Data & Feature Engineering
Defining success metrics, then gathering and engineering features from your data and sensor streams — usually the step that decides whether the rest of the project succeeds.
PandasNumPyAugmentation
Capability · India
Custom AI Model Development & Training
We build and train custom AI models for organisations that have data and a problem but no in-house machine learning team — and, just as often, for teams whose model works in a notebook but not on the device. Our Vadodara engineers cover the full path: data and feature engineering, model development, training and optimisation, and integration into a product that has to keep working.
Edge AIPhysical AIPyTorchDeep learningComputer visionModel trainingQuantizationEdge deploymentMLOps
What we do
From your data to a model running in your product
Custom Model Development
Models designed for your problem rather than pulled off a shelf — classification, regression, detection and segmentation, using CNNs, transformers or transfer learning as the data warrants.
CNNTransformersTransfer learning
Training & Evaluation
Training runs with hyperparameter tuning and honest evaluation against metrics agreed up front, so performance claims mean something outside the training set.
TrainingTuningMetrics
Computer Vision
Detection, segmentation and image analysis built on OpenCV and PyVision, including work on medical images where annotation quality matters as much as architecture.
OpenCVPyVisionSegmentation
Optimization for Deployment
Tuning, quantisation and acceleration for the target hardware — GPU, edge or embedded — with export to runtimes such as TorchScript and ONNX.
QuantizationTorchScriptONNX
Production Integration
Deploying models into real products — embedded, edge, on-premise or cloud — with monitoring, versioning and the reliability our customers expect.
EdgeAPIsMonitoring
How we work
From data to deployed model
Most models that fail in production do so for reasons visible at the start: unclear success criteria, data that does not represent deployment, or a target platform nobody costed. We work through those in order.
- Define what success means numerically, and how it will be measured.
- Gather and engineer features from your data and sensor streams.
- Develop and train candidate models, comparing them honestly.
- Tune, quantise and accelerate for the target — GPU, edge or embedded.
- Integrate into the product with monitoring and versioning in place.
- Hand over training pipelines so the model can be retrained without us.
What you get
Deliverables from an AI engagement
Including the pipeline, not just the weights — a model you cannot retrain is a liability once your data changes.
- Trained model weights and architecture
- Reproducible training pipeline and configuration
- Data preparation and augmentation code
- Evaluation report against agreed metrics
- Optimised export for the target runtime
- Inference integration code
- Monitoring and versioning approach
- Documentation of data, assumptions and limits
Where our engineers are
AI model development across India, from our Vadodara engineering team
Our engineering team works from two hubs: an engineering office in Vadodara, Gujarat and headquarters in Silicon Valley. Most day-to-day design and verification work is carried out by the Vadodara team, which means clients across India — in Bangalore, Hyderabad, Pune, Delhi NCR, Ahmedabad and beyond — get direct access to the engineers doing the work, in the same time zone, at competitive rates — while the US office keeps the programme close to customers in North America.
India — Engineering Office: 301-306 (3rd Floor), Ozone, Sarabhai Road, Vadodara, Gujarat 390023 ·
+91 265 3100926 ·
contact@awengworks.in
USA — Headquarters: 1307 S. Mary Ave., Suite 260, Sunnyvale, CA 94087
Common questions
Custom AI questions we are asked most
How much data do we need before this is worth doing?
It depends more on how representative the data is than how much there is. The first thing we do is assess what you have against the problem you want solved, and say plainly if the answer is that you need to collect more first.
Can the model run on our device rather than in the cloud?
Usually yes, and it is a large part of what we do. Getting a model inside an embedded power and latency budget means quantisation and export to a runtime like TorchScript or ONNX, and treating inference cost as a hardware requirement.
Do you work with medical imaging data?
Yes. AI-assisted imaging is an active area for us, including segmentation and automated measurement. Where a model contributes to a clinical decision it becomes part of the regulated device, so training data, validation and change control need documenting accordingly.
Where is your AI team based?
In our Vadodara engineering office at Ozone, Sarabhai Road, Gujarat 390023, with headquarters in Sunnyvale, California.
Have data and a problem worth solving?
Tell us what you are trying to predict, detect or measure, and we will tell you honestly whether machine learning is the right tool for it.