Solutions
In-App AI Assistants
For companies with a software product or customer portal whose customers and board are asking about AI features. We build assistants into your product with your engineering team, ready for paying customers.
You're likely a fit if…
Everyone is asking about your AI roadmap.
Customers, your board and your sales team, especially since competitors announced features.
Your prototype isn't ready for paying customers.
It demos well. Answers vary, costs are unclear, and nothing tests it before a release.
Users get stuck in the complex parts of your product.
Your support queue is full of how-do-I questions the product could answer itself.
You're not sure what customer data can go to an AI provider.
Your contracts and security commitments were written before any of this existed.
What we build
A product-aware assistant
Answers how-to questions from your documentation and the user's own account context, inside the product.
Learn more: A product-aware assistantActions in the app
The assistant fills forms, builds reports or configures settings for the user, and asks them to confirm before saving.
Natural-language search and reporting
Users ask questions of their own data, scoped strictly to their account or organization.
A test suite on real user questions
Representative questions with expected behavior, run before every release and every model upgrade.
Learn more: A test suite on real user questionsUsage and cost controls
Per-customer limits, monitoring of model spend, debugging logs, and admin settings that let each customer turn features on or off.
Scope
Included, and not
Design and build in your codebase alongside your engineers, with tests, monitoring and documentation your team owns. Not included: product strategy beyond the AI feature, or rewriting your application.
What we need from you
A product owner, an engineering lead, access to your codebase and staging environment, and your customer data commitments.
Customer data
Each customer's data reaches only their own sessions. Prompts and logs follow your retention policy, and the AI provider is one your contracts allow.
What drives cost
How many actions the assistant can take, the data it draws on, the complexity of your permissions, expected usage, and how closely your engineers pair with us. Builds are scoped and priced after the assessment.
How we'd start
Start with an Assessment of your product, your users' jobs and your data commitments, and pick the feature that fits.
Define success with real user questions and a test set before building.
Build in your codebase with your engineers, behind a feature flag, for a pilot group of customers.
Measure, fix and widen the rollout.
Hand over: your engineers own the code, tests and prompts. Enablement is available if they want to work this way on everything.
Common questions
Should we build our assistant or buy an embedded one?
It depends on how much it needs to know about your product and data. The Assessment makes the build-vs-buy call, and we'll recommend buying when that's better.
Will it work with our stack?
We work in your codebase and your cloud, in your languages, and pick an AI provider your contracts and security team accept.
How do we keep model costs under control?
Through design: the right model for each task, limits per customer, caching where it's safe, and monitoring that shows spend before the invoice does.
How long does it take?
A narrow assistant feature is usually M (1–2 months). An assistant that takes actions across your product is typically L (3–6 months), shipped in stages.
Industries where this applies
Let's find where AI pays off in your business.
Thirty minutes with Daniel. No pitch deck: you talk about your business, I tell you honestly where AI fits and where it doesn't.