AI SaaS Development from MVP to Production
An AI SaaS needs everything a normal SaaS needs — accounts, billing, tenancy, admin — plus usage metering and cost control for every model call.
Discuss your AI projectThe problem
01 — ContextFounders often launch with AI costs that scale faster than revenue because usage was never metered.
MVPs built without tenancy or billing in mind need a rewrite exactly when traction arrives.
The approach
02 — MethodWe scope a narrow MVP that proves the core AI value, on an architecture that already handles tenants, billing and metering.
Then we grow it in milestones based on what real users do.
What we build
03 — CapabilityMVP builds
The smallest product that proves your core AI workflow with paying users.
Subscriptions and billing
Stripe subscriptions, trials and webhook handling.
Admin panels
Internal tooling for support, usage and configuration.
Usage metering
Per-tenant tracking of tokens, calls and cost.
Use cases
04 — Applications- Vertical AI tools for a specific industry
- AI features for an existing SaaS
- AI-native CRM or back-office tools
Typical architecture
05 — SystemProcess
06 — DeliveryUnderstand
Understand the business problem and the outcome you need.
Architect
Choose the AI architecture, integrations and technical approach.
Prototype
Validate the critical workflow before building around it.
Build
Develop the product in milestones you can review.
Validate
Test quality, performance, reliability and edge cases.
Launch
Deploy the production system and monitor it.
Improve
Optimise using real usage and feedback.
Questions
07 — FAQTell us what you want to build.
Send a short brief. We'll reply personally with questions or a suggested next step — usually a technical discovery call.