SOKRATEQUE.AI
Sokrateque.ai is an AI-powered personal research assistant tailored for Master's and PhD students. It leverages cutting-edge Retrieval-Augmented Generation (RAG) techniques and the OpenAI API to answer users' research-related questions by intelligently utilizing their uploaded research papers, articles, and supplemental data from the web.
Students often struggle to manage and retrieve information from vast amounts of academic material. Sokrateque.ai addresses this challenge by offering an intelligent assistant that understands, extracts, and responds to queries in a research-specific context.
"EdgeFirm has been instrumental in bringing Sokrateque.ai to life. Their team built advanced multi-agent systems, integrated Power BI with LLMs, and delivered a seamless data exploration pipeline that exceeded our expectations. Their deep understanding of AI, automation, and scalable architectures helped us unlock real value from our product. We're incredibly satisfied with their work and highly recommend them."
PERSONALIZED RESEARCH ASSISTANT FOR GRADUATE STUDENTS
SCOPE OF WORK
The Sokrateque.ai project focused on building a highly customized RAG-based system capable of: Ingesting and understanding user-uploaded research documents. Retrieving relevant information dynamically from both a Vector Database and on-the-fly without prior indexing. Seamlessly combining external web knowledge with internal document insights to deliver comprehensive answers.
Key Deliverables:
- Custom RAG architecture for dual-mode operation (Vector DB + Live Extraction)
- Advanced data ingestion pipelines (including OCR for non-digital PDFs)
- Efficient API development for user interaction and backend processing
DEVELOPED PIPELINES
A Text Extraction and OCR Pipeline was implemented to handle a variety of academic and research document formats, including PDFs, Word files, and image-based documents. Optical Character Recognition (OCR) was integrated to ensure that scanned documents and image-based academic papers could be accurately processed and converted into searchable text.
TECHNOLOGY STACK
- Compute: AWS EC2 for backend processing
- Storage: AWS S3 for file uploads and extracted data
- Database: MySQL for metadata, Weaviate Vector DB for semantic search
- LLM: OpenAI API (GPT Models) for response generation
- Backend Stack: FastAPI (Python) for API services
- Additional: Node.js services for supporting utilities and asynchronous tasks
RESULTS
- Reduced Research Time: Students were able to retrieve accurate answers from thousands of pages in seconds.
- Higher Research Efficiency: The dual-mode RAG system ensured that even unindexed documents could be immediately used for queries.
- Wider Accessibility: OCR capabilities allowed students to use older, scanned materials as part of their research base.
- Scalable Architecture: Built on AWS, the system can seamlessly handle increasing user loads and data volumes.
Conclusion
Through a deep integration of RAG methodologies, state-of-the-art text processing pipelines, and OpenAI's language capabilities, Sokrateque.ai sets a new standard for AI-driven research support. It empowers graduate students to spend less time searching — and more time thinking, writing, and innovating.
Industry
Education
Sokrateque.ai was built specifically for the education sector, with a strong focus on assisting graduate-level researchers. Key considerations included: Handling complex academic language and citations. Supporting multiple formats and types of academic papers. Delivering reliable, well-cited, and plagiarism-free AI responses.
Headquarters
Amsterdam, Netherlands
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