Fitting AI to your product, not the other way around
The best AI features feel like a natural extension of what your product already does. They remove a step, draft a first version, or surface an answer inside the workflow your users already know.
The worst ones are bolted-on chatbots that duplicate your search. We start from your users' existing jobs, not from the technology.
- Anchoring features to a real job your users already do in the product
- Adding AI inside existing screens instead of a separate "AI section"
- Designing for a helpful default with an easy way to edit or undo
- Setting clear boundaries so the feature doesn't overpromise
- Instrumenting usage so you can see what actually earns its keep
Pricing, cost, and margins
AI features carry real per-use costs, so they interact with your pricing in a way most features don't. Getting this wrong can quietly erode margin on your heaviest users.
We help you model usage and choose a packaging approach that protects your unit economics.
- Metering usage per customer so costs are visible before they surprise you
- Deciding between add-on, usage-based, tier-gated, or included pricing
- Setting sensible limits and graceful behavior when a user hits them
- Caching and smaller models where quality allows, to control cost
- A path to restructure pricing as usage patterns become clear
Shipping without derailing the roadmap
You still have a product to run. We scope AI work as a distinct, incremental track with its own flags and rollout so it doesn't stall your core commitments.
- Feature-flagged rollout to a subset of accounts first
- A thin first version that proves demand before deep investment
- Isolation so an AI provider outage can't take down core features
- Multi-tenant data handling that respects each customer's boundaries
- Clear internal docs so your team can support and extend it
More on ai integrations
Frequently asked questions
Should every SaaS product add AI right now?
No. If you don't have a use case where AI clearly removes friction or unlocks something, adding it just to say you have it usually creates support burden without retention. We'll tell you honestly if we don't see a strong fit yet.
How do we keep AI costs from eating our margins?
By metering usage from day one, choosing packaging that matches cost to price, and using caching and right-sized models. We model expected cost per plan before launch so pricing is a decision, not a guess.
Can you build this into our existing stack?
Usually yes. We work with your current framework and data model rather than replacing them, adding the AI layer behind an interface so it stays isolated and swappable.