AI infrastructure for complex real-world systems.
The proposed first government engagement is a bounded evidence sprint for reviewing traffic-safety risks, evidence, and next actions.
The proposed engagement would focus on one government decision and one high-risk site or bounded site set, producing an inspectable evidence package before any live test.
Name the decision owner, review audience, site or bounded site set, and accepted output.
Use available observations, current workflow records, and mature substitutes before adding simulation.
Explain the event, risk, limitations, and recommended next action from the same evidence base.
Measure whether reviewers accepted and used the package, plus delivery time, cost, and rework.
See the simulator concept. The proposed delivery model starts with a bounded, decision-first evidence sprint.
This product demonstration shows how simulation can explore beyond-line-of-sight interactions. Simulation enters a government evidence package only when it adds measurable decision value.
The proposed delivery model follows a gated evidence loop: sense conditions, simulate only when it adds evidence, review with decision owners, and learn from accepted use.
We start with traffic safety.
Our first proposed application would give government traffic teams one place to review a high-risk site, compare scenarios, and understand limitations.
Over time, the same evidence approach may inform other complex physical systems. These are future directions, not current products or deployments.
AI infrastructure for complex real-world systems.
Apkallu starts with traffic safety, then extends the same evidence loop to broader physical-world systems.
Apkallu is a member of the NVIDIA Inception program.
Co-Founder / CSO
Brings geospatial, satellite-data, and engineering experience to simulation and sensing work.
CCOO
Supports operations and business development for AI-powered infrastructure pilots.