Before you hire anyone to add AI to your SaaS product, ask them eight things: how they start from a workflow rather than a model, how they pick the first feature, how they define a good result before building, where the AI calls will run in your app, what data leaves your system, how they handle wrong or manipulated output, how cost and latency are watched after launch, and who owns the feature once it ships. A partner who answers these clearly will usually ship something useful. One who leads with a demo chatbot usually will not.
This guide is for founders and product leaders with a working SaaS product who want AI features inside it and are deciding who should build them. For the engineering side, see Adding AI to an existing Rails application without putting production at risk.
1. Do they start from your workflow or from a model?
The useful question is which task in your product gets faster or better, not which model is newest. A good partner asks what your users and staff do by hand today, how often, and what a mistake costs. If the first conversation is about models and tools, the feature is likely to be a bolt-on rather than part of how your product works.
2. How will they choose the first feature?
Ask for their selection criteria. A sound answer weighs three things: the value of the task, the risk if the output is wrong, and how easily quality can be measured. Good first features are frequent, mostly text (drafting, summarizing, classifying, extracting), use data your application already holds, and keep a person deciding the outcome. Open-ended chat over your whole product and features that act without review are weaker places to start.
3. How will they define "good enough" before building?
Ask what a good result looks like and how it will be tested. Expect a set of real examples from your product with the answer a good employee would give, run before launch and after every prompt or model change. Without this, nobody can tell whether a change made the feature better or worse.
4. Where will the AI calls run in your app?
Model responses can be slow and can fail. Ask how they keep that from affecting the rest of the product. Look for background jobs instead of calls inside web requests, caching where it makes sense, a fallback or manual path when the provider is down, and one service layer in your code that holds the provider client, prompts, timeouts and logging, so switching providers later is a change in one place.
5. What data leaves your system?
Ask exactly which fields will be sent to a model provider and why. A careful partner sends only what the task needs, keeps your existing permissions in force so one customer never sees another's data, and reviews the provider's data terms with you before anything ships.
6. How do they handle wrong or manipulated output?
Ask how they treat model output: as trusted, or as untrusted input that must be checked. The OWASP Top 10 for LLM Applications lists prompt injection, improper output handling and excessive agency among its risks. In practice that means validating structured output before using it, separating instructions from user content, and giving the model draft or read permissions first, with a person confirming anything that sends, deletes or charges.
7. How will cost and latency be watched after launch?
Usage-based pricing means a loop or a heavy account can run up the bill. Ask how cost per request and per account will be tracked, what limits and alerts will exist, and how quality and latency will be monitored once real users arrive.
8. Who owns it after launch?
AI features need tuning as models, prompts and your data change. Ask whether the partner will monitor and extend the feature, and make sure you own the code, the prompts, the logs and the evaluation examples, so another team could pick it up.
Have an AI feature in mind? Bring the workflow and the data it would use to a Systems Discovery Call and we will tell you whether it is a good first feature.
Red flags
- The proposal starts with a chatbot on your homepage rather than a task inside your product.
- They recommend rebuilding your application before adding any AI.
- No plan for testing quality before users see the feature.
- Model calls inside web requests with no fallback.
- No answer to which customer data leaves your system.
- The model can take actions on its own from day one.
How we work
We work with SaaS teams and operators who have a working Rails product and want AI features inside it. We pick the first feature worth building, integrate it into your existing architecture, and measure its quality before it reaches users. Background jobs, caching, and fallbacks keep AI calls from slowing down or taking down the rest of the app; prompt design, output checks, and rules for what data leaves your system cover guardrails and data controls. In production, we built AI product recommendations into Midway Sports' Shopify operation, alongside the order and inventory system behind 18.5M+ transactions and 200,000+ SKUs. Details are on our AI integration for Rails apps page.
Common questions
Do we need an AI specialist firm, or can our current developers do it?
Calling a model API is the easy part. Most of the work is the same engineering your product already needs: background jobs, permissions, logging, testing and monitoring. A team that knows your codebase and can show how it will measure quality is often a better fit than one that only knows models.
Do we have to rebuild our product to add AI?
Usually not. AI features can be added to an existing application behind one service layer. If the app is several framework versions behind, plan the upgrade alongside the feature; see Upgrading an old Rails app without a rewrite.
Is a chatbot a good first AI feature?
Rarely. Open-ended chat is hard to measure and easy to manipulate. A narrow feature inside an existing workflow, such as drafting a reply or extracting fields for review, is easier to test and shows its value sooner.
Next step
If you want AI to take real work off your users or staff, book a Systems Discovery Call. It is 45 minutes on your product and the workflow you want to improve, and you leave knowing which AI feature to build first and how it fits your app, whether you hire us or not.
Farooq Ch
Oct 2026