SaaS Product Development Built to Be Sold, Not Demoed

Often, and it is a common starting point. We begin with an audit of tenancy, permissions and data model, because those three decide whether the codebase can be extended or should be replaced in slices. You get that answer in writing before committing to a build.

Yes. We size and provision the infrastructure, set up CI and monitoring, and attribute cost per tenant so unit economics are visible from launch. Ongoing operation can stay with us or transfer to your team with runbooks, the code and the infrastructure definitions are yours either way. Our AI development cost page covers how running costs are estimated before you commit.

We start with a short discovery call to clarify goals, constraints, and success metrics. Then we identify high-impact AI use cases, review your data and systems, and propose a phased roadmap with costs and risks attached to each phase.

Discovery runs 2 to 4 weeks, an MVP typically lands in 8 to 16 weeks of weekly-demo sprints, and operate is an ongoing arrangement with monitoring and support after launch.

Fixed-scope work is priced per milestone, dedicated teams are billed monthly per seat, and staff augmentation is weekly or monthly. You pick the model that fits and can switch between phases. There is a fuller breakdown of what moves the number on our AI development cost page.

You do. The full repository, documentation, and deployment transfer to you at every milestone, with no lock-in to us.

NDAs from day one, scoped access, and data residency decided at the architecture stage. We work within HIPAA, SOC 2, and similar requirements where they apply.

We monitor accuracy, cost, and uptime, run regression checks as models drift, and keep a defined escalation path with support so the system stays healthy after release.