A copilot inside your product
Put AI inside the product you sell: a copilot your customers use every day, wired into your SaaS's real data and the permissions that already decide who sees what
What it solves
You have a SaaS product that works, and customers use it. For plenty of things, though, they still have to work out for themselves where to click, dig through the documentation, rebuild context the product already has. AI could close that gap, yet it usually stays an internal experiment: it runs on your side, and end users never get near it.
The hard part isn't getting a model to answer; it's turning that capability into an actual product feature: one that draws on your software's domain knowledge, builds on your existing architecture, and that you can release, monitor and evolve like everything else. Without it becoming a second system you have to maintain alongside the first.
This scenario is for anyone selling software who wants a copilot customers recognise as part of the product: it guides them through complex flows, takes the repetitive steps off their hands, and answers using the real data from their own instance. A feature that carries your name, not a generic assistant bolted on from outside.
How it works
We pick the flows where AI makes a difference
We look at the product together and find the points where users get stuck or lose time today: a slow onboarding, repetitive operations, a piece of data that exists but is buried three screens away. That's where a copilot earns its place. We define what it should do and, just as important, which steps it should never handle on its own.
Domain knowledge and permissions, not generic know-how
The copilot reasons over your product's own data and rules, wired into the APIs and systems you already have. It answers with the context of that specific customer's instance and goes through the same access controls as the rest of the software, so it never sees or touches anything the user couldn't see or touch themselves. People ask in their own words, the copilot acts or proposes, and at the sensitive steps it asks for confirmation.
It goes live and grows on what works
The copilot ships with gradual rollouts, an action log and a measure of how much it's actually used. From there, we grow it on the cases that work and drop the ones that don't pay off. We've done this before: for a SaaS startup, we built an agent that read product data and drafted replies to customers, starting from a single support flow before widening the scope.
Why it stays under control
- Every action the copilot takes leaves a trace: you know what it did, on which data and on whose behalf, and you can reconstruct it whenever you need to
- The copilot inherits the permissions and domain boundaries of the rest of the product, because it lives in the same architecture rather than a separate service you'd have to govern on its own
- You decide the limits: what it can carry out on its own, what needs human confirmation, and what stays entirely out of its reach
What changes
- Customers stop leaving the product to look elsewhere for something it could already do for them
- You have a feature you can show your users without reservations, because it holds up in production, not just in the demo
- You find out early which use cases are worth pursuing and where it's better to stop, while you're still building rather than after the fact
What we don't promise
- We don't promise a copilot that does everything: it starts from a few flows that hold up and grows from there, never from an endless feature list
- We don't sell full autonomy; at the steps that matter, human confirmation stays in place, because that's how it has to work with your customers' data
- If a use case looks great in a meeting but doesn't hold up in production, we tell you before you start. We once dropped a copilot designed to fill in forms on the user's behalf: it looked like magic in the demo, but on real data it got things wrong too often to trust, so we scaled it back to a suggestion the user confirms
Frequently asked questions
How is this different from bolting a generic chatbot onto the product
A generic chatbot doesn't know your domain and lives outside your systems. The copilot reasons over your product's real data, goes through the same permissions, and carries out real actions in the flows you already have. It's a feature of the product, while the chatbot stays a widget sitting on top.
How can I trust what the copilot does with my customers' data
Every action leaves a trace that can be reconstructed: you know what it did, on which data and on whose behalf. The copilot inherits the same permissions and the same domain boundaries as the rest of the product, and you decide what it can do on its own and what needs human confirmation.
Will I have to rebuild my architecture to add AI
No, it's the opposite. The copilot connects to the APIs and systems you already have, without becoming a second system to maintain alongside the first. Staying consistent with your architecture is one of the constraints we design around, not something we simply hope to achieve.
How much do we need to commit just to find out if this makes sense
You start light with the Inception package: 2–3 weeks of exploratory work at a contained investment, to validate a concrete use case and design the first copilot, before deciding whether and how much to build.
At a glance
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Does this scenario sound familiar?
We start with an Inception package to see, on your actual case, whether and where it makes sense.
Book a callTell us where you're stuck
A fragile prototype, a burdensome legacy codebase or unpredictable delivery: that's where we start