Begin with the decision or action
An AI system needs a clearer brief than 'add a chatbot'. What should it help someone decide? Which information should it use? Can it propose an action, or is it allowed to execute one?
We work from those questions to define a bounded use case. This is useful for founders and technical teams who need a custom capability to fit an existing product or internal process.
What the system may include
- Context retrieval from approved knowledge and business data sources.
- Task-specific reasoning and structured output for downstream services.
- Tool and API access with explicit permission and execution boundaries.
- Validation, fallback behaviour and human approval for sensitive steps.
From feasibility to an integrated capability
The first step can be a technical review or a small proof of concept using representative inputs. It should reveal data limitations, failure modes and the work needed around the model.
A subsequent build can connect that capability to backend services and native workflows. The delivery scope, operating responsibilities and acceptance criteria are agreed for the project.
Make the result checkable
We would agree examples of acceptable and unacceptable behaviour, including insufficient context and failed tool calls. Evaluation should cover the complete task, not just whether a response sounds convincing.
Research into context, retrieval and reasoning informs the approach. Experimental ideas remain distinct from capabilities that have been built and evaluated for your use case.