AI development for products and business systems
AI development with Julius: task definition, data integration, quality evaluation, permissions and operating costs. Begin with a business workflow and test cases before expanding.
AI development is useful when it solves a bounded, testable task: retrieving information, summarizing documents, drafting content or assisting a workflow. Before choosing a model, assess whether AI is needed, which data is available and what happens when an answer is wrong.
Task specification and acceptance criteria
Define the user, inputs, outputs and constraints. Collect typical examples and edge cases, then specify how relevance, errors and missing answers will be evaluated. The deliverable is a task definition and evaluation set, not just a chat interface description.
Data integration and permissions
Establish which sources the system may read, how information is refreshed and how access is separated between users. Document retrieval also needs source references and behavior for unsupported answers. Missing information or permission must lead to an explicit recovery path.
Integration into an existing workflow
Separate a recommendation from an action that changes data. A first version can produce a draft for human approval before sending or updating a system. Plan usage limits, provider failures and a way to continue operating when the model is unavailable.
Evaluation before expansion
Compare results against the evaluation set and examine latency and cost per task. Include malicious instructions embedded in retrieved material, unauthorized use and behavior changes after a model switch. Expand according to the acceptance conditions agreed for the project.
Product context: La Vision
The La Vision specification separates AI suggestions from actions requiring a user decision, with control over memory and information. This specification example explains why system boundaries are part of the product. It is not a performance metric or a claim of clinical effectiveness.
Explore the La Vision product decisionsAI development questions
Do we need to train a new model?
Not necessarily. An existing model with instructions and authorized information may be sufficient. Evaluate prompting, adaptation and training against the task, available data, privacy constraints and cost. Test examples from your workflow before making that decision.
What determines the cost of AI development?
Cost depends on integration scope, data preparation, the interface, evaluation and usage frequency. Separate development cost from ongoing model, storage and monitoring costs. A useful estimate states assumptions and usage volumes rather than only a setup price.
How do we know the solution is ready for users?
Agree on examples and acceptance criteria, test permissions and failures, and identify where human approval is required. Expansion also needs a recovery path and an owner for incidents. One successful demo cannot replace repeatable evaluation of the complete workflow.