Applied research keeps our production systems ahead of the conditions they face. We experiment deliberately, measure honestly and ship what works.
Six active lines of work.
Each area feeds directly into the systems we deploy.
- 01
Applied AI Research
We study new perception methods against the problems that appear in real deployments rather than benchmark datasets. Research is judged by whether it changes what we can reliably build for a client.
- Robustness to lighting, weather and camera variation
- Learning from limited and imbalanced data
- Evaluation methods that reflect field conditions
- 02
Model Experimentation
Every project involves candidate architectures, training strategies and trade-offs between accuracy, latency and size. We run structured experiments so those decisions rest on measurements, not preference.
- Architecture and backbone comparisons
- Transfer learning and fine-tuning strategies
- Reproducible training and tracking of experiments
- Accuracy versus latency trade-off analysis
- 03
Computer Vision Development
Detection, tracking, segmentation and recognition form the core of what we build, and each keeps evolving. We develop and refine our own pipelines so that every new project starts from a tested foundation.
- Multi-object tracking under occlusion
- Segmentation for measurement and inspection
- Text and identifier recognition in the scene
- 04
Edge Inference Optimization
The gap between a model that works in training and one that runs within budget on an edge device is where many systems fail. We investigate quantization, pruning, export paths and runtime behavior on the hardware we deploy.
- Quantization and pruning with accuracy tracking
- ONNX export and accelerated runtime behavior
- Multi-stream scheduling on constrained devices
- Thermal and power behavior under sustained load
- 05
AI Prototyping
Before committing to a full build, we prototype against real footage to test whether an idea is viable. Fast prototypes reduce risk for clients and tell us early where the hard problems are.
- Rapid feasibility studies on sample data
- End-to-end proof-of-concept pipelines
- Early measurement of accuracy and failure modes
- 06
Emerging Technology Exploration
Vision-language models, foundation models for perception and new sensing modalities are changing what is possible. We explore these developments to understand where they become practical for production systems.
- Foundation and vision-language models for perception
- Multimodal sensing beyond RGB cameras
- Synthetic data for rare conditions
- Privacy-preserving analytics techniques
Experiments with a destination.
Research at NexShapers is not separate from delivery. It is how delivery gets better.
- 01
Problem-led
Research questions come from production friction: a failure mode on a real site, a latency ceiling, a labeling bottleneck.
- 02
Measured
Every experiment has a baseline, a metric and a held-out set from real footage. Results that do not survive that test do not ship.
- 03
Shipped
The output of research is a change to a pipeline, a model or a tool that goes into the next deployment — not a slide deck.
Have a Hard Vision Problem?
We take on a small number of research collaborations where the problem is real, the data exists and the result would matter in production.