SaaS MVP Development · San Francisco, CA

SaaS MVP development in San Francisco.

San Francisco MVPs ship into the most AI-literate market on earth: buyers who have seen every demo, investors who ask about evals and unit economics, and competitors who launch weekly. The bar is not a working prototype; it is a product with measured quality and economics that survive scrutiny.

We build AI-native SaaS MVPs end to end: Next.js and TypeScript on Postgres, real auth and Stripe billing, language-model features with metering and an eval set, and production deployment. You own everything from the first commit.

How we work

Tell us what you are building and when it needs to exist.

The technical reality of shipping an MVP in San Francisco.

AI-native here means the model is in the product's critical path, which raises the engineering bar in specific ways: an eval set before launch (because prompt changes without measurement are gambling), per-tenant cost metering (because usage-based costs need usage-based visibility), and fallback behavior (because your uptime should not inherit a vendor incident). We build those in as the skeleton, not as post-launch repairs.

Defensibility at this stage is operational: own the workflow, own the usage data, own the eval set. We instrument from day one so every week of real usage compounds into an asset competitors cannot copy from your landing page.

Speed is a scope discipline. One core workflow end to end, one integration that matters, standard patterns for everything undifferentiated. Fridays have working demos; decisions have deadlines; the scope document is the referee. Six to ten weeks to production is normal when that holds.

Billing is product strategy in SF: usage-based pricing needs metering before pricing, entitlements that map to plans, and a Stripe setup that can evolve without migration. We default to seats-plus-metered-events so the pricing conversation stays open while the meter runs.

What an MVP engagement includes.

The standard build, tuned for AI-native products and SF's pace.

Core workflow, end to end

Next.js, TypeScript, Postgres. The one thing your product does, built completely and running on real data by mid-engagement.

AI capability with evals

Model features in the critical path shipped with a labeled eval set, regression runs on every change, and quality dashboards you can show investors.

Metering and usage-based billing

Per-tenant cost and usage metering wired to Stripe entitlements, so pricing can follow evidence instead of guesswork.

Model routing and fallbacks

An internal model interface, difficulty-based routing where volume justifies it, and tested fallback paths for vendor incidents.

Auth, tenancy, and flags

Organization accounts, roles, feature flags, and SSO-ready auth, so design partners onboard cleanly and experiments stay controlled.

Deployment, runbook, handoff

Production deployment with monitoring, an architecture doc, a runbook, and a handoff designed for the team you have not hired yet.

Built for the San Francisco market.

San Francisco founders operate with the shortest feedback loops anywhere: design partners respond this week, investors compare you to last month's batch, and the model landscape shifts under everyone quarterly. The MVPs that thrive are the ones whose quality is measured, whose costs are visible, and whose architecture can absorb a model swap without a rewrite.

Our defaults match that tempo: eval sets as company assets, metering before pricing, boring code that new hires extend instead of rewriting, and weekly demos that keep the build honest.

We work with SF teams remotely, with demos and pairing on video in Pacific hours. Most builds reach production in six to ten weeks.

Questions from San Francisco founders.

How fast can we realistically get to first users?: Six to ten weeks to a production MVP with real users is the honest range for a focused scope, and the variable is almost entirely scope, not engineering speed. The builds that hit six weeks have one core workflow, one integration that matters, auth and billing from a standard pattern, and a founder who makes decisions in days rather than weeks. The builds that drift to fourteen have three personas, a marketplace dynamic, and a pricing model still being invented in week eight. We fix scope in writing before the first commit and demo working software every Friday, so if the answer is drifting you see it by week two, not week ten. Speed here is a discipline, not a heroic sprint.
Everyone says AI wrappers have no moat. What actually makes an AI product defensible at MVP stage?: At MVP stage, defensibility is mostly a story about data and workflow, and the honest version is thinner than pitch decks imply. What you can genuinely build early: a workflow position (the product owns the place where work happens, not just a chat box beside it), proprietary evaluation data from real usage that makes your quality measurably better on your niche, and integration depth that makes switching annoying. What you cannot buy at this stage is a model advantage; the labs own that. Our build choices follow: instrument everything (usage becomes training and eval data you own), go deep on one workflow rather than wide on capabilities, and treat the eval set as a company asset from day one. The moat grows from operations, not architecture.
What does a smaller scope buy versus a larger one?: At the smaller end: one core workflow built end to end on our standard stack, standard auth, Stripe billing with simple tiers, one AI feature with basic metering and a starter eval set, deployed to production with monitoring. At the larger end: multi-role products, usage-based billing with entitlements, deeper AI work (routing across models, retrieval over customer data, a real evaluation harness in CI), two or three integrations, and the enterprise-leaning scaffolding (SSO-ready auth, audit logs) that lets a design partner's security review say yes. The middle of the range is most SF builds: one strong workflow plus one serious AI capability plus billing that matches how the product creates cost. We quote after a one-hour scoping call, and the quote itemizes what moves the number.
What happens after launch? We will hire engineers eventually but not immediately.: The handoff is designed for that gap. Every build ships with a runbook (deploys, environments, common failures, escalation paths), an architecture document explaining the decisions while they are still fresh, and a recorded walkthrough your future hires watch on day one. The code itself is boring on purpose: standard Next.js patterns, typed end to end, no clever abstractions that need their author present. For the gap itself, most founders take a maintenance arrangement covering dependency updates, monitoring response, and small fixes, cancelable monthly once your team is seated. When you do hire, we do a handoff session with the new engineer and then get out of the way. The deliverable is a codebase your hire respects, not one they campaign to rewrite.

