Nexus AI - AI Document Intelligence Platform
Nexus AI is a powerful SaaS application that revolutionizes how you interact with your documents. Upload your PDFs and have intelligent conversations with them using advanced AI technology. The platform provides secure document storage and leverages cutting-edge AI models to understand context and provide accurate responses to your queries.
The Story
- •Early 2023: I had mountains of study notes in PDFs but ChatGPT (GPT-3.5) couldn't ingest documents—you could only paste text, and context windows were tiny
- •Asked my friend Shadrack: 'How do you even use AI to study if it can't read your notes and give relevant answers?' He had no solution either
- •That question stuck with me. If AI couldn't understand my documents, it was useless for the one thing I needed—studying efficiently
- •Discovered vector embeddings and RAG architecture. Built v1 using OpenAI's API, but costs hit $400/month, I decided to throw away my debit card and not pay that debt
- •Switched to Groq's ultra-fast inference + Pinecone's vector search. Dropped costs to ˜$20/month while keeping accuracy
- •Real breakthrough: hybrid search (Dense Vector + Sparse Keyword) eliminated hallucinations. Users trust precise answers, not confident guesses
- •Nexus AI became the tool I needed to exist—study notes that talk back with accurate, context-aware responses
Video Demo
Key Features
- Running 500+ documents monthly through the system with responses in under 1.5 seconds—fast enough that users don't think twice about asking follow-up questions
- Getting document discovery down to 8 seconds instead of 30+ when searching traditional PDFs—that's 75% faster, and it compounds when you're doing this a dozen times daily
- Paystack handles our subscriptions with 98.5% payment success rate, so we're not chasing refunds or losing revenue to failed transactions
- Dropped infrastructure costs from $400/month to about $20/month by optimizing the Groq + Pinecone combo—that's real margin improvement
Challenges & Solutions
The Challenge
The real challenge wasn't the RAG architecture itself—it was the hallucination problem. When you're querying documents, users expect precision. I had to implement a hybrid search algorithm combining dense vector embeddings with sparse keyword matching, then re-rank results to eliminate noise. The system needed to understand both semantic meaning and exact phrase matching simultaneously, which sounds simple until you realize context windows can be limited and every millisecond counts.
What I Learned
- •Vectors are smart, but incomplete—pure semantic search misses exact phrases. That's why hybrid search with keyword matching is the real answer
- •LLM API costs will destroy your margins unless you optimize hard. Groq + Pinecone was the breakthrough that made this sustainable
- •Speed is trust. Anything under 2 seconds feels instant. Over 3 seconds and users start thinking the system is broken
- •Billing is harder than it looks. Refunds, failed transactions, chargebacks—you need robust error handling or you lose money and credibility
- •Users know what they need better than I do. Early feedback kept mentioning 'Can I use this on mobile?' I didn't think mobile mattered for document analysis—nobody was signing up. Made it fully responsive. All my friends signed up finally, and suddenly I was getting real feedback from actual users. Learned: my assumptions about user behavior are usually wrong. Ship what users ask for, not what I think they need
Impact & Growth
- •Proved there's real demand for this—people will pay for document search that actually works
- •The cost optimization became our biggest differentiator. Most competitors get buried by API bills; we stayed profitable
- •Built a reusable RAG pipeline that's now the foundation for every AI project I touch
- •Learned how to ship an MVP and iterate. Started bare-bones, listened to users, kept only what mattered