AI Strategy & Scoping
Before writing code, define what the AI needs to do, how it connects to your product, and what 'good enough' actually means for your use case.
Why scoping matters for AI
AI features fail most often because the use case was vague, the accuracy requirements were undefined, or nobody decided what happens when the model is wrong. A strategy session answers those questions before build time begins — so the engineering work targets the right problem with the right constraints.
Define the use case precisely
"Add AI to our product" is not a use case. "Classify incoming support tickets by urgency and route them to the right queue" is. We narrow the scope to something that can be built, tested, and measured.
Choose the right approach
Not every AI feature needs an LLM. Some problems are better solved with rules, classifiers, embeddings, or structured extraction. We evaluate which approach fits the use case, accuracy requirements, latency budget, and cost constraints.
Map the integration surface
Where does the AI connect to your existing system? What data does it need? What does it output? Who sees the result? How does it fail? These integration questions shape the architecture more than the model choice.
Set success criteria before building
We define what accuracy, latency, and cost look like for the first version — and what "good enough to ship" means. This keeps the build focused and the evaluation honest.
Questions we typically work through
- What specific task should the AI perform, and what does a correct result look like?
- What happens when the AI is wrong — and who notices?
- Is this a customer-facing feature, an internal tool, or a backend automation?
- What data is available, and are there privacy or compliance constraints on how it's used?
- Does this need to run in real-time, or can it process asynchronously?
Have an AI use case but not sure where to start?
A scoping session defines the right approach, the integration surface, and what to build first.