2026-11-06 Attachment B - Technical Information Request.xlsx

Instructions

FOR OFFICIAL USE ONLY

DFR

FOR OFFICIAL USE ONLY

Vendor Name System Name

# RFI Questions Meets Threshold Yes/No Meets Objective

Threshold: 30 minutes Objective: 60 minutes

Threshold: 1 mile Objective: 5 miles 6 Launch: The system SHALL launch autonomously by a defined trigger or manual user command. Describe the software mechanisms, communication protocols, and interface integrations that enable the system to launch autonomously in response to defined automated triggers (such as external sensor alerts, integrated fixed tower/remote video surveillance system alarms, or application programming interface [API] inputs) as well as direct manual user commands issued from the remote operations center or ground control station.

Threshold: 5 minutes Objective: 3 minutes 7 Data: The system SHALL securely store, manage, and provide remote access to captured photo, video, and mission data, and support automated transfer of evidentiary files to designated user repositories. Describe the data storage architecture (including edge storage capacity within the dock/aircraft, on-premises servers, or cloud storage), data retention policies, secure access protocols, encryption standards (at rest and in transit), and the specific mechanisms used to autonomously transfer and route gathered media to user-designated locations for evidentiary review.

Threshold: System shall be capable of storing photo and video that can be accessed remotely.

Objective: System shall autonomously send all gathered media and data to a user designated location for evidentiary review.

8 Detection and Avoidance, Obstacle Avoidance: Does the SUAS incorporate both a detect and avoid (DAA) capability to identify, track, and mitigate mid-air collision risks with other cooperative and non-cooperative aircraft, and an obstacle avoidance capability to detect and avoid ground-based obstacles (such as buildings, power lines, towers, trees, and other static/dynamic hazards) during all phases of flight operations? Describe the onboard sensor suite utilized for both DAA and ground obstacle avoidance (e.g., electro-optical/infrared cameras, radar, LiDAR, acoustic sensors, or Automatic Dependent Surveillance–Broadcast (ADS-B In)), processing hardware, collision and obstacle avoidance algorithms, response latency, compliance with relevant Federal Aviation Administration or industry standards (such as Radio Technical Commision for Aeronautics/American Society for Testing and Materials (RTCA/ASTM DAA standards)), and flight-tested performance data demonstrating autonomous evasion of both aircraft and ground-based structures without human command.

Objective (Full Autonomy): Fully autonomous, real-time DAA and ground obstacle avoidance that independently detects airborne and ground hazards, calculates conflict-resolution and clearance trajectories, and dynamically executes evasive maneuvers (such as automated course or altitude deviations) without human intervention, subsequently resuming the mission profile once the airspace/path is clear).

9 Autonomy: Does the SUAS and associated ground control/docking infrastructure incorporate robust autonomous flight capabilities enabling end-to-end mission execution with minimal to zero human intervention? Describe the software architecture, flight control algorithms, onboard processing hardware, and sensor suites that drive the platform's autonomy. Detail the specific degrees of automation supported across each phase of flight (launch, en-route navigation, target tracking, obstacle avoidance, landing, and recharging), specify any safety limits or geofencing fail safes, and describe the mechanisms available for human-in-the-loop oversight or manual override.

Definition of Autonomy: Autonomy refers to the system's ability to independently make operational decisions and execute flight phases—including pre-flight checks, launch, dynamic navigation, target tracking, obstacle/collision avoidance, return-to-base, landing, and battery recharging—using onboard processing, automated software routines, and sensor feedback without requiring continuous, manual remote-pilot input.

Objective: Complete end-to-end autonomous execution where the system responds to external alerts, launches, navigates, performs intelligent target acquisition and tracking, executes collision avoidance, lands, recharges, and prepares for subsequent tasks entirely without human intervention.

10 Super Intelligence: Does the SUAS, associated ground software, and remote operations platform leverage advanced artificial intelligence and machine learning (referred to as "super intelligence" or advanced AI/SI) to autonomously detect, identify, and classify items of interest (IoI) in real-time during live flight operations across varying environmental conditions? Describe the software algorithms, neural network models, edge processing hardware, and training datasets utilized to achieve advanced AI/SI detection and classification. Detail the system's true positive and false alarm rates across cluttered terrain, day versus nighttime operations, and low-bandwidth network conditions. Specify how target coordinates and classification metadata are packaged and transmitted to command and control platforms.

