This use case was realized with:
In 2024, Iltis partnered with Codesphere to implement and deploy a speech recognition and knowledge retrieval use case based on the latest open source AI technologies.
The main achievements of the project
“Together with Codesphere, we developed a working PoC in just 6 weeks, which allowed us to test and gather feedback incredibly quickly.”
Alexander Ott
CEO @ ILTIS
In under 6 weeks, the team managed to develop a fully functional, scalable AI voice transcription tools with 4 different services:
Real time speech recognition
Utilizing OpenAI’s Whisper models for real time voice transcription.
ERP integration
Fully integrated into ILTIS knowledge system with automated updates.
Semantic search
Using the transcribed information to search through a Vector DB.
Operator cockpit UI
UI for ILTIS employees to seamlessly interact with the application.
6
Weeks
>2000+
Documents embedded
100%
GDPR compliant
“Great product, amazing team behind, superb support at any time! We love Codesphere!”
Alexander Woelke
Co-Founder & Co-CEO @ SaaS Titans
via Product Hunt
The application creates numerical representations of the transcribed data and stores them in a vector database.
Records voice, handles interaction with services.
Transcribes audio sequences into text.
Creates numerical representations of spoken input.
Database for storing numerical input
All services were built with only open source technologies, easy and seamlessly connected through Codesphere.
The frontend runs a basic Sveltekit application, taking care of recording the microphone input and sending it as a .wav to the transcription server.
A Whisper.cpp server running on a Codesphere Pro plan, enabling real time CPU inference for speech to text. No GPU needed, keeping the cost down
A FastAPI server running the sentence-transformer library to create the embeddings needed to store and retrieve ILTIS data from the database
A PostgreSQL database with the pgvector extension which extends the stored information with vector embeddings and implements a rapid vector search, perfect for RAG use cases
Node.js server that pulls data from the ERP, parses all PDFs, creates embeddings and checks and fills the .
Sterling PDF endpoint that takes in legacy .doc files and converts them into PDF files that can be parsed by the main Node server.
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