What "AI features" actually means in a SaaS
"AI" covers a wide range: an LLM that drafts or summarizes, retrieval over your customers' own documents, classification and extraction, or agents that take actions in your app. Each has different reliability, cost, and privacy characteristics.
The engineering is less about the model and more about the plumbing around it — grounding it in real data, keeping it inside guardrails, and handling the times it's wrong.
- LLM-in-the-loop features: drafting, summarizing, rewriting, and Q&A over user content
- Retrieval-augmented generation so answers are grounded in your customer's data, not the model's guesses
- Extraction and classification: turning messy input into structured, usable fields
- Automation and agents that call your app's own functions, behind a permission and confirmation layer
- Evaluation harnesses so you can tell whether a prompt or model change actually improved things
The parts that are easy to underestimate
A demo that works once is easy. A feature that works reliably for thousands of users, at a predictable cost, without leaking one tenant's data into another's, is the real work. Models are non-deterministic, so you need evaluation, fallbacks, and honest UI that shows uncertainty instead of hiding it.
- Cost control: token usage adds up fast — caching, model routing, and limits keep the bill sane
- Data privacy: which provider sees customer data, its retention terms, and per-tenant isolation
- Hallucination handling: grounding, citations, and UI that lets users verify before acting
- Latency and streaming so the feature feels fast even when the model is slow
- Evals and regression tests so a prompt tweak doesn't silently break another use case
- A path to swap providers without rewriting the product
How we build AI into your product
We start from the job the feature does for the user, pick the simplest approach that does it reliably, and build the evaluation and guardrails alongside it. We're honest about what current models can and can't do — an AI feature that's wrong a quarter of the time and hides it is worse than no feature.
- Prototype the feature against real data and measure quality before committing
- Build retrieval, prompting, and guardrails as a maintainable layer, not scattered API calls
- Wire cost tracking, rate limits, and provider fallback from day one
- Design UI that surfaces confidence and keeps a human in control of consequential actions
- Set up evals so you can improve the feature with evidence, not vibes
More on saas products
Frequently asked questions
Do we need to train our own model?
Almost never, at least to start. Most SaaS AI features are best served by a hosted model plus retrieval over your own data and good prompting. Training or fine-tuning a custom model is expensive and only pays off in specific cases. We'll tell you honestly whether your use case is one of them.
Will our customer data be used to train someone else's model?
That depends entirely on the provider and plan you choose — it's a contract question, not just a technical one. The major API providers offer no-training, zero-retention options on business tiers. We'll help you pick a setup that matches what you can promise your customers, and build tenant isolation so data doesn't cross between accounts.
How do you keep the AI from making things up?
You can't make an LLM perfect, so we design around it: ground answers in retrieved source data, show citations, keep a human confirming any consequential action, and measure error rates with evals. The honest framing to your users is "assisted," not "automated and always right."