Where AI actually earns its place in your product

The pressure to ship an AI feature is real, and it is usually the wrong pressure. Teams add a chat box or a summarize button because a stakeholder asked about AI, then wonder why nobody uses it. The feature that lands is the one that removes a specific piece of work a user already dislikes doing. Everything else is decoration.

Start from the job, not the model

Before you pick a model, write down the exact task you want to shorten. Not the capability, the task. Draft a first reply to a support ticket. Turn a call transcript into a structured follow up. Pull the three risky clauses out of a contract. When the task is concrete, the design gets easy, because you can measure whether the feature actually saved time.

A useful filter: if a competent new hire could do the task in a few minutes with clear instructions, a modern model can usually do a solid first pass of it. If the task needs judgment your business is legally on the hook for, keep a human in the loop and design for review, not autopilot.

Design for the wrong answer

Language models are confident when they are wrong, so the interface has to make a wrong answer cheap. Show the source. Make edits one click away. Never auto send. The products that feel trustworthy are not the ones with the best model, they are the ones where a bad output costs the user two seconds instead of an apology to a customer.

What we build first

• Drafting and summarizing, where a human always confirms before anything ships.
• Classification and routing, where the model sorts and a person handles the edge cases.
• Search and retrieval over your own content, where the answer is grounded in documents you control.

If you are scoping an AI feature and want a second opinion on whether it earns its place, tell us the task you are trying to shorten and we will come back with a plan and a price.