Applied R&D in Vision AI and Edge Intelligence
We research, prototype, and engineer vision-AI and edge-intelligence systems for demanding, real-world environments — taking hard perception problems from concept to working, field-credible prototype.
Engineering Perception for the Field
We research and engineer perception systems built for the conditions they have to survive — ensuring reliability when connectivity is denied and environments are unforgiving.
Reliability
Designed and tested against environmental stress and operational edge cases — because mission environments demand systems that hold up under real conditions.
Precision
We engineer for high detection probability with disciplined false-alarm suppression — accurate intelligence, not noise.
Mission Continuity
Edge-native architectures ensure operational capability persists when connectivity is denied, infrastructure is disrupted, and environmental conditions are unforgiving.
Research & Engineering Capabilities
Our research and engineering spans perception, sensing, and edge intelligence — from algorithm to field-ready prototype.
Vision & Perception Systems
Real-time vision pipelines for optical, thermal, and infrared imagery — engineered for detection, classification, and tracking in demanding operational environments.
- Object Detection & Tracking
- EO/IR Processing
- Real-Time Perception
Multi-Sensor Fusion
Integrated sensor architectures that fuse thermal, visible, LiDAR, and radar inputs — resolving ambiguity where single-sensor systems fail.
- Thermal-Visible Fusion
- LiDAR & Radar Integration
- Degraded Visibility Operation
Edge AI & Embedded Vision
Perception systems optimized for real-time edge inference on low-power, embedded hardware — engineered for offline and connectivity-denied environments.
- Edge Inference Optimization
- SWaP-Constrained Design
- Offline Operation
Systems Engineering & Integration
End-to-end sensor integration, hardware/software co-design, and environmental robustness engineering — bridging the gap between lab performance and field reliability.
- Sensor Calibration
- Environmental Hardening
- Verification & Validation
Thermal & Infrared (EO/IR) Perception
Thermal-native perception calibrated for infrared imagery — detection beyond visible light, in darkness and low visibility.
- Thermal-Native Pipelines
- Low-Visibility Detection
- IR Calibration
Prototyping & Field Evaluation
Rapid prototyping and structured evaluation against real-world conditions — de-risking perception concepts before commitment.
- Rapid Prototyping
- Proof-of-Concept
- Field Evaluation
Where Our Research Applies
Representative problem areas our R&D targets — where reliable perception is hard and matters.
Border & Perimeter Intelligence
Thermal and optical fusion for intrusion detection in darkness, low visibility, and adverse weather — persistent situational awareness across extended perimeters.
Critical Asset Monitoring
Edge vision research for monitoring energy facilities, transport hubs, and sensitive infrastructure — detecting anomalies and threats in real time without cloud dependency.
Hazard & Safety Monitoring
Perception R&D for identifying safety violations, hazardous conditions, and operational risks in manufacturing, energy, and industrial environments.
Why Conventional Vision AI Fails
Many vision initiatives fail during deployment due to architectural assumptions that do not survive real-world operational conditions.
- Reliance on cloud connectivity for mission-critical inference.
- Models trained only on clean RGB imagery that fail in real conditions.
- Inability to operate in low visibility, smoke, dust, or adverse weather.
- High false alarm rates that overwhelm operators and erode trust.
- Lack of sensor calibration and integration expertise for field deployment.
“We measure success the way the field does — by whether perception works under real conditions.”
— Pesgit Engineering
Engineered for the Field, Not the Lab
We don't stop at lab benchmarks. We research and engineer perception for the conditions it has to survive.
Typical AI Providers
- ✕Cloud-dependent architectures that fail offline
- ✕RGB-only models that degrade in adverse conditions
- ✕High false alarm rates that overwhelm operators
- ✕Software delivery without systems integration
The Pesgit Approach
- ✓Edge-native & offline-capable architectures
- ✓Thermal-native perception with multi-sensor fusion
- ✓False alarm discipline with maintained detection probability
- ✓Full systems engineering & sensor integration
Let's explore the problem.
Have a perception or edge-intelligence challenge worth exploring? Talk to us about a feasibility study, a proof-of-concept, or a scoped R&D engagement.