RNDAI builds ML systems for deep-tech startups across energytech, greentech, agritech, foodtech, biotech and advanced materials — where the domain matters as much as the code.
Most deep-tech R&D teams sit on years of experimental data and do almost nothing with it.
Not because they don't want to. Because the people who understand the science can't build production ML systems — and the people who can build ML systems don't understand the science.
That gap is exactly where RNDAI works.
Most teams in these fields already have the chemistry and the data. What tends to be missing is the layer in between — someone who can read a lab notebook, build the model, and then run it as a service.
Five service lines, all built on the same principle: chemistry-native logic, production-grade engineering.
Ingestion, cleaning, and versioning of experimental data from scattered instruments, notebooks, and spreadsheets — turned into one queryable source of truth.
ML models that predict material properties from composition, structure, or process parameters — every prediction shipped with uncertainty quantification.
Systems that name the next experiments worth running — turning 1,000 possible candidates into the 10 that actually matter for your next iteration.
Web platforms that quantify the financial case of your technology for each prospect — used live in sales calls, exported as branded reports for their CFO.
Every model shipped as a versioned API with drift monitoring and audit trails — not a Jupyter notebook that only runs on one machine.
Every calculation, every model, every dataset is grounded in the actual science of your process. No black boxes, no keyword-fitting — the logic reads the way your R&D team already reasons.
Versioning, audit trails, drift monitoring, reproducibility. Built to the discipline of a live product — Ranbval's parent platform runs to the same standard — not the discipline of a research script.
Whatever we build — model, pipeline, or tool — comes with a surface your non-technical team can actually use. If it needs a manual, it isn't finished.
RNDAI is the Deep-tech & AI division of Ranbval — a product of TariqDreamsTech.
Ranbval's flagship platform runs in the security & automation space at ranbval.com. RNDAI extends that same production-grade engineering discipline — versioning, audit trails, deployment hygiene — into R&D and AI for deep-tech startups.
Led by Hussnain Tariq — an AI/ML engineer with an MSc in Chemistry, working at the intersection of science and shipped software for years. Chemistry-trained, but the same principles carry across the sciences — energytech, greentech, agritech, foodtech, biotech and advanced materials are already the areas we work in.
If you're a deep-tech startup drowning in R&D data but struggling to extract value from it — let's talk.