Mission Support in the Most Dangerous Domains

Today’s missions, from disaster response to humanitarian relief, require fast, intelligent coordination across land, sea, air, and space. ForeTL eliminates guesswork by unifying dynamic risk variables into a single command interface, providing go/no go decision making support.

Screenshot of a presentation slide titled 'Real-Time Weather Integration,' explaining it automatically pulls meteorological data along flight trajectories using Windy API for dynamic safety evaluation, with a rain cloud icon in the top left and a green label 'Live Data' in the top right.

Screenshot of a webpage showcasing an integrated API for live operations, with a gear icon in the top left and a 'Connected' status in the top right.

Slide titled 'Modular Risk Modeling' explaining its purpose to calculate likelihood and impact of adverse events by segment, mission phase, and operational role. Top left has a speedometer icon, and top right has a label 'AI-Powered'.

Slide with title 'Custom Thresholds & Profiles' and description about defining and storing risk limits for parachute constraints, sea state tolerances, and vehicle capabilities.

Title slide for Mission Safety Analytics, emphasizing comprehensive safety assessments with automated alerts and mitigation strategy recommendations.

Axiom Mission 4

Operator Solutions Case Study

Map showing weather risk levels with colored squares indicating threat levels along a coastline, with a weather model interface on the right side, including input fields, buttons, and risk layer options.

Operator Solutions transformed their rescue operations for the Ax4 mission by replacing manual, subjective risk assessments with ForeTL’s automated decision-support platform. Managing four distinct vehicle types - fixed wing aircraft, rotary wing aircraft, boats, and paratroopers - each with unique weather requirements and operational constraints, they needed objective risk analysis to avoid inadvertently putting rescue teams in harm’s way during critical mission phases.

A digital dashboard displaying statistics about vehicle support, human bias incidents, and a predictive window showing 4 vehicle types supported, 0 human bias incidents, and a 9+ hour predictive window.

Eliminated subjective “pen and paper” risk calculations with automated, standardized assessments.
Provided objective risk levels for 4 vehicle types: fixed wing, rotary wing, boats, and paratroopers.
Delivered dynamic risk predictions 9+ hours ahead for optimal mission timing decisions.
Automated abort scenario analysis for North Atlantic and Shannon Ireland contingencies.
Real-time risk flagging system prevented high-hazard deployments during storm systems.
Enabled time-critical decisions balancing payload recovery vs. personnel safety windows.

Key Achievements

Watch the real-world application

Mission Timeline

Infographic outlining T-7h fixed wing pre-positioning based on 9-hour storm prediction, and recovery with rotary wing and boat assets deployed with zero red-flag conditions.

Challenge: Manual vs. Automated

Comparison table showing manual assessment methods versus automated ForeTL solution for risk evaluation.

Mission Impact & Lessons Learned

A slide titled 'Decision Support Capabilities' lists features such as risk-benefit analysis, vehicle threshold enforcement, standardized criteria, weather corridor optimization, and contingency planning.

Slide titled 'Transformation Achievements' listing improvements such as eliminating subjective pen and paper risk calculations, providing objective risk scoring for four vehicle types, enabling decision-making with 9+ hour predictive windows, preventing personnel exposure to hazards, and automating red-flag alerts for abort scenarios.

Military Base

Perimeter Protection Case Study

A critical military installation deployed ForeTL platform to orchestrate autonomous perimeter defense using multiple unmanned vehicle types. The system coordinated UUVs, UASs, and ground vehicles in real-time threat detection and neutralization, moving from manual surveillance protocols to AI-driven autonomous response that eliminated human risk while achieving faster threat identification and mitigation.