Ready to scope your MVP?

Tell us the workflow, the AI capability, and the date it needs to be real. We reply within one business day with a rough scope and a fixed price range.

SaaS MVP development in San Francisco.

The technical reality of shipping an MVP in San Francisco.

What an MVP engagement includes.

The standard build, tuned for AI-native products and SF's pace.

Core workflow, end to end

Next.js, TypeScript, Postgres. The one thing your product does, built completely and running on real data by mid-engagement.

AI capability with evals

Model features in the critical path shipped with a labeled eval set, regression runs on every change, and quality dashboards you can show investors.

Metering and usage-based billing

Per-tenant cost and usage metering wired to Stripe entitlements, so pricing can follow evidence instead of guesswork.

Model routing and fallbacks

An internal model interface, difficulty-based routing where volume justifies it, and tested fallback paths for vendor incidents.

Auth, tenancy, and flags

Organization accounts, roles, feature flags, and SSO-ready auth, so design partners onboard cleanly and experiments stay controlled.

Deployment, runbook, handoff

Production deployment with monitoring, an architecture doc, a runbook, and a handoff designed for the team you have not hired yet.

Built for the San Francisco market.

Our defaults match that tempo: eval sets as company assets, metering before pricing, boring code that new hires extend instead of rewriting, and weekly demos that keep the build honest.

We work with SF teams remotely, with demos and pairing on video in Pacific hours. Most builds reach production in six to ten weeks.

Questions from San Francisco founders.

How fast can we realistically get to first users?

Six to ten weeks to a production MVP with real users is the honest range for a focused scope, and the variable is almost entirely scope, not engineering speed. The builds that hit six weeks have one core workflow, one integration that matters, auth and billing from a standard pattern, and a founder who makes decisions in days rather than weeks. The builds that drift to fourteen have three personas, a marketplace dynamic, and a pricing model still being invented in week eight. We fix scope in writing before the first commit and demo working software every Friday, so if the answer is drifting you see it by week two, not week ten. Speed here is a discipline, not a heroic sprint.

Everyone says AI wrappers have no moat. What actually makes an AI product defensible at MVP stage?

At MVP stage, defensibility is mostly a story about data and workflow, and the honest version is thinner than pitch decks imply. What you can genuinely build early: a workflow position (the product owns the place where work happens, not just a chat box beside it), proprietary evaluation data from real usage that makes your quality measurably better on your niche, and integration depth that makes switching annoying. What you cannot buy at this stage is a model advantage; the labs own that. Our build choices follow: instrument everything (usage becomes training and eval data you own), go deep on one workflow rather than wide on capabilities, and treat the eval set as a company asset from day one. The moat grows from operations, not architecture.

What does a smaller scope buy versus a larger one?

At the smaller end: one core workflow built end to end on our standard stack, standard auth, Stripe billing with simple tiers, one AI feature with basic metering and a starter eval set, deployed to production with monitoring. At the larger end: multi-role products, usage-based billing with entitlements, deeper AI work (routing across models, retrieval over customer data, a real evaluation harness in CI), two or three integrations, and the enterprise-leaning scaffolding (SSO-ready auth, audit logs) that lets a design partner's security review say yes. The middle of the range is most SF builds: one strong workflow plus one serious AI capability plus billing that matches how the product creates cost. We quote after a one-hour scoping call, and the quote itemizes what moves the number.

What happens after launch? We will hire engineers eventually but not immediately.

The handoff is designed for that gap. Every build ships with a runbook (deploys, environments, common failures, escalation paths), an architecture document explaining the decisions while they are still fresh, and a recorded walkthrough your future hires watch on day one. The code itself is boring on purpose: standard Next.js patterns, typed end to end, no clever abstractions that need their author present. For the gap itself, most founders take a maintenance arrangement covering dependency updates, monitoring response, and small fixes, cancelable monthly once your team is seated. When you do hire, we do a handoff session with the new engineer and then get out of the way. The deliverable is a codebase your hire respects, not one they campaign to rewrite.

Ready to scope your MVP?

Tell us the workflow, the AI capability, and the date it needs to be real. We reply within one business day with a rough scope and a fixed price range.