/services/saas
AI-native products, from idea to paying subscribers
Product strategy, MVP builds, billing and multi-tenancy for products where AI is the point. Including the economics: per-query costs that still work when you have ten thousand users.
// what we do
Where we earn our keep
- MVP in weeks, not quarters. The smallest product that can earn a paying customer, with the AI feature that makes it worth paying for, shipped fast enough to learn from.
- Unit economics that survive scale. Model costs per user modelled before launch, with caching, routing and tiering so growth improves your margin instead of eating it.
- Billing done right. Subscriptions, trials, usage-based pricing, VAT and dunning. The unglamorous 20% that causes 80% of support tickets, handled.
- Multi-tenancy. One codebase, every customer's data isolated, and per-tenant AI context that never leaks between accounts.
// tools we reach for
The stack
Chosen per project, never by habit. These are the tools we most often ship with for SaaS.
// deliverables
What you actually get
Every engagement ends with things you can point at, not hours on an invoice.
A launchable product
Onboarding, billing and the core AI feature live. Someone can sign up and pay on day one.
Cost-per-user model
What each tier costs you to serve, in writing, with the caching and routing that keeps it profitable.
Subscription infrastructure
Plans, trials, usage metering and tax handled through Stripe or Paddle.
Tenant architecture
Data isolation and per-customer AI context, documented and enforced.
Admin and metrics
MRR, churn, usage and model spend visible from one dashboard.
A roadmap grounded in data
What to build next, based on what users do rather than the loudest request.