Real-Time, Automated, Predictive Biosurveillance Infrastructure

HELGEN builds biosurveillance infrastructure to detect biological threats in the environment on-site, completely automated.

Project Details

Current biological threat detection relies on outdated workflow: laborious, slow, and reactive, making them ineffective for real-time detection cases.

HELGEN builds biosurveillance infrastructure to detect biological threats in the environment on-site, completely automated. We help operators detect, predict, and devise countermeasure for outbreaks real-time, remotely.

We combine automated, on-site molecular diagnostics hardware with a cloud-native software platform, enabling continuous environmental monitoring without human intervention. We build AI models from collected data to do real-time threat detection, outbreak prediction, and countermeasure suggestions.

We are collaborating with Professorships in TUM (expert in building sensors and automated diagnostics) and LMU (expert in infectious disease diagnostics e.g. COVID-19, tuberculosis, Ebolavirus).

With this funding, we aim to produce our first prototype and eventually working MVP (automated hardware + software to track environmental biological sample).

Theory of Impact

Biosecurity is one of humanity's biggest threat. Geopolitical instability, synthetic bio x AI, and climate changes exacerbated the chance of the next biological outbreak happening. Worst case, this could be the catalyst of human extinction.

The rise of AI exacerbate the risk and severity of potential deliberate bioterrorism, by lowering the cost and effort of designing one. To combat this, there has to be multiple infrastructure to detect outbreak early and design cure.

Our infrastructure helps on the former one, providing first line of warning when there is potential biological outbreak. We help shift detection from reactive (seeing people getting sick / animals dying) to being proactive (detecting before first symptoms occur), ultimately preventing the AI-fueled pandemic / bioterrorism from occuring in the first place.

People

Dominique Gian Leonardo

Team Member

Funding Details

Start Date: Aug 1, 2026
End Date: -
Expected Duration: 12 months
Funding Raised to Date: -
Annual Budget: -
Monthly Burn Rate: -
Current Runway: -
Funding Goal: -
Funding Stage: -
Fiscal Sponsor: -

Grants Received– no grants recorded

Add grant

Funding Asks

grantmaking.ai Launch Round

Applied

Minimum

$39,000

Ideal

$50,000

What this ask is

Current biological threat detection relies on outdated workflow: laborious, slow, and reactive, making them ineffective for real-time detection cases.

We are collaborating with Professorships in TUM (expert in building sensors and automated diagnostics) and LMU (expert in infectious disease diagnostics e.g. COVID-19, tuberculosis, Ebolavirus).

With this funding, we aim to produce our first prototype and eventually working MVP (automated hardware + software to track environmental biological sample).

How the money will be spent

min: 6 months

25k: validating lab methodology as a baseline for prototype, parts to build initial prototype

9k: wage

5k: operationals (hardware, software, AI tokens, travel, ect.)

ideal: 12 months

28.5k: validating lab methodology as a baseline for prototype, parts to build initial prototype

18k: wage

5.5k: operationals (hardware, software, AI tokens, travel, ect.)

Discussion

No comments yet. Be the first to share your thoughts.