MVP Development

SaaS product MVPs with multi-tenant architecture from the first line of code -- PostgreSQL with row-level security or schema-per-tenant isolation, so the first paying customer uses the same architecture as the hundredth rather than requiring a rebuild when you try to add a second customer. Authentication via NextAuth.js (email/password, Google, GitHub OAuth) or Clerk for a hosted auth solution that includes user management UI. Stripe Billing integration: monthly and annual plans, free trial with card capture, subscription lifecycle events handled via webhooks (checkout.session.completed, invoice.payment_failed, customer.subscription.deleted). Team management: invite by email, role assignment (admin/member), seat-based access control. Onboarding flow designed for activation: the steps between signup and the user completing the core action for the first time, with email sequences triggered at each activation milestone. Admin panel for your team: user list, plan management, usage metrics, and impersonation for support. Deployed to Vercel (frontend) and Railway or Render (backend) with a staging environment that mirrors production. From authentication to core feature to admin panel, scoped and priced as one fixed-cost engagement.

Mobile MVPs in React Native (Expo managed workflow for speed; bare workflow where native modules are required) for iOS and Android from a single codebase. React Native with Expo Router for file-based navigation, Zustand or Jotai for state management, and React Query for server state with offline cache persistence. Firebase Cloud Messaging for push notifications on both platforms with a single API. Expo's OTA (Over-the-Air) updates allow bug fixes and non-native code changes to be deployed without a new App Store submission -- reducing time-to-fix for early-stage post-launch issues. Where platform-specific capabilities are required (ARKit, HealthKit, Android NFC), native modules bridge to the platform SDK. App Store and Google Play submissions handled end-to-end: developer account setup, privacy policy and data disclosures, screenshots, and review navigation. TestFlight (iOS) and Play Internal Testing (Android) configured for beta distribution before public launch. Backend scoped alongside the mobile app -- authentication, data persistence, push notifications, and the business logic the app needs -- no assumption that an existing backend exists or needs to be extended.

B2B product MVPs for tools sold to other businesses -- where the buying decision is made by someone other than the end user and the demo matters more than the marketing site. Multi-user accounts with team permissions: workspace-based access so organisations buy one subscription and manage seats internally. CSV import with client-side parsing (PapaParse) and server-side validation with clear error messages per row -- because B2B buyers bring their own data on day one and a bulk import that takes 45 minutes is the first support ticket you'll get. Slack webhook integration for activity notifications because B2B buyers want updates in their existing communication channels, not a new app to monitor. Zapier or webhook endpoint for connecting to other business tools without requiring custom integrations per customer. Audit log so enterprise buyers can answer "who did what and when" during security reviews. SSO readiness: even if SAML SSO isn't in the MVP, the authentication model (NextAuth with a pluggable provider system) supports adding it in the next phase without a re-architecture. The B2B MVP that looks like a real product to a procurement evaluation rather than an early-stage experiment, with the commercial credibility needed to close the first paying customers.

Backend-first MVPs for products where the core value is in the data, logic, or integration layer rather than the UI -- APIs that other systems consume, data processing pipelines, and integration hubs that technical buyers evaluate by connecting to rather than clicking through. Node.js with Fastify or FastAPI (Python) depending on the use case: Fastify for high-throughput REST APIs with strong TypeScript typing and schema validation via JSON Schema; FastAPI for Python-heavy use cases (ML model serving, data processing) with automatic OpenAPI documentation generation. Authentication via JWT (RS256) with API key authentication as an alternative for machine-to-machine integrations. OpenAPI 3.0 specification generated from the code (Fastify via @fastify/swagger; FastAPI auto-generates) so documentation stays in sync with the implementation rather than drifting. Rate limiting from day one: token bucket algorithm with per-key limits and standard RateLimit-* response headers. PostgreSQL for relational data with Prisma ORM for type-safe queries and migration management. The API MVP that a technical buyer can connect to in a day, evaluate against their real use case, and approve a paid integration without a support call.

MVPs where AI is the core product -- not a chatbot widget added to a conventional application, but systems where the LLM, classification model, or document processing pipeline is the primary value proposition. LLM integration via OpenAI GPT-4o or Anthropic Claude using structured outputs (OpenAI's response_format: { type: "json_schema" } with strict: true) so the model produces typed data rather than free-form text that requires parsing. RAG (Retrieval-Augmented Generation) pipeline for MVPs that need to answer questions against a proprietary knowledge base: document ingestion into pgvector (PostgreSQL extension) or Pinecone, semantic search at query time, context injection into the LLM prompt. LangChain or a direct API integration depending on whether the abstraction adds value for the specific use case. Streaming responses via Server-Sent Events so users see output incrementally rather than waiting for the full LLM response (typical improvement from 4-8s wait to first visible token in under 1s). Cost controls: token usage logging per request, per-user monthly limits, model selection based on complexity (GPT-4o-mini for classification tasks; GPT-4o for reasoning-heavy queries). The AI MVP that demonstrates real capability against a defined use case -- not a demo scripted to avoid edge cases.

Structured post-launch development based on actual usage rather than continued assumption. Product analytics instrumented from day one using PostHog (self-hosted or cloud) or Mixpanel: funnel analysis to identify where users drop off (the 40% who complete signup but never return usually drop at a specific feature that doesn't work as expected), feature adoption heatmaps, and cohort retention curves that show whether different user segments retain differently. Error monitoring via Sentry configured at launch with source maps and release tracking -- so every uncaught exception is attributed to a specific commit, user session, and code path rather than surfacing as a vague support ticket. Feature flags via LaunchDarkly or Unleash enable controlled rollout of second-phase features to a percentage of users before full release, and allow instant kill-switch without a deployment if a new feature causes unexpected behaviour in production. Feature requests from early customers categorised by frequency, revenue impact, and implementation cost -- not everything the loudest customer asks for is worth building. Architecture review against the next-phase requirements: the decisions appropriate for an MVP (simple queue instead of Kafka, single-process deployment instead of microservices) may not hold at 10x the user count; the review identifies which ones need replacing before they constrain growth. For founders approaching seed funding, we prepare the architecture documentation, codebase handoff package, and metrics export that technical due diligence teams ask for: database schema, API documentation, infrastructure diagram, test coverage report, and the dependency audit showing no critical CVEs. Second phase scoped and priced as a separate fixed-cost engagement based on what the data shows rather than what was anticipated at the start.