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LEAP turns scientific literature, clinical data, and omics datasets into decisions your R&D team can actually make. From ingredient discovery to claims substantiation, in hours instead of months.
Manual literature review takes months. LEAP turns scientific literature into structured, interconnected knowledge — so your team can move from "what does the evidence say?" to "we have a formulation" in a single working session.
Ingredient effects, interactions, safety, mechanism — synthesized, sourced, and ready to defend.
Ask LEAP what the evidence says about an ingredient, a health target, or a mechanism of action. It returns structured summaries grounded in primary literature, with every claim traceable to its sources. Use it to build a health claim, screen candidate ingredients, brief a stakeholder, or respond to a regulator — with citations that hold up under scrutiny.
"PIPA's LEAP platform is revolutionizing the way food and beverage businesses do nutrition science."
— Marketing & Sustainability Director, ingredient manufacturer
Connect literature to molecules. Predict activity before a single assay runs.
LEAP's knowledge graph connects millions of compounds to their biological activities, food matrices, and health outcomes. Teams use it to discover novel bioactives in under-studied ingredients, identify compounds with evidence-backed synergies, and narrow a thousand candidates down to the ten worth testing. Peptides, polyphenols, MFGM, colostrum — LEAP finds what the literature already knows but nobody has connected.
"We were able to identify bioactive peptides and polyphenols in our upcycled plant protein, linked with positive health effects, in just a few short months."
— Ingredient manufacturer, upcycled protein
Generative AI grounded in LEAP's knowledge graph and primary literature.
LEAPChat combines large language models with LEAP's proprietary scientific knowledge graph — so answers are generative, but sources are specific. Ask for a mechanism, request a comparison between two ingredients, brief yourself on a regulatory topic, or draft a claims argument. Every statement links back to the paper it came from.
LEAPChat powers Mars's PHI research assistant — giving Mars Associates instant Generative AI summaries on thousands of ingredient and health topics, backed by primary literature.
— Deployed inside Mars Advanced Research Institute (MARI)
Structured evidence for EFSA, FDA, and global health-claim frameworks.
Every health claim needs an evidence package — primary studies, mechanism, dose-response, safety. LEAP builds that package for you: surfacing relevant literature, organizing it by claim architecture, flagging evidence gaps, and producing regulatory-ready dossiers. Whether you're filing an EFSA Article 13, an FDA structure/function claim, or responding to a dietary supplement label challenge, LEAP gives you the argument in a form regulators recognize.
EFSA-structured health claim dossiers shipping today — used across nutraceutical and functional-food product launches.
— Functional food, nutraceutical, and supplement teams
Paragraph-level semantic search across 30+ million articles.
LEAP indexes scientific literature at the paragraph level, not the paper level. Ask a specific question and LEAP surfaces the specific passages that answer it — not just a list of relevant papers. Automated literature reviews across up to 100 articles. Topic discovery across 500-paper sets. Emerging research cluster identification. The work that used to take a scientist two weeks now takes an afternoon.
"Excellent teamwork with PIPA to articulate and prioritize our project goals prior to leveraging LEAP, PIPA's proprietary AI platform, to provide a focused and deep understanding of our target design space."
— General Manager, health technology partner
LEAP is built on PIPA's hybrid-model architecture — combining AI, chemistry, and omics data into a single interconnected map of ingredients, compounds, mechanisms, and health outcomes.
Millions of ingredients, compounds, and health entities connected through decades of scientific literature. The graph is the source of truth; every LEAP output traces back to it.
Generative AI that cites its sources. LEAP's LLMs are constrained by the knowledge graph, so answers are conversational but defensible.
Molecular structures, metabolomic pathways, and multi-omics datasets integrated into the graph — so LEAP reasons at the mechanism level, not just the citation level.
LEAP is in active use across pharma-adjacent food, premium nutraceutical, and global CPG research teams.