AI engineering, MLOps, and agentic workflows
We build AI and machine learning into software and operational workflows. Our work includes predictive models, AI agents, MCP-connected tooling, and the pipelines required to operate models after deployment.
AI integrations and agents
We connect models to application data, tools, and services. This includes AI agents and MCP-connected tooling for tasks across multiple systems, with human approval for consequential actions.
Machine learning operations
We build pipelines for model training, versioning, deployment, monitoring, and retraining. These pipelines connect machine learning work to the infrastructure and software delivery processes used in production.
Data and workflow requirements
We assess the available data, the task a model needs to perform, and the systems that will consume its output. An advisory engagement can establish whether an AI approach is appropriate before implementation.
Production integration
We integrate AI components with the surrounding application, infrastructure, and approval processes. The engagement can include backend development, deployment automation, and the operational work needed to maintain the model after launch.