A defined workflow
Documented data flow, system boundaries and expected behavior.
Services / AI engineering & MLOps
Give your AI work a path to everyday use
An AI demo is a starting point. We help with the data, deployment, evaluation and monitoring needed to turn it into something people can use.
Where we can help
AI systems need attention beyond the model itself. Input quality, permissions, response times, evaluation and ongoing costs all affect whether an idea works in practice. We help define those needs before adding more complexity.
AI application integration
Connect AI capabilities to application workflows with clear inputs, outputs and failure handling.
Data processing
Build repeatable steps for preparing and moving data, including validation and access considerations.
Model deployment
Plan model serving, versioning and environment configuration for the intended workload.
Evaluation & monitoring
Define evaluation examples and track useful signals such as quality, latency, failures and resource use.
What the work can include
Something your team can use.
Deployment assets
The agreed serving or integration components, configuration and operating instructions.
Evaluation plan
Relevant examples, checks and monitoring guidance to support ongoing decisions.
Deliverables are tailored to the scope agreed for your project.
Before we start
A few useful answers.
Can this include generative AI?
Yes. The scope can cover generative AI integration, retrieval workflows and evaluation where they fit the use case.
Can you guarantee a model will always be correct?
No. AI outputs need evaluation and appropriate review. The design should account for uncertainty and define when a person needs to step in.
Let’s talk about your setup.
A few notes about the problem are enough to get started.