BioBuddy.

The problem

People who take their health seriously already track it obsessively — Oura, WHOOP, Levels CGM, InsideTracker blood panels — yet the data lives in incompatible silos. A biohacker who wakes up flat spends twenty minutes hopping between four apps and a lab PDF, builds a rough hypothesis, and has no way to validate it or track whether the pattern repeats.

Blood tests stay trapped in PDFs with no longitudinal tracking. Mood and journal entries never connect to objective biomarkers. Every existing tool forces you to look at one source at a time, and the whole process is reactive — you only investigate after you already feel the impact.

The solution

BioBuddy is a personal health intelligence platform that unifies blood test reports, Apple Health exports, and nutrition logs into one queryable knowledge base. It answers cross-source questions no single-metric tool can — "Did my HRV drop in weeks my ferritin was low?" or "How does my sleep look on high-calorie days?" — grounded in the user's own numbers with cited sources.

The product combines a RAG-powered chat interface for deep reactive Q&A, a real-time health dashboard, and a proactive pattern-detection engine that surfaces concerning trends before the user notices. Insights are calibrated against both clinical reference ranges and the individual's personal baseline.

How it works

Three prompts govern the system: a clinical extraction prompt that pulls numeric biomarkers from any lab PDF into strict JSON, a pattern-detection prompt that only fires on 3+ consecutive data points, and a Q&A prompt that answers from the user's data first and never makes diagnoses.

Under the hood, pdfplumber handles layout-aware table extraction, biomarkers are chunked per-marker, and text is embedded with nomic-embed-text via Ollama. Retrieval is hybrid — an LLM classifier routes each query to structured SQL, vector similarity search, or both. Inference runs on Groq's llama-3.1-8b-instant (128k context, sub-2s latency), with a FastAPI backend and Supabase PostgreSQL plus pgvector for storage.

Who it's for

BioBuddy is a B2C product for the biohacking and quantified-self community — people aged 25–45 who already spend on self-tracking hardware and lab work and have proven willingness to pay for health insights.

The most revenue-generating segments are biohackers who order their own blood panels and want to correlate everything, and fitness-focused optimizers tracking recovery, macros, and performance. A third segment, health-anxious individuals managing diagnoses like thyroid or iron issues, wants to understand trends without a doctor visit. The common thread is a motivation to understand how inputs — nutrition, sleep, training — affect outputs like energy and biomarkers.

Why it matters

The biohacking and quantified-self market was valued at $24.81B in 2024 and is projected to reach $149.6B by 2029 — a ~32% CAGR — while AI in healthcare grows at roughly 48% CAGR. Wearable adoption, mainstream direct-to-consumer lab testing, and post-pandemic health awareness have made multi-source pattern detection feasible at the consumer level for the first time.

The launch plan is a closed beta with recruited biohackers to validate Q&A quality and alert precision before opening paid tiers. AI targets are exacting — extraction accuracy above 95%, hallucination rate under 1% — and HIPAA and PIPEDA compliance are prerequisites before commercial launch.

At a glance

Project

BioBuddy

Built by

Rodrigo Tello and Ansar

One-liner

BioBuddy is an AI-powered personal health intelligence platform that unifies blood test reports, Apple Health exports, and nutrition logs into one queryable knowledge base.