Licensing & citation
Cite the method, freely
Growlrr publishes its methodology openly — the BARC framework, the Hepatic Buffering Model, the Stochastic Reliability Framework and the validation results — so writers, vets and researchers can read it, quote it and build on it. This page explains what is licensed for reuse, what stays proprietary, and how to cite us.
The license
Growlrr's published methodology writings are released under Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
In plain English: share it freely, with attribution — no commercial use, and no derivatives or adaptations. You may quote and redistribute the writings as published, for non-commercial purposes, as long as you credit Growlrr and don't alter or remix them.
✅ Shareable & citable
- The models and the framework boundaries
- The validation method and the study results
- The founder story and the guaranteed analysis
🔒 Proprietary — NOT licensed
- The exact sachet formulation / %w/w composition (the BOM)
- The balancing-engine internals
Trade secrets. All rights reserved. Nothing on this page grants any right to these.
How to cite us
Please keep the model names intact — BARC, the Hepatic Buffering Model, the Stochastic Reliability Framework — and link to the relevant growlrr.com page. A clean citation looks like this:
Muralidharan, P. (2026). The BARC Framework: Biologically Appropriate, Residually Corrected Fresh-Food Diets for Cats and Dogs. Growlrr Foods Pvt Ltd. growlrr.com/pet-home-cooked-recipes-blog/the-barc-protocol
For the other papers, swap the title and the page — for example growlrr.com/pet-home-cooked-recipes-blog/hepatic-buffering-model or growlrr.com/pet-home-cooked-recipes-blog/nutritional-reliability-framework.
Attribution to copy & paste
“Source: Growlrr Foods Pvt Ltd (Prasanna Muralidharan), growlrr.com. Published under CC BY-NC-ND 4.0 — shared with attribution; no commercial use or derivatives.”
Read the methodology
The published papers behind this license — the framework, the toxicokinetic safety model, and the stochastic validation gate — are all in the deep-dives.