Digestiva
Amplifye: an intelligent AI research engine for biotech supplement leader
Summary
AI-powered nutrition. An intelligent research engine that translates complex biotech science into clear, personalized insights for consumers.
Digestiva's patented P24 enzyme amplifies the absorption of key protein nutrients, delivering a range of health benefits. But biotechnology is not always easy to understand for every consumer. To build trust and support adoption, we developed Amplifye, an AI-powered tool that translates scientific research into clear, personalized insights. Drawing from peer-reviewed articles, the system extracts, summarizes, and cites health data based on user input, while providing direct links to original sources for transparency and credibility.
Services
Industry
The Challenge
The science is proven, but the communication gap between biotech research and consumer understanding is vast.
Digestiva's patented P24 enzyme amplifies nutrient absorption, but biotechnology is hard for consumers to understand. With 380 possible combinations of protein type and amino acid impact, the science behind the product is inherently complex. Consumers are skeptical of supplement claims, every health claim must be evidence-based, and the AI must never hallucinate or fabricate citations.
Problem 01
Complex science, simple communication
- ▪Patented enzyme biotechnology is difficult for consumers to grasp
- ▪380 possible combinations of protein type and amino acid impact
- ▪Health claims must be evidence-based and verifiable
- ▪Tone must be factual without being clinical or inaccessible
Problem 02
Trust and credibility at scale
- ▪Consumers are skeptical of supplement health claims
- ▪Every claim needs direct links to peer-reviewed sources
- ▪The LLM must never hallucinate or fabricate citations
- ▪Content must be personalized to the user's specific interests
The Solution
We built an AI agent grounded entirely in curated, peer-reviewed science. The LLM retrieves information exclusively from a pre-ingested database of 90+ scientific articles, preventing hallucination and ensuring every claim is backed by evidence with direct links to the original publications.
01
Extract and curate
Scientific articles are ingested into a curated database. The system parses, structures, and indexes the content, creating a rich knowledge base of protein research, health claims, and amino acid absorption data from 90+ peer-reviewed sources.
02
Understand and match
When a user asks a question, the engine uses vector similarity search and protein-specific querying to find the most relevant scientific evidence, matching the user's interest with the right research across 380 combinations.
03
Personalize and cite
The LLM generates a clear, personalized summary of the health benefits based on matched evidence, with JSON schema validation ensuring consistent structure and direct links to source materials for full transparency.
Impact
A functional AI research engine launched as part of Digestiva's consumer product launch in the USA, translating complex science into trustworthy, personalized insights.
Evidence-Based Claims
Every health claim is directly linked to peer-reviewed scientific publications, building credibility and trust with consumers in a skeptical supplement market.
Personalized Responses
380 unique combinations of protein type and amino acid impact mean every consumer gets a response tailored to their specific health interests and needs.
Scalable Knowledge Base
New scientific articles can be ingested and indexed continuously, keeping the system current as research evolves and the product line expands.
Methods
A retrieval-augmented AI architecture, from scientific literature curation through semantic search to personalized, evidence-based consumer insights.
RAG architecture
Vector database
Semantic search
LLM orchestration
JSON schema validation
Hallucination prevention
Scientific literature curation
Custom protein query logic
Content personalization
Evidence-based filtering