Build the process, not only the chat interface.
In an AI application, the whole process matters: which data enters, what the model may do, who checks the result, and what happens when an answer is uncertain.
Start an AI platform briefUseful when
- There is a useful AI case, but the required data and rules are not yet clear.
- Documents, public sources, and internal data need to work in the same process.
- A prompt-based prototype must become an application that can be tested and operated.
- Mistakes have consequences, so the system must know when to stop and ask for human review.
Typical outcomes
- Process architecture, sources, and a clear approach to uncertainty.
- Structured inputs and outputs, tool contracts, and clear provider boundaries.
- Human review, an audit trail, and error handling.
- A working first path with evaluations and tests based on real cases.
ICE
How the build moves
Sources and limits
Define trusted data, what the system may decide, and when human involvement is mandatory.
Contracts
Define data structures, tools, prompts, and behaviour when something fails.
Implementation
Build one complete path from data and model to interface, logging, and storage.
Testing
Check ordinary cases, problematic inputs, source handling, and recovery before expanding the system.
What we avoid
- A chatbot added to a product without access to domain data and rules.
- Unreviewed full automation where mistakes have material consequences.
- A demo presented as proof that the system is ready for production.