Custom AI model development covers training, fine-tuning, evaluating, and deploying models built on project-specific data — for tasks a general-purpose AI API doesn't cover, or where data can't leave your infrastructure.
Who this is for
Teams with a specific dataset and a task that off-the-shelf AI APIs don't handle well: specialized imagery, movement or sensor data, or domain-specific prediction tasks. Also a fit where data privacy rules out sending information to a third-party API.
Problems it solves
- A task that depends on proprietary or domain-specific data no general API was trained on
- Data privacy or compliance constraints that rule out third-party model APIs
- The need for tighter control over latency, cost, or model behavior than an API allows
Capabilities
- Data preparation and evaluation pipelines
- Model training and fine-tuning in PyTorch and Keras
- Computer vision and motion/pose-based model architectures
- Inference API design for production consumption
- Cloud or local deployment depending on latency, cost, and privacy requirements
Delivery process
Work starts with the data: what exists, what needs collecting, and what the model actually needs to predict or detect. From there, the model is trained, evaluated against real examples, and deployed behind an API the production application can call — not left as a standalone research artifact.