AI in Your Product: Start With the Problem, Not the Model (Demo)

Adding a chat assistant to any product is easy today. Making that assistant genuinely useful — and improving its answers over time — is the hard part.

Start with a specific task

Instead of "an assistant for everything", pick a repetitive, costly task: classifying support tickets, extracting data from invoices, or answering questions about an internal policy. A narrow task makes success measurable.

Data before models

If your documents are outdated or contradictory, the assistant will reproduce the problem with more confidence. Clean the sources and assign clear ownership for updates.

Set explicit boundaries

Define what the system must never do, and keep human review in sensitive flows. Log failed questions to improve both content and retrieval.

Measure cost and quality together

Higher accuracy at twice the running cost is not automatically right. Test smaller models on narrow tasks, and use a larger model only when it adds clear value.

Demonstration article from development data.