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The best way to understand how hybrid models work is to see them solving your team's actual problems.
Try FIOS®
Technology
How PIPA builds systems that predict, not just describe.
Our platforms are built on hybrid models, combining AI, physics, chemistry, and omics into a single architecture. Plus a growing library of digital twins for the food processes that matter most.
AI, physics, and chemistry working together.
Most AI-for-food platforms are pure machine learning — they can only predict what they've seen before. Most simulation platforms are pure physics — they can't learn from data. PIPA combines four modeling approaches into a single prediction. That's why our systems work where either alone falls short.
01
AI, ML & knowledge graphs
Pattern recognition across large-scale scientific, formulation, and production datasets. Knowledge graphs connect compounds to mechanisms and outcomes, while machine-learning models identify patterns across formulation and process data.
02
CFD & multiphysics
First-principles simulation of food processes — heat transfer, mass transfer, phase change, rheology, and chemical reaction, all solved simultaneously. This is where off-the-shelf CAE tools fail and where we shine.
03
Chemical, kinetic & omics
Reaction kinetics, degradation pathways, ingredient interactions. Plus omics-level data, metabolomics, proteomics, transcriptomics. The chemistry a formulation will actually do, modeled alongside the data on how ingredients behave in the body.
04
Production data calibration
Where production data is available, relevant models can be calibrated and refined against real manufacturing runs. This helps improve predictions for the specific processes and operating conditions being modeled, while customer data remains within the customer’s controlled environment.
Defensible by design.
Patents & publications
A decade of peer-reviewed science.
PIPA authors have contributed 50+ peer-reviewed publications in leading food and health journals, alongside issued patent coverage on our core AI methodology. Every platform capability traces back to work that has passed scientific peer review.
Academic collaborations
Connected to the frontier of food-systems research.
Active research collaborations with leading academic institutions including the AI Institute for Next Generation Food Systems (AIFS), the Innovation Institute for Food and Health (IIFH), and the University of California, Davis. Fundamental research informs our platforms; commercial deployments inform research priorities.
Proprietary code & process
Ten years of codebase and domain expertise.
A mature, production-hardened code base powering FIOS® and LEAP™, built on proprietary solutions across AI/ML, knowledge graphs, multiphysics, and chemoinformatics. Combined with deep domain expertise across food science, nutrition, and bioinformatics.
Why this matters
Predictions you can take to the plant floor.
Pure ML models tell you what is likely, but not why. They also struggle with novel formulations. Pure physics models explain why, but they are slow, brittle, and require exact material properties that teams rarely have.
Hybrid models bring both approaches together: the speed and breadth of ML, grounded in the physical and chemical reality that physics provides.
The practical result is that FIOS® can predict how a product may behave at scale before committing to a physical run, and explain the factors driving that prediction. LEAP™ can surface novel bioactive candidates and map the evidence connecting compounds, mechanisms, and health outcomes.
Purpose-built simulations for the processes that matter most.
Off-the-shelf CAE tools do not handle food processes well because there is too much coupled physics, chemistry, and phase change. So we build digital twins from first principles, calibrate them with real production data, and expand the library as customers need new processes modeled.
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Digital Extruder
Predicts texture, expansion, and density. Optimizes screw configuration and thermal zones before committing to a run.
A subset of the coupled physics: heat transfer between material and barrel, Navier-Stokes flow through screw channels and die, viscosity and shear resolved in a virtual environment and continuously refined through multivariate process monitoring.
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Digital Oven
Maps moisture migration and Maillard browning. Ensures uniform structure at scale across oven configurations.
Coupled multiphysics simulation: mass transfer, heat transfer, and thermo-mechanical equilibrium solved simultaneously across the process domain, enabling predictive insight into temperature and moisture distribution.
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Digital Fermentor
Models biomass growth and metabolite production. Optimizes yield and batch consistency across fermentation conditions.
Dynamic simulation of fermentation kinetics: substrate, biomass, product, and byproduct profiles resolved over time and continuously refined through real process data.
In development
Digital Dryer
Heat and mass transfer for spray, drum, and belt drying.
Multiphysics treatment of moisture removal across drying modalities, with particular focus on shelf-stable food and supplement powder applications. Contact us to discuss early-access pilots.
The library grows as customers and equipment partners need new processes modeled.
Our digital twins are not off-the-shelf modules. They are built around the relevant process physics and chemistry, then calibrated and validated against customer equipment and production data as they move toward deployment. We also partner with equipment OEMs on co-developed and co-branded twins for their installed base.
Built for enterprise R&D environments.
PIPA platforms operate today inside some of the most demanding enterprise environments in food and nutrition, clearing the procurement, security, and integration bars that come with global CPG deployments.
SaaS or on-premise deployment
Hosted in PIPA's secure cloud, or deployed inside your own infrastructure for the strictest data-residency and IP-isolation requirements. Customer choice, not our preference.
Role-based access & SSO
Permission controls at the project, workspace, and dataset level. Integrates with enterprise identity providers for single sign-on and centralized user management.
Connect your data & tools
Connects to your existing tools and internal material databases. Ingests proprietary recipes, TDS, and production logs in the formats your teams already use.
Versioning & audit trails
Project data, analyses, and outputs retain version history and provenance, making it easy to compare iterations, track changes, and trace results back to their source.
Data handling & IP
Customer proprietary data stays isolated. Models can be trained on customer data at customer choice, with full control over what's shared and what stays private.
Global deployment experience
Deployed across global CPG enterprises operating through their procurement processes, security reviews, and multi-region requirements.
Compliance & data handling
GDPR-aligned data handling practices. Data Processing Agreements available for customers operating under EU or California privacy regimes. Additional compliance documentation provided as part of enterprise procurement review.
Customer audit rights & SLAs
Service Level Agreements tailored to enterprise deployment scope. Audit rights, uptime commitments, and support response times negotiated as part of the Master Services Agreement.