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For SaaS product teams

Ship AI features your users trust and you can afford

Adding AI features to a SaaS product works when each feature has a defined job, an evaluation set built from real user requests, and a cost per request you know before launch. We help you choose the features worth building, build them into your product and keep them measured after release.

Free first conversation · A person replies within one business day · You own the code and accounts

Working session

Test a feature idea before you build it

Pick the idea closest to the one on your roadmap and see how a first session weighs fit, approach, controls and the next step, evaluation and cost included.

Scripted walkthrough

Pick a situation
You An assistant inside our app that answers how-to questions from our docs.
Nexstella
Fit
A strong fit when the documentation is current. Answering from your own docs is a common, well-tested approach, and users already expect help where they work.
Approach
Index the docs with their versions, retrieve by the user's plan and role, and answer with links to the pages used. Questions the docs do not cover go to support.
Controls
An evaluation set from real support questions, groundedness checks, a token budget per answer and caching for the questions asked most often.
Next step
Gather real questions from support tickets, agree what a correct answer looks like, and measure retrieval quality before anything ships.
You Summaries of long customer threads for our account managers.
Nexstella
Fit
A good fit. Summarizing text you already store carries little risk, and a person reads the summary before acting on it.
Approach
Summarize on request rather than on every update, use a smaller model where tests show it is good enough, and keep the original thread one click away.
Controls
Checks that summaries keep names, dates and commitments intact, tenant isolation on every call, and a spending cap per account.
Next step
Pick one team, compare model summaries with their own on real threads, and decide from that comparison whether to roll it out.
You An agent that changes settings in a customer's account when they ask.
Nexstella
Fit
Only with limits. Reading settings and proposing a change is a sensible start; changing a paying customer's account on its own is not.
Approach
The agent drafts the change, shows the user exactly what will happen and applies it only after they confirm. Riskier changes still go to your support team.
Controls
Changes limited to an allow-list, the agent acting with the user's own permissions, injection attempts in the evaluation set, and every action logged with the account history.
Next step
Start with answers about settings, measure how often users accept the proposed change, and widen the scope only when the results support it.

Talk this through with us →

01 Outcomes

What changes for you

  • Features with a clear job

    Each feature starts from a user task and a definition of a good result, so the team can turn down ideas that only look impressive in a demo.

  • Quality you can measure

    An evaluation set drawn from real requests runs on every change to the model, prompts or data, so most regressions show up before your users notice them.

  • Cost you can plan for

    We estimate the cost per request early, add caching and model routing where they help, and track spend per feature once it is live.

02 How it starts

How it starts

  1. Your roadmap and constraints

    You bring the feature ideas and the user problems behind them. We ask about your data, tenancy model, current stack and the margin each feature has to fit within.

  2. One feature, defined

    We agree on the feature with the clearest user value and the lowest risk, write down what a good result looks like and set a budget per request.

  3. A bounded proof in your product

    We build it behind a feature flag and run the evaluation on real requests from a small group of users, with cost and latency measured alongside quality.

  4. Decide from the results

    You see the results against the criteria and decide whether the feature ships, with monitoring in place, or is set aside because it did not hold up.

The usual engagement model: Proof of concept or pilot

03 Start

Pick the first AI feature with us

Book a call with an engineer. We will tell you which ideas suit AI, which a plain feature would serve better, and roughly what each would cost to run.

What happens next

  1. You pick a time, or send us a message.
  2. A person at Nexstella reads what you shared before we talk.
  3. You leave with a clear next step, even when that step is to wait.

Or write to [email protected]. Send a brief instead

Meet

Pick a time with an engineer

What is your work email? The calendar invite and the video link go there.

The invite goes to the calendar you work from. Personal address? Use the Contact tab . If you stop before picking a time, we send one reminder to this address, and nothing else.

30 minutes, video call with an engineer · Times show in your time zone

What happens next

  1. You pick a time, or send us a message.
  2. A person at Nexstella reads what you shared before we talk.
  3. You leave with a clear next step, even when that step is to wait.

Contact

Send us a message

Pick the topic that fits and tell us what you are working on. A person replies within one business day.

A person replies from a real address.

Phone (optional)

Choose the country code, then type the number without it.

The topic leads the subject line, so the right person picks it up first.

A few sentences is plenty. You do not need to know which technology you need.

We use what you send to reply and, if it leads somewhere, to scope the work. Read the privacy notice, or write to [email protected].

Want the full picture? Read Generative AI, agentic AI and intelligent automation.

Also written for you: Enterprise AI to production , Support knowledge assistant .

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