
Onboarding first customers, feedback from real use and adjusting priorities
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