Startup Software Development

The smallest product that puts real functionality in front of real users and generates real learning, not a clickable prototype, but a working system with a database, actual user accounts, and core data flows that work end-to-end. Typically 2-3 user flows that test the core value proposition: the search-and-book flow, the post-and-respond flow, the upload-and-process flow, whatever the heart of your product is. Essential data model designed to support the MVP use cases without over-engineering for scale you don't have yet. Deployed and accessible in 8-12 weeks. The product you hand to your first 50 users to validate whether the problem you're solving is real and whether your approach to solving it is the right one, before you spend more time and money building features for assumptions you haven't tested.

Full-stack web applications built with React or Next.js for the frontend and Node.js (Express, NestJS), Python (FastAPI, Django), or Go for the backend, language and framework chosen for your specific requirements, not default preferences. PostgreSQL or MySQL for relational data, Redis for caching and real-time features, S3 for file storage. Deployable on AWS (ECS, Lambda, RDS) or Vercel + PlanetScale for modern serverless architectures. Authentication via Clerk, Auth0, or custom JWT depending on your multi-tenant and SSO requirements. From SaaS dashboards to marketplace platforms to internal operations tools, built with an architecture where adding a new feature in month 6 doesn't require refactoring what was built in month 1.

iOS and Android mobile apps built with Flutter for cross-platform deployment from a single Dart codebase, reducing development time by 30-40% vs. building native apps separately when platform feature parity is the requirement. Native Swift/SwiftUI (iOS) or Kotlin/Jetpack Compose (Android) when the use case requires deep platform integration: HealthKit, ARKit, background Bluetooth, or hardware-specific sensor access that Flutter's plugin ecosystem doesn't cover reliably. We've built food delivery apps with real-time tracking, booking platforms with complex availability logic, IoT dashboards connected to hardware devices, and AI mobile tools processing on-device inference. Push notifications, offline-first architecture, in-app purchases, and App Store / Play Store launch included.

AI-native products built with production engineering, not just API wrappers: LLM integrations using OpenAI (GPT-4o, GPT-4o mini) or Anthropic (Claude Sonnet, Haiku) with streaming, tool use, and structured output; RAG systems with Pinecone or pgvector for document and knowledge retrieval grounded in your data; and AI agent workflows using LangGraph for multi-step automation that requires planning and tool use. Evaluation pipelines built alongside the product so you can measure quality before launch and detect regressions after. We've shipped AI products for healthcare diagnostics, legal document review, financial analysis, and operational automation, 20+ AI systems in production. PSi's voice AI platform supports 300+ concurrent users; Perceptional's research chatbot delivers insights 4x deeper than traditional survey methods.

Multi-tenant SaaS platforms built with the architecture decisions that determine your product trajectory for the next three years: tenant isolation strategy (shared schema with tenant ID vs. schema-per-tenant vs. database-per-tenant), subscription billing via Stripe Billing with trial periods, proration, usage-based components, and dunning sequences, feature flags (LaunchDarkly or Unleash) for controlled rollout and plan-based feature gating, admin tooling that lets your team manage customer accounts without engineering involvement, and a REST or GraphQL API if your product needs to integrate with your customers' systems. B2B and B2C SaaS models handled differently, B2B requires organization/team constructs, RBAC within organizations, SSO via SAML, and audit logging; B2C requires individual account models, self-serve upgrade flows, and payment method management.

Two-sided marketplace platforms where the core product is the matching of supply and demand: buyer and seller onboarding flows with verification and trust mechanisms, listing creation and search with filtering and ranking, booking or transaction flows with availability management and conflict prevention, payment routing with Stripe Connect (split payments, automatic payouts to sellers, commission deduction), dispute and refund workflows, and review systems. On-demand platforms add real-time dispatch: driver or provider assignment, live location tracking via WebSocket, and status updates pushed to all parties. We've built food delivery platforms with 30% reduction in order processing time, booking systems with 3x direct booking conversion improvement, and B2B marketplaces connecting enterprise buyers with specialized suppliers.

We don't start writing code until the scope is clear. Discovery session to understand the user problems, wireframes to validate the flows, and a defined scope document before the fixed cost is set. What's in scope and what's not in scope is documented before development starts.

Feature prioritisation uses MoSCoW (Must-have, Should-have, Could-have, Won't-have) to separate the core value proposition from the nice-to-haves. A two-sided marketplace has a Must-have set of roughly 8-12 features and a long tail of 30+ that wait until you have traction data to justify building them. We walk every founder through this exercise because the natural instinct is to build the full vision. Scope creep kills startup timelines faster than any technical problem. Wireframes are built before a line of code is written, Figma flows covering the critical user journeys, reviewed with you before the engineering estimate is set. The wireframe-to-code workflow means your frontend engineers aren't making UI decisions; those decisions are made and approved first, then implemented. This single step prevents the most common source of mid-project rework: designs that work on paper but fail in use.

We quote a fixed cost based on defined scope. The cost doesn't change unless the scope changes, and if you want to change scope mid-project, we tell you the cost impact before proceeding. No hourly billing, no surprises at invoice time.

The standard stack for a seed-stage web product: Next.js or React frontend, Node.js (Express or NestJS) or Python (FastAPI) backend, PostgreSQL as the primary database, Redis for session management and caching, S3-compatible object storage for file uploads. Deployed on Vercel (frontend) plus Railway or Render (backend and database) for rapid iteration without DevOps overhead, both platforms offer one-command deployments and environment variable management out of the box. For payment collection, Stripe Checkout or Stripe Billing handles subscription and one-time payment flows, including webhooks for fulfillment triggers. Feature flags via LaunchDarkly or open-source Unleash let you gate new functionality by user cohort or plan tier without deploying new code. Error tracking via Sentry catches runtime exceptions in production with full stack traces and session replay context. Product analytics via PostHog, Mixpanel, or Amplitude (depending on your self-hosted vs. cloud preference) tracks the funnel events that tell you whether users are reaching the aha moment. This stack is battle-tested, well-documented, and hireable, your future engineering team will know it.

We build in sprints with deployed builds at the end of each. You see working software every two weeks, not a completed product at the end. If something isn't right, we catch it early when changes are cheap, not at launch when