Scaling Personalized Health Reports with AI-Powered Automation
From Hours of Manual Work to Instant Lab Report Generation
We built an AI-powered lab reporting engine that transforms raw biomarker data into structured health insights with personalized supplement and IV recommendations.
The Challenge
Providers were manually analyzing labs and writing long reports for each client, a process that was inconsistent and unsustainable. Reports took 2–3 hours each, limiting patient volume and frustrating clients waiting on results.
Our client, a multi-location wellness and IV therapy provider, needed a scalable way to deliver consistent, personalized lab reports. The inconsistency also made it difficult to align recommendations with the brand's supplement line, and reliance on clinician "copy/paste" slowed output and increased risk of errors.
Manual Lab Report Process
Our Approach
We reviewed the client's intake process, lab panel data formats, and current manual reports to identify patterns and opportunities for standardization. Our methodology designed a structured scoring system by health category (Metabolic, Hormones, Inflammation, Sleep, etc.), then layered AI-driven narrative generation on top.
Key Discovery Findings
- •Biomarker interpretation was repeatable across clients, but clinicians were doing redundant work
- •The missing piece was a reliable pipeline to structure data → insights → narrative
- •No centralized system for biomarker scoring, narrative generation, or automated recommendations
- •Providers were burning out creating reports while clients waited weeks for results
The Solution
We built a backend pipeline that ingests structured lab data, scores biomarkers into categories, and generates branded narrative reports with supplement/IV product recommendations. The solution included a data pipeline to process intake + lab results, AI model prompts for each health category with category-specific scoring logic, and a report template with branded supplement and IV therapy recommendations.
Technical implementation used Supabase for database + auth, FastAPI backend for pipeline orchestration, OpenAI LLMs for narrative generation with custom category prompts, and structured CSV/JSON export for downstream use. The approach was iterative development with category-by-category testing (Metabolic → Inflammation → Hormones, etc.).
AI-Powered Lab Report Pipeline
Implementation & Results
The project was completed over 12 weeks: data modeling + schema definition (weeks 1-2), AI category scoring + narrative generation build (weeks 3-6), report template + branding (weeks 7-9), and testing + rollout to clinical team (weeks 10-12).
We provided training to providers on how to review/edit AI reports and built in a human-in-the-loop review step for clinical oversight. The results exceeded expectations: reports now delivered same-day, improving patient trust and freeing providers to focus on client interaction instead of paperwork.
Measurable Outcomes
- •Report generation time reduced from 2–3 hours → 5 minutes
- •Standardized scoring system across all patients
- •Increased provider capacity (2–3x more patients per day)
- •Consistent recommendations aligned with brand supplements + IV menu