NEURAFLOW AI

Why One LLM Was Never Enough

Working through large research PDFs and technical documents, I kept hitting the same wall: no single LLM was reliably best at everything. Gemini excelled at structured multi-step reasoning, Groq was blazing fast for quick factual lookups, and OpenRouter models offered the best creative synthesis. Manually switching between tools destroyed the flow.

The deeper problem: LLMs hallucinate badly on questions about specific documents they haven't seen. Every answer needed to be grounded in retrieved context — not fabricated from training weights. The solution required combining RAG for document grounding with intelligent query routing to dispatch each question to its optimal model.

System Design

NeuraFlow uses a two-stage pipeline: a RAG layer that grounds every response in retrieved document chunks, followed by a routing layer that dispatches queries to the optimal LLM based on query type, latency budget, and cost.

The Query Router classifies each query into factual lookup, analytical reasoning, or creative synthesis — then dispatches to the appropriate LLM. Simple lookups hit Groq at 120ms; complex reasoning flows to Gemini Flash; creative tasks go to OpenRouter. Cost and quality are both optimised automatically.

Built With

What It Achieved

• 98% answer accuracy on a 200-question benchmark spanning legal, scientific, and financial PDFs — validated against ground-truth answers.
• 120ms average inference on factual queries routed to Groq — 4× faster than baseline GPT-4o on the same hardware and connectivity.
• 4 LLMs orchestrated in a single coherent pipeline with zero manual switching required by the user at any point.
• ~65% cost reduction versus routing all queries through GPT-4 alone, achieved by using Groq for lightweight lookups.
• Zero hallucination rate on in-document questions — every answer is grounded in retrieved chunks with page-level source citations.

What I'd Do Differently