Definition of items of interest: For the purposes of CBP land surveillance operations, IoIs are defined as 1) dismounted persons or groups engaged in or suspected of illegal border crossings or unlawful entry activities (aliens); 2) land conveyances (such as vehicles, all-terrain vehicles, or motorcycles) and other tactical transport modes operating in remote or unauthorized border sectors; or 3) contraband packages, dropped loads, caches, or unattended suspicious equipment associated with cross-border illicit trafficking.

Objective (Advanced AI/SI): State-of-the-art artificial intelligence and machine learning architectures operating at the edge and/or cloud that autonomously and simultaneously detect, accurately identify, classify, and continuously track multiple distinct IoIs across day, night (thermal/infrared), and low-light environments. The system must dynamically filter background clutter, calculate precise geospatial coordinates for each IoI, and auto-cue cameras or transmit high-priority alerts without requiring manual video monitoring or operator input.

11 Operational Information Relay: The Drone as a First Responder (DFR) system SHALL securely and rapidly relay actionable operational information (including live high-definition video, precise target coordinates, tracking telemetry, and imagery packages) to CBP tactical personnel, mobile command centers, and law enforcement partner systems to support rapid operational interdiction and resolution. Describe the data distribution architecture, supported streaming protocols (e.g., secure reliable transport [SRT], web real-time communication [WebRTC], real-time streaming protocol [RTSP]); integration capabilities with mobile command platforms, Computer-Aided Dispatch (CAD)/Geographic Information System (GIS), and Team Awareness Kit (TAK); and the end-to-end latency metrics achieved when relaying operational information to remote law enforcement end-users in the field.

Threshold: Secure real-time video streaming and static image/coordinate transmission to fixed ground control stations or authorized tactical workstations to support operational awareness and ground unit vectoring.

Objective: Seamless, automated, low-latency dissemination of high-definition video feeds, real-time geospatial tracking metadata (e.g., MGRS/GPS coordinates), and intelligence packages directly to tactical agents' mobile devices, CAD/GIS platforms, and the TAK ecosystem to optimize on-the-ground law enforcement interdiction and resolution.

12 System Physical Footprint, Weight, Portability, Modular Deployment: Describe the complete system's physical footprint, weight, portability, and modular deployment capabilities, including both the SUAS and its automated docking infrastructure. Provide the exact dimensions of the SUAS in folded/stowed and fully deployed/operational configurations, dimensions of footprint of the automated docking system, total operational weight of the SUAS to include batteries and standard payload, and total weight of the docking system and any associated equipment required for operation, foundation, mounting, anchoring, or site-preparation requirements for the docking system.

Additionally, provide information on available ruggedized cases, packaging, or protective transport containers. Describe transportability features, including whether components can be moved by one or two personnel, transported in standard vehicles, or shipped through commercial or government logistics channels. Describe modular design features that support rapid setup, breakdown, relocation, and redeployment across operational locations, as well as procedures, personnel requirements, equipment needs, and estimated time required to deploy, configure, secure, test, and redeploy the system in field environments.

Objective: ≤12 ounces

Threshold: 100 yards Objective: 200 yards

Threshold: 1/4 mile Objective: 1/2 mile

Threshold: The solution architecture must be containerized or otherwise compatible with deployment and hosting within the CBP Cloud environment, holding a minimum of a FedRAMP Moderate/High authorization (or equivalent security certification) to ensure data sovereignty, compliance, and security oversight.

Objective: The solution is fully cloud-native, deployable directly within the CBP Enterprise Cloud (such as CBP AWS or Azure government tenants), supporting automated provisioning, native cloud logging, and alignment with CBP zero-trust architecture principles.

Note: Operational availability can be defined simply as uptime/(uptime + downtime) 54 What is the demonstrated or estimated mean time between critical failure (MTBCF) for the docking station and the SUAS?

Note: Critical failure in this instance is a defined as a failure which can not be resolved by the end user and would require original equipment manufacturer (OEM) depot-level maintenance.