SaaS development company for AI-native products, from MVP to scale

The cost of a SaaS MVP comes down to scope, and the drivers are easy to name: the number of core workflows at launch, user roles and permissions, integrations, billing complexity (flat plans are simpler than seats plus usage), AI features that need evaluation work, and enterprise requirements such as single sign-on and audit logs. The most affordable MVP does one job well. We cut scope with you during discovery, then give you a fixed price for the MVP so you can plan your runway.

A focused SaaS MVP typically takes 3–6 months from kickoff to public launch: 2–4 weeks of discovery and validation, 2–3 weeks of design and architecture, 8–12 weeks of building, and a 2–4 week beta. Validation is where timelines are won, because it's where features get cut. Launching a narrow product in 4 months and learning from paying users beats spending a year on features nobody asked for.

Multi-tenant, for most B2B products. In a multi-tenant (pooled) design, every customer shares one application and database, and each row carries a tenant ID enforced by PostgreSQL row-level security, a database rule that stops one customer from reading another's data. It's cheaper to run and simpler to update. Single-tenant (siloed) setups, with a dedicated database per customer, suit regulated or very large customers who require isolation. We design the data layer so a big customer can move to a dedicated database later without a rewrite.

Price AI features from the cost per active user, and measure it from day one. Example: if a typical user triggers 300 AI requests a month at about $0.01 each in model fees, that's $3 per user per month, easy to absorb on a $49 plan. A power user running 10 times that costs $30 and erases the margin. So we meter AI usage per tenant, set plan limits or credits, cache repeated context, and route simple tasks to smaller, cheaper models. A model price change then shows up on a dashboard, not in your gross margin.

You own the code and the intellectual property, and we set it up the way investors and acquirers expect to find it: IP assignment from everyone who touches the code, the repository and cloud accounts in your company's name, an inventory of open-source licenses, and documentation of the architecture and deployment. Technical due diligence goes looking for exactly these gaps, and closing them during the build costs far less than closing them in the middle of a fundraise.

Not to launch, but you do need technical leadership. Founders regularly reach their first paying customers with an outside product team, as long as someone experienced owns the architecture, the roadmap, and the trade-offs. We can fill that role, as your product team or as a fractional CTO, and help you hire your first engineers once traction justifies it. Some investors want an in-house technical leader before a later round, so we document everything with that handoff in mind.

We fixed-bid the MVP once discovery has settled the scope, because founders need to plan runway. After launch the work changes: you learn from users every week, so a monthly product retainer or time-and-materials sprints fit better than a fixed scope. That ongoing work covers new features, performance and scaling, security updates, monitoring, and the analytics reviews that decide what to build next. If you hire your own team, we transition the codebase with documentation and pairing sessions.