AI Development
Practical AI: a support assistant grounded in your own documents, an extraction pipeline that reads invoices, a recommendation model that lifts a real number. We measure it before and after, and we tell you when a rule beats a model.
What you get
Typical stack
Engagement
A two-week paid pilot with a measurable target, then a build if the numbers hold.
Planning your ai development project
Start with a defined task and examples of acceptable outputs. Data quality, retrieval permissions, evaluation criteria and cost per request matter as much as model choice. Sensitive workflows should specify when a person reviews the output and what happens when the system is uncertain.
Explore our delivery process →Questions about ai development
What should an AI pilot measure?
Choose task-specific examples and acceptance criteria, such as extraction accuracy or whether an answer is supported by source documents. Include failure cases and operating cost in the evaluation.
What information is needed to scope an assistant?
Describe its users, permitted data sources, access rules and the actions it may take. Provide representative questions and examples of answers it should refuse or escalate.