Realtime Perception Systems
Low-latency computer vision systems for dynamic physical environments.
Applied AI Engineering Studio
Real-Time Computer Vision & Applied ML Systems
Building production-oriented perception systems, realtime ML pipelines, and deployable AI infrastructure for dynamic real-world environments.
pipeline:
input: video_stream
detection: YOLO11
tracking: ByteTrack
inference: low_latency
events: zone_reasoning
telemetry: enabled
deployment: edge_to_cloudSystems
What We Build
Computer Vision Pipelines
Detection, tracking, preprocessing, event reasoning, and evaluation pipelines.
ML Infrastructure
APIs, services, model serving, deployment workflows, and reproducible systems.
Streaming & Telemetry
Realtime streams, WebSockets, runtime metrics, monitoring, and system visibility.
Edge-to-Cloud Deployment
Deployable inference systems spanning edge devices, cloud services, and dashboards.
Selected Work
Featured Projects
Real-Time Vision Intelligence System
A production-oriented realtime computer vision system for object detection, multi-object tracking, motion estimation, event reasoning, streaming APIs, telemetry, and dashboard monitoring.
YOLO11 · ByteTrack · OpenCV · FastAPI · WebSockets · Redis · Docker
Occupancy Intelligence Platform
A multi-camera perception system for occupancy detection, vehicle tracking, zone reasoning, and spatial monitoring in dynamic real-world environments.
YOLO · DeepSORT/ByteTrack · FastAPI · MongoDB/Redis · Docker
Environmental Monitoring Systems
Applied computer vision systems for environmental monitoring using video, satellite imagery, and sensor data, building on previous work in marine biodiversity and solar forecasting.
PyTorch · OpenCV · Satellite Imagery · Object Detection · Data Pipelines
Implementation
Engineering Stack
Python
PyTorch
TensorFlow
OpenCV
YOLO
ByteTrack
FastAPI
Redis
MongoDB
PostgreSQL
Docker
GitHub Actions
Linux
AWS
WebSockets
Focus
Technical Focus Areas
- Real-time inference
- Object detection and tracking
- Multi-camera perception
- Motion estimation
- Event reasoning
- Stream processing
- ML deployment
- Observability and telemetry
- Applied computer vision for real-world environments
About
About AOTC
Ahead of the Curve (AOTC) is an applied AI engineering studio focused on building production-oriented machine learning systems for real-world environments. We develop computer vision, perception, and realtime ML infrastructure across domains including spatial intelligence, smart environments, monitoring systems, and operational automation.
People
Team
Meghna Asthana, PhD
Founding Machine Learning Engineer
Applied ML engineer focused on realtime computer vision, perception systems, streaming inference infrastructure, and production-grade ML deployment.
LinkedIn →Tanish Gupta
Machine Learning Engineer - Systems
Focused on ML systems engineering, deployment workflows, backend infrastructure, operational tooling, and realtime services.
LinkedIn →Shruti Rekha Naik
Junior Engineer - Backend
Backend engineering support across APIs, service integration, operational systems, and internal tooling.
LinkedIn →Bimod Thapa, PhD
Chief Science Officer
Scientific systems, automation workflows, experimental infrastructure, and operational optimisation across applied domains.
LinkedIn →Anvita Asthana
Design Lead
Visual systems, interface consistency, brand direction, and design operations across AOTC products and platforms.
LinkedIn →Bishesh Thapa
Intern
Supporting development, operational workflows, research tasks, and internal engineering initiatives.
Contact