Smarter data.
Healthier crops.
A more sustainable future.
We combine genetic data comparisons and biostatistics with AI to help agricultural businesses breed better, grow stronger and make data-driven decisions.
Every great harvest begins as data.
Inside every resilient crop is a genome with something to say. Amaltas helps agricultural businesses listen: comparing genetic data, testing it with rigorous statistics and turning it into decisions that lead to stronger crops, more productive farms and a more sustainable food system.
Why this mattersFrom genome to harvest
Genetic data comparisons
Find the traits worth keeping.
See where the valuable variation lives, and breed with confidence instead of guesswork.
Biostatistics services
Trust what the numbers say.
Rigorous analysis turns noisy data into results you can defend.
AI-powered insights
See what's coming.
Predictive models help you choose the right candidates before you plant them.
Agricultural impact
Grow with confidence.
Better decisions mean higher productivity, greater resilience and a stronger food system.
Spend your time on the science, not the paperwork.
Decipher, our AI-assisted platform, compares and analyzes plant genomic data and transforms the results into dossiers that are ready for regulatory submission.
Ready to put your data to work?
Tell us about your crop and your data, and we'll show you what they can do.
Partner with usThe problem we're solving
Companies across agriculture and plant science still compare genomic data sets by hand and build regulatory dossiers document by document. The work is slow, and it ties up skilled people, time and budget that could go toward research and breeding. Amaltas exists to change that. Our AI-driven platform and services compare and analyze plant genomic data for you, and turn the results into dossiers that are ready to submit.
What the evidence says
Published research and regulators' own documents describe the same pattern: heavy data requirements, slow submissions and a growing workload for developers of every size. Most published figures concern genetically modified (GM) crop traits, where the cost and time of regulation are best documented.
51% of the time, 38% of the costFor a new GM trait in a large commodity crop, the regulatory phase takes more than half of the total time (51.1%) and nearly 40% of the cost (37.6%), according to a survey of four of the largest developers. [1]
About US$115 million and 16 yearsThe average cost and time to bring a new GM trait to the global market, as reported by crop developers' scientists and regulators at a 2024 workshop. [2]
In the words of crop R&D scientists
On genetic stability data requirements for vegetative crops
“adds regulatory burden to all developers, including small developers and the public sector”
On the growth of regulatory data requirements for GM crops
“have continued to grow globally”
On characterizing transgene insertions with conventional methods
“conventional PCR-based methods are labor-intensive”
In the words of regulators
Health Canada and the CFIA, on whole genome sequencing submissions
“Given the complexity of WGS data, industry has requested guidance”
The European Food Safety Authority, on why it supports applicants
“harmonise data submission and reduce risk assessment time”
- AgbioInvestor (2022). GM Trait Study: cost and time to discover, develop and authorise a new plant biotechnology-derived trait. https://gm.agbioinvestor.com/gm-trait-study
- Storer N.P., Simmons A., Sottosanto J., Anderson J., et al. (2024). Modernizing and harmonizing regulatory data requirements for genetically modified crops. Frontiers in Bioengineering and Biotechnology 12:1394704. https://doi.org/10.3389/fbioe.2024.1394704
- Koch M., Pence M.G., DeMond J., Rudgers G. (2024). Improving regulatory efficiency for biotechnology products. Frontiers in Bioengineering and Biotechnology 12:1354743. https://doi.org/10.3389/fbioe.2024.1354743
- T-DNA Analyzer: A Long-Read Sequencing Pipeline for Characterizing T-DNA Insertion Sites in Transgenic Crops. PubMed Central PMC13411805. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13411805/
- Health Canada and CFIA (2018). Guidance for Submitting Whole Genome Sequencing (WGS) Data to Support the Pre-Market Assessment of Novel Foods, Novel Feeds and Plants with Novel Traits. https://www.canada.ca/en/health-canada/services/food-nutrition/legislation-guidelines/guidance-documents/guidance-submit-wgs-data.html
- EFSA (2024). Review of risk assessment methodology for RNAi-based GM plants. Ad hoc meeting with GMO applicants, 8 April 2024. https://efsa.europa.eu/sites/default/files/2024-05/6-rnai-update.pdf
What we do
Four ways Amaltas helps agricultural businesses turn data into better crops.
Genetic data comparisons
Identify valuable traits, track diversity and speed up breeding decisions.
We line up genotype and trait data across lines, populations and breeding generations, so you can see exactly where the valuable variation sits. Clear comparisons give breeders a shared, reliable view of their germplasm.
- Trait and allele discovery
- Diversity and relatedness tracking
- Evidence for selection decisions
Biostatistics services
Turn complex data into clear insights with rigorous statistical analysis.
Good decisions need numbers you can defend. Our biostatisticians design analyses suited to your trials and genomic data, check each signal for strength and bias, and report results in plain language that your team, partners and regulators can follow.
- Trial design and analysis
- Testing of genomic signals
- Clear, reproducible reports
AI-powered insights
Machine learning and predictive models for greater accuracy.
Machine learning models learn from your own trials and genomic data to forecast how candidates will perform, flag patterns people would miss and rank options before anything goes in the ground. We validate every model, so predictions can be trusted and explained.
- Predictive trait models
- Pattern discovery at scale
- Validated, explainable results
Agricultural impact
Higher productivity, better resilience and a more sustainable food system.
The goal is better crops and better farms. By shortening the path from data to decision, we help teams develop varieties that yield more, withstand stress and use resources wisely, supporting a more sustainable food system.
- Faster breeding cycles
- Resilient, productive varieties
- Less manual data work
From raw genomes to a breeding decision
Our pipeline takes the data you already collect and returns answers your team can act on this season.
Compare the genetics
We line up genotype and trait data across lines and populations to show where the useful variation is.
Test it statistically
Biostatisticians check every signal for strength and bias, so you know which results to trust.
Predict with AI
Models learn from your trials to forecast performance and rank candidates before you plant them.
Breed and grow
You get clear recommendations, ready for your breeding programme and your growers.
Decipher
Decipher is our AI-assisted platform for comparing and analyzing plant genomic data. It turns the results into dossiers that are ready for regulatory submission, so your team spends less time assembling documents and more time on the science.
Request a DemoBetter harvests start with better questions about the data.
Rooted in science, built for growers
For centuries, South Asian farmers have read the coming season in the golden blossoms of the Amaltas tree (Cassia fistula). Early blooms, tradition holds, foretold a bountiful monsoon; late ones warned of drought. Long before weather instruments, the tree's own biological clock turned humidity and temperature cues into a forecast for the crop cycle.
Amaltas mirrors that foresight. Our AI-assisted engines read complex omics and biological data to forecast the traits and phenotypic potential of your microbial candidates.
By turning intricate bio-data into predictive insight, we cut the trial and error from the R&D pipeline, moving high-performing microbes from discovery to commercialization and cultivating a more resilient, bountiful harvest.
Shib Sankar Basu, PhD
Founder and CEO
Dr. Basu brings more than 20 years of leadership in agriculture and biotechnology R&D. He spent 16 years at Syngenta developing crop traits and helped bring Enogen corn and Quantum Phytase to market, then served as Principal Scientist at Indigo, leading a team of computational biologists. His work has produced more than 20 patented inventions. Amaltas builds on that experience to turn complex genomic data into clear decisions.
Partner with Amaltas
Tell us about your crop, your data and what you want to improve. We'll reply within two business days.