AI SaaS Development
An AI SaaS is two products: the intelligence, and the platform that bills, isolates and scales it. wwwdot.dev builds both, so per-customer model cost is understood before pricing is set rather than after margin disappears.
What we build
- Multi-tenant SaaS architecture
- Usage metering and billing integration
- Per-tenant model cost tracking
- Authentication, roles and permissions
- Analytics and reporting dashboards
- Admin and back-office tooling
- Subscription and credit-based pricing
How we work
Unit economics designed first
AI SaaS margins die when inference cost per customer is discovered after pricing is published. Cost per tenant is instrumented from the first week so a pricing model can be set against real numbers.
Tenancy decided early
Shared, siloed or hybrid tenancy is an architectural decision that is expensive to reverse. It is settled during architecture against your actual compliance and scale requirements, not defaulted.
The dashboard is the product
Customers renew because the interface makes the intelligence legible, not because the model is strong. GeoIQ turns AI-search visibility into a score a marketer can act on; QuantOS turns five models into one market state a trader can read.
Shipped work
Products already built and running, not concept pieces.
GeoIQ
Find out what AI says about you.
Marketing Intelligence · React · PostgreSQL · OpenAI
QuantOS
Trade smarter. Powered by AI.
Quantitative Finance · React · WebSocket · Python · AI
SQUANT
Markets move. Our intelligence moves first.
Quantitative Trading · Next.js · Python · AI · WebSocket
MERIDIAN
One desk. Every regime.
Algorithmic Trading · React · WebSocket · Python · AI
AI SaaS Development questions
- How much does it cost to build an AI SaaS platform?
- Cost follows scope. An AI SaaS MVP is the most common engagement and typically runs 4 to 8 weeks; a full platform runs 2 to 4 months. wwwdot.dev fixes scope and figure after a discovery call, before work begins.
- How do you handle AI costs in a SaaS product?
- Per-tenant inference cost is metered from the start, with caching, context trimming and model tiering applied so cheaper decisions run on cheaper models. That produces a real cost-per-customer figure to set pricing against.
- Do we own the code?
- Yes. Clients receive the full codebase with documentation, and can take it in-house or continue on a retainer.
Related services
Tell us what you are trying to ship.
We reply within one business day, usually with questions and a rough scope.