Adding AI to an existing product

This guide is for founders, product leads and engineering leads who want to add an AI feature to a product that already works, without breaking it or overspending.

1. Pick the workflow first

An AI feature improves one specific workflow. Before writing any code, answer two questions:

• Where are users stuck or waiting? Manual tagging and slow responses are common examples.
• What will you measure? Choose a number that should move, such as support response time or churn.

Answering these first saves sprints later.

2. Check whether your system is ready

Look at three areas:

• Data. Is it clean, structured and labeled?
• Architecture. Can your system call external services securely and at the volume you need?
• People. Does someone on the team understand both the models and your business logic?

3. Choose how to integrate

You do not need to train a model. There are three common approaches:

1. A hosted API such as OpenAI, Anthropic or AWS Bedrock. This is the fastest way to start.
2. A model that runs inside your own system, for tighter control and privacy.
3. A fine-tuned model, when you have data no general model has seen.

Start with the lightest option and move to a heavier one only once the feature has proven its value.

4. Plan for failure

AI features fail differently from ordinary code.

• Set confidence thresholds, and fall back when a response is below them.
• Log inputs and outputs, and collect feedback, so you can see what goes wrong.
• Have a person review output where accuracy matters.

5. Start with one feature

Launch one feature, measure how much it is used and what it changes, then decide what comes next. For example:

• Phase 1: summarize support tickets.
• Phase 2: route tickets automatically.
• Phase 3: flag customers who may churn based on their tickets.

6. Keep the stack small

For most teams this is enough:

• the model provider's own SDK,
• Postgres with pgvector for embeddings,
• the web framework you already run.

Add orchestration frameworks or experiment tracking when you have a problem they solve, and only use tools your team can maintain.

7. Show the result

Once the feature has moved the number you chose, show it inside the company with the data behind it. Measured results get the next feature approved.

Summary

Start with a clear workflow, build the smallest thing that works, measure it, and grow from there.

To plan your first AI feature with us:

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