How to add AI to your existing app: practical use cases and what it costs

Start with a task, not a technology

The projects that work begin with a specific, repeated, text-heavy task that costs your team or users time. The projects that stall begin with "we need an AI feature". Pick the task first, then decide whether AI is the right tool.

Use cases that usually pay off

• Support triage and drafting. Classify tickets and suggest replies for a human to approve. We share what worked in lessons from an AI support flow.
• Search over your own content. Let users ask questions of documents, help articles or product data.
• Summaries and extraction. Turn long calls, forms or invoices into structured fields.
• Personalised recommendations. Suggest next steps based on what a user has done.
• Content assistance. Draft descriptions, emails or reports that users then edit.

How we ship an AI feature

Prototype in days. Use a hosted model API and real examples from your data. If it cannot do the task on twenty real cases, stop there.

Keep a human in the loop. Suggest rather than act, at least at first. It builds trust and gives you data on where the model is wrong.

Measure on a test set. Keep a fixed list of real inputs and expected outputs and re-run it whenever you change a prompt or model. Without this you are guessing.

Add basic observability. Log inputs, outputs, cost and latency so you can debug and control spend. The approach is similar to our cheap observability stack.

What it costs

A focused first feature built on a hosted model typically takes a few weeks of engineering, which for an Indian team is often in the low-to-mid five figures in US dollars depending on integration depth. Running costs depend on usage: model calls are billed per volume, so set limits, cache repeated results and choose the smallest model that passes your test set.

Risks to plan for

• Wrong or invented answers, so show sources and allow correction
• Sensitive data leaving your system, so check what you send and your provider terms
• Runaway usage costs, so rate-limit and monitor
• Prompt injection from user-supplied text, so never let model output trigger risky actions unchecked

The takeaway

Pick one valuable task, prototype fast, keep people in control and measure quality. If you are still shaping the idea, our MVP development service can scope it, and our app development team can build it into your product. Send a brief for a fixed quote in 48 hours.