Engineering AI-Ready Data and Applied Solutions
We transform raw clinical, industrial, and operational data into AI-ready assets, support model development, and enable real-world system integration across domains.
Where We Excel
Dataset Engineering
We build preprocessing pipelines for clinical, sensor, or industrial data—handling signal alignment, normalization, augmentation, and schema mapping. A well-structured dataset reduces bias, avoids unnecessary retraining, and directly improves model performance and stability.
Data Labeling
We label 2D and 3D images, time-series data, and event logs for machine learning applications using manual, semi-automated, and hybrid workflows—eliminating annotation bottlenecks in medical imaging, autonomous systems, and industrial QA.
Computer Vision
We develop image classification, object detection, and segmentation pipelines with OpenCV, TensorFlow, and PyTorch—addressing domain-specific visual data challenges in healthcare, manufacturing, and robotics.
Feature Engineering
We implement feature extraction, embedding generation, and input-formatting workflows for structured and unstructured data—accelerating machine learning prototyping and improving model performance.
IoT and Sensor Data
We ingest, preprocess, and validate telemetry, sensor streams, and device logs for AI-driven analytics and real-time monitoring—resolving sampling gaps, drift, and noise in IoT environments.
Validation and Testing
We conduct pipeline validation, data integrity checks, and real-time scenario testing across platforms—ensuring model accuracy and preventing costly failures. When needed, we also automate feedback loops from end users to monitor model quality in live environments.
MLOps & DevOps
We implement CI/CD pipelines, containerization, and automated model versioning with MLflow, Docker, GitLab CI, and Kubernetes—streamlining training, deployment, and machine learning operations.
Domains served
Your bring-up and validation teams drown in high-frequency sensor streams, debug logs, and performance counters—manual review misses subtle failure modes and delays silicon iterations. We build end-to-end ML pipelines that ingest raw ADC samples and log files, train autoencoder and time-series models to flag anomalies, and deploy real-time inference agents on test rigs. The result: automated root-cause analysis, faster board bring-up, and more engineer time spent on design, not log sifting.
Clinical devices produce multimodal data—ultrasound, ECG, infusion-pump telemetry—that’s costly to label and validate under IEC 62304/ISO 13485. We craft AI workflows combining UNet-based segmentation, time-series classifiers, and automated annotation tools to detect anomalies in imaging and sensor streams. Integrated into your compliance pipelines, our validation frameworks ensure model outputs hit regulatory accuracy thresholds, slashing manual QC and accelerating software releases.
Your platform emits terabytes of user events, API logs, and business metrics—but insights stay buried in dashboards. We embed machine learning into your cloud-native stack: from feature-store design and batch/stream preprocessing to model training with MLflow and Kubernetes deployments. Whether it’s recommendation engines, predictive analytics, or anomaly detection, we streamline experimentation-to-production in your CI/CD pipelines—so AI-driven features ship as reliably as any other service.
Why choose us
Built Around Real Data
→ We work with clinical records, 3D scans, LIDAR, sensor streams, and unlabeled imaging—not not simplified or pre-cleaned datasets.
No Outsourced Thinking
→ Our engineers own design, code, and support end-to-end—no handoffs to generic AI labs.
Cross-Domain Proven
→ Models running in medical devices, embedded boards, and SaaS platforms—deployed, not just prototypes.
Data Comes First
→ Preprocessing, bias reduction, and augmentation—so your models start with the right foundation.
Scalable MLOps
→ CI/CD for training, versioning, and containerized deployment—so your ML workflows grow with you.
Technologies & Tools
Model Development
- Python
- TensorFlow
- Keras
- PyTorch
- Caffe
- scikit-learn
- OpenCV
- MONAI
- SciPy
- C++
- Qt
- OpenGL
- ARM
Data Engineering & APIs
- Label Studio
- ROS Bag
- HDF5
- DVC
- Apache Airflow
- Pandas
- NumPy
- FastAPI
- MQTT
- Kafka
MLOps &DevOps
- MLflow
- Weights & Biases
- Docker
- Kubernetes
- Jenkins
- GitLab CI/CD
- TeamCity
- JIRA
- pytest
BI & Visualization
- Qlik Sense
- Tableau
- Power BI
- Grafana
- Postman
Got questions?
How do you handle inconsistent or incomplete data?
We design preprocessing pipelines that align, normalize, and validate raw clinical, industrial, and sensor data—turning fragmented inputs into AI-ready datasets.
How do you maintain model reliability without retraining it?
We design validation pipelines, versioned data sets, and hooks for monitoring tools—so your team can maintain model performance with minimal rework.
Can you work with siloed, domain-specific data formats?
Yes. We build parsing and preprocessing logic for logs, time-series, DICOM, LIDAR, and other domain-specific formats—avoiding expensive data migrations.
How do you handle labeling for healthcare or industrial data?
We combine expert labeling, semi-automated workflows, and augmentation tools like Label Studio to create domain-specific datasets for imaging, time-series, and sensor data.
How do you test AI models for real-time performance?
We validate model accuracy, latency, and stability across real-time and batch pipelines—using pytest, MLflow, and simulated production scenarios.
How do you make AI pipelines deployment-ready?
We deliver containerized, versioned pipelines with integrated CI/CD and validation logic—ready to run in cloud, edge, or hybrid environments.
Can you deploy AI across edge, cloud, and hybrid setups?
Yes. We design AI solutions that operate efficiently on embedded hardware (ARM, CUDA), hybrid cloud platforms (AWS, Azure), and real-time edge environments.
What happens if project requirements change midstream?
Our modular AI architectures allow updates to datasets, models, and APIs without overhauling the entire system—keeping projects flexible and aligned with evolving needs.
Let’s Make Your Data Work Smarter
From dataset preparation to AI system deployment, we help you turn raw inputs into real-world solutions. Let’s chat