A deep-tech biotech startup
A platform turning airborne diagnostics into disease-risk forecasts up to two weeks before outbreak.
~2 weeks
Earlier outbreak warning
Predictive
Prevention over reaction
Multi-tenant
Cloud backend for global rollout
Traditional farm- and plant-health monitoring relied on result-based lab tests after symptoms emerged. The client needed to shift to a predictive paradigm, detecting pathogens before a visible outbreak.
Combining airborne sampling devices, biotech lab analysis, and machine-learning prediction models demanded careful design of hardware-software workflows and data pipelines.
Clients expect rapid, actionable insights, low-entry-cost models, and measurable ROI through reduced disease losses and improved productivity. The platform had to deliver value quickly and demonstrate business impact.
We architected a system where airborne microbial sampling devices feed data into a cloud platform, where ML algorithms analyze the environmental pressure of infection and forecast outbreak risk up to roughly two weeks ahead of onset.
We built a web dashboard for farm and veterinary clients showing risk scores, trend analysis, and actionable alerts, delivering intuitive insights to decision-makers rather than raw data.
The backend was designed to scale across geographies, ingesting hardware data, lab results, and ML outputs while maintaining secure roles, multi-tenant architecture, and readiness for expansion into new markets.
Clients reported anticipating pathogen outbreaks, such as a virus in poultry, at least two weeks earlier than conventional methods.
By enabling preventive action, the platform supports less downtime, fewer losses in livestock and crops, and improved outcomes for food safety and animal health.
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