9 Mothers

Competitive Intelligence & Product Roadmap

AI point-defense turret for close-range drone threats.

Company Overview

9 Mothers is a defense hardware startup that builds EDDA, an AI-enabled counter-drone turret using passive acoustic cueing and visual fine tracking. Serving defense buyers protecting soldiers, vehicles, and bases; no public customers named.

Latest Intel

Zeitgeist tracks private signals to determine where the company is heading strategically.

May 18, 2026

Deployment Bottleneck

Confidence:

Medium

New Intel: 9 Mothers is building depth across acoustic ML, vision, sensor fusion, and embedded control. The key bottleneck is field data, not demand: each real test can improve EDDA, but weak false-positive control would stall defense adoption.

New Intel: 9 Mothers is centering EDDA on passive acoustic cueing plus visual fine tracking. That narrows the C-UAS fight to close-range vehicle and base defense, where radar-heavy systems may be too costly or power-hungry.

What They're Building

The company's public product roadmap & what they're committed to building.

EDDA Counter-Drone Turret

EDDA is the company’s first product, a low-power point-defense turret for close-range Group 1 drone threats.

Passive Acoustic Cueing

The system uses acoustic tracking to detect and roughly locate drones without relying on active radar.

Visual Fine Tracking

EDDA pairs acoustic cueing with vision models for close-range target tracking before engagement.

Vehicle, Base, And Carried Deployment

The product is described for vehicles, fixed bases, and carried defense use cases.

Founder and Key Execs

Russell Smith

Founder and CEO (former Rainforest QA founder and CTO)

Roman Khomenko

Founder (CTO at 9 Mothers, Kaggle competition background)

Bogdan Pyzh

Co-Founder and COO (9 Mothers operating leader)

Founder Force Multiplier

Russell Smith brings prior technical-founder experience from Rainforest QA; Roman Khomenko adds competition-grade ML depth. That pairing suits a hardware product where edge perception must work under real field conditions.

Funding History

2026 | YC Spring 2026; total funding and valuation undisclosed

Notable Open Roles

Senior Applied Audio ML Engineer

This role matters because passive acoustic detection is the front door to EDDA’s product thesis and likely the hardest signal-quality problem in the stack.

Senior Computer Vision Engineer

This points to visual fine tracking as the margin layer after acoustic cueing, where reliable target lock separates a defense product from a sensing demo.

Robotics Engineer

This connects perception to actuation, making turret control, latency, and field reliability core product constraints.

Competitors

Allen Control Systems:

Builds AI-enabled kinetic counter-drone turrets, making it the closest public peer to 9 Mothers.

Fortem Technologies:

Focuses on drone detection and interceptor systems rather than a passive-first close-range turret.

Epirus:

Uses directed energy for counter-drone defense, a different technical and cost profile.

D-Fend Solutions:

Centers on cyber takeover of drones rather than kinetic point defense.

DroneShield:

Sells a broader counter-UAS portfolio across detection and defeat products.

9 Mothers

's Moat:

The path to a moat is proprietary field data: acoustic signatures, hard negatives, and video from deployments that improve close-range detection and tracking.

How They're Leveraging AI

Passive Acoustic Drone Cueing

9 Mothers uses audio ML to detect and roughly locate small drones before the system moves into visual tracking and turret control. The user is a defense operator protecting vehicles, bases, or soldiers at close range.

Visual Fine Tracking For Small Drones

9 Mothers uses computer vision to take over after acoustic cueing and track fast Group 1 drones at short range. The system is built for the moment where coarse detection has to become a target lock.

Sensor Fusion Fire-Control Loop

9 Mothers appears to combine acoustic cues, visual detections, and robotics control into a closed-loop aiming system for EDDA. The AI value is in turning noisy sensor inputs into a usable engagement path.

AI Use Overview:

9 Mothers applies edge ML to a sensor-fusion weapons stack, using acoustic models for passive cueing and real-time vision models for fine tracking.

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