AI Research Assistant With Citation-Verified Answers
How We Built an AI Research Assistant That Processes 50,000+ Academic Papers
Client
Sokrateque.ai
Headquarters
Amsterdam, Netherlands
Timeline
4 months
Technology
RAG SYSTEM
Time to find 20 relevant papers cut from 8 hours to 45 minutes (client-reported)
50,000+
academic papers indexed
Citation-verified responses
Domain-tuned embeddings and query routing
The challenge
Graduate students spending many hours each week on literature review, much of it on papers that turn out to be irrelevant
The solution
4-layer RAG architecture with domain-specific embeddings, citation-aware chunking, and intelligent query routing
The results
- Time to find 20 relevant papers cut from 8 hours to 45 minutes (client-reported)
- 50,000+ academic papers indexed
- Citation-verified responses
- Domain-tuned embeddings and query routing
AI Research Assistant for Academic Papers
Sokrateque.ai is an AI research assistant for Master's and PhD students who spend long hours working through academic literature. It searches 50,000+ papers and answers with citations the student can check. The platform uses a 4-layer RAG architecture to help researchers discover, analyze, and synthesize academic knowledge.
The project started with a simple question. Why do graduate students spend so much of their research time on papers that turn out to be irrelevant? The answer was a research assistant built around the way academics search and cite.
6 capabilities carried the build
Design and deploy a production-ready RAG system optimized for academic research, capable of processing 50,000+ papers with citation-aware responses and sub-2-second query latency.
01
4-layer RAG architecture with domain-specific optimizations
02
Academic document processing pipeline (PDF, LaTeX, DOCX)
03
Fine-tuned embedding model on 2M+ academic papers
04
Citation extraction and verification system
05
Production API with <2 second response time
06
Next.js frontend with research-focused UX
The stack behind it
GPT-4, Pinecone, LangChain, Next.js, FastAPI
OpenAI
LangChain
AWS
PineconeLLM & Embeddings
GPT-4 for generation, fine-tuned Sentence-BERT for embeddings
Vector Database
Pinecone with namespace partitioning by discipline
Orchestration
LangChain for RAG pipeline, custom query router
Backend
FastAPI (Python) with Celery for async processing
Frontend
Next.js 14 with streaming responses
Infrastructure
AWS (EC2, S3, ElastiCache), CloudFlare CDN
Monitoring
LangSmith for LLM observability, Datadog for infrastructure
Our development process
Academic Document Processing
We built a specialized ingestion pipeline that treats academic papers as structured documents. The system extracts sections (abstract, intro, methodology, results, discussion), preserves figure and table references, parses LaTeX equations, and extracts all citations with their contexts. This structured representation enables more precise retrieval.
Embedding Fine-Tuning
Generic embeddings struggle with academic terminology. We fine-tuned a sentence transformer on 2M academic papers using contrastive learning. Papers that cite each other are positive pairs, random papers are negative. This improved retrieval quality for domain-specific queries.
“Before Sokrateque, I spent hours every week just trying to find relevant papers. Now I get better results in a fraction of the time. EdgeFirm built us a research partner that understands academic nuance. The citation-aware responses alone have saved me from countless rabbit holes.”
Dr. Sarah Chen
What changed after launch
Faster Literature Discovery
Average time to find 20 relevant papers dropped from 8 hours to 45 minutes, client-reported from user time tracking.
Citation-Verified Answers
Every response links to the source papers it draws on, so researchers can check each claim.
50,000+ Papers Indexed
The platform indexes 50,000+ academic papers across disciplines and returns responses in under 2 seconds.
Domain-Tuned Retrieval
Fine-tuned embeddings and citation-aware chunking help the system match academic terminology.
Sokrateque.ai shows that production RAG systems need deep domain understanding as well as technical skill. We invested in academic-specific document processing, domain-tuned embeddings, and citation-aware generation to build a research assistant that researchers trust and use daily. The key insight: in specialized domains, the gap between a working demo and a production system is large. Closing that gap takes close attention to the details that domain experts care about.
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