Brittle Vision
Current computer vision systems degrade under fog, low light, motion, angle changes, partial observability, and sensor variation.
Synthetic Intelligence Platform
Synthetic EO/IR training data for autonomy programs.
Thalatta Labs builds synthetic data generation infrastructure that helps computer vision teams create labeled, metadata-rich EO/IR training data for unmanned systems under sparse-data, edge-case, and operationally constrained conditions.
Designed for rapid scenario generation, CV-ready annotation, model retraining, and integration into existing MLOps workflows.
Synthetic EO/IR data
CV-ready metadata
Rapid model adaptation
Where Autonomy Breaks
Detection is not enough.
Autonomous systems must identify what they are seeing with enough precision to support downstream decisions. Small changes in angle, lighting, weather, occlusion, motion, or sensor mode can collapse identity even when object detection still works.
Data Bottleneck
Operationally relevant labeled data is expensive, slow to collect, and rarely captures edge cases, new environments, or emerging threat presentations.
Synthetic-to-Real Gap
Conventional simulation often lacks the realism, variation, and validation needed to transfer reliably to real-world imagery.
The Platform
A synthetic data engine for operational autonomy.
The Thalatta Synthetic Intelligence Platform combines sparse real-world examples, domain-adapted generative models, automated annotation, structured metadata, model training, and validation into a repeatable synthetic-to-real workflow.
01
Sparse Source Data
Ingest limited real-world EO/IR examples from operational contexts — the starting signal for generative expansion.
02
Domain-Adapted Generation
Generative models produce varied synthetic scenes across lighting, weather, sensor mode, altitude, and target configuration.
03
Automated Curation & Annotation
Bounding boxes, class labels, frame-level metadata, and quality filters applied automatically at generation time.
04
Model Training & Evaluation
Synthetic datasets feed directly into training pipelines with structured metadata for reproducible experiment tracking.
05
Validated Model Refresh
Continuous validation against real-world imagery closes the synthetic-to-real loop and drives iterative model improvement.
- — Order-of-magnitude faster iteration cycles
- — Lower collection and labeling burden
- — Continuous validation against real-world imagery
Assessment-Ready Outputs
Designed around the data products autonomy teams actually need.
Thalatta is building TSIP to generate synthetic data packages that can move directly into computer vision development, evaluation, and retraining workflows.
EO/IR Synthetic Imagery & Video
Generation of mission-relevant synthetic scenes for EO/IR computer vision model development, including maritime and ISR-relevant conditions.
Frame-Level Metadata
Machine-readable metadata describing scene conditions: lighting, weather, sensor orientation, target class, scenario parameters, and environmental context.
CV-Ready Annotations
Bounding boxes and structured labels designed for downstream training, evaluation, and error analysis. Compatible with standard annotation formats.
MLOps-Compatible Export
Data packaging designed to integrate with model training pipelines, experiment tracking, retraining workflows, and validation loops.
Capabilities
Built for EO/IR synthetic data operations.
EO/IR synthetic imagery and video generation
Structured frame-level metadata
Bounding boxes and CV-ready annotations
Scenario and environment variation
Edge-case and rare-event generation
MLOps-ready export and retraining workflows
Validation
Synthetic-to-real performance, measured.
99.6%
Aggregate F1 score
~27k
Synthetic samples generated
9
Validated classes
>100fps
Inference on commercial hardware
1–2d
Model iteration cycle
Integration Path
Built to support organic model development workflows.
TSIP is being designed for operator-configurable scenario generation, repeatable dataset creation, and integration into existing machine learning operations environments.
Self-Service Operation
Government users should be able to configure scenarios, vary conditions, and regenerate datasets without sustained vendor involvement.
Government-Hosted Pathway
Architecture intended to support secure cloud, on-premise, or hybrid deployment patterns as program requirements mature.
Traceable Data Lineage
Scenario parameters, metadata, annotations, and generated outputs are designed to remain machine-readable and auditable.
Applications
Multi-domain autonomy infrastructure.
The same synthetic intelligence layer can support drones, maritime systems, ground robotics, ISR networks, and multi-sensor fusion stacks.
Maritime Autonomy
EO/IR synthetic data for USV and maritime surveillance programs. Relevant to UxSAI assessment scenarios.
ISR and FMV Analysis
Training data infrastructure for ISR and full-motion video exploitation systems across EO and IR sensor bands.
Unmanned Aerial Systems
Diverse EO/IR training scenarios for UAS perception stacks operating across altitude, lighting, and environmental conditions.
Ground Robotics & Platform ID
Synthetic training data for ground vehicle classification and autonomous platform identification across varied terrain and conditions.
Team
Built by operators and autonomy engineers.
Zach Hober
Founder & CEO
Operational ISR systems leader. AI/ML perception and autonomy infrastructure. Former Army Infantry officer. West Point. Michigan Ross MBA. Google / YouTube AI.
Scott
CTO
Embedded autonomy systems leader. Robotics and ML engineering. Deployed autonomy on real platforms.
Kelsee
Advisor
Defense technology and autonomy product leader. Product owner experience on U.S. DoD programs of record.
Contact
Build the data layer autonomy needs.
Thalatta Labs is preparing synthetic data generation capabilities for defense autonomy, EO/IR computer vision, maritime assessment scenarios, and rapid model adaptation.
zach@thalattalabs.com
Huntington Beach, CA