PROJECT 02 / AEGISGRID

Spot the threat before it gets close.

We built AegisGrid as a synthetic drone-swarm simulation. It groups nearby threats, scores their risk and helps explain which ones need attention first.

SYNTHETIC SWARM / SCENARIO 04

THREAT GROUPING / RISK REVIEW

C-01LOW
C-02MEDIUM
C-03HIGH

C-01

Low4 points

C-02

Medium3 points

C-03

High5 points

AFTER-ACTION

Nearby points are grouped first, then ordered by risk for review.

This visual was created for the portfolio to show the project idea clearly.

01 / CONTEXT

What we were trying to solve

A large group of incoming objects can be difficult to review one at a time. The project explores how nearby threats can be grouped and prioritized before presenting the result clearly.

Everything shown here stays inside a synthetic simulation.

02 / TEAM

Team project

We combined clustering, risk scoring, resource allocation and a visual interface during the hackathon.

An optional AI layer helped explain the result in plain language, but it sat outside the core decision loop.

03 / SYSTEM

How it comes together

The decision path stays separate from the explanation shown after it.

  1. 01Synthetic incoming objects
  2. 02Movement and distance information
  3. 03DBSCAN-style grouping
  4. 04Cluster risk
  5. 05Priority ranking
  6. 06Response and allocation context
  7. 07Optional explanation layer
  8. 08After-action summary

04 / SIMULATION VIEW

From moving points to a review order.

The supporting views show allocation context and a quieter after-action readout without turning the simulation into a game interface.

PRIORITY / ALLOCATION CONTEXT

SYNTHETIC SCENARIO

REVIEW ORDER

01C-03 / HIGH RISK
  1. 01C-03REVIEW FIRST
  2. 02C-02NEXT
  3. 03C-01MONITOR

ALLOCATION CONTEXT

The interface keeps priority and response context adjacent without turning the simulation into a targeting display.
Cluster risk creates a review order before explanation is added.

SITUATION / AFTER-ACTION SUMMARY

ANALYTICAL REVIEW

01GROUP

Nearby synthetic objects form clusters.

02RANK

Cluster distance and risk shape priority.

03EXPLAIN

An optional layer describes the result afterward.

Nearby points are grouped first, then ordered by risk for review.
Explanation follows the decision logic instead of replacing it.

05 / REFLECTION

What I learned

A fast team build still needs a clear line between what makes a decision and what explains it.
  1. 01

    Clustering output becomes more useful when the interface gives it a readable order.

  2. 02

    Technical logic and explanation should remain separate.

  3. 03

    A serious visual tone can communicate urgency without becoming sensational.

WHERE IT STANDS NOW

A hackathon simulation and working project concept.

The case study focuses on the grouping, priority and explanation flow created during the event.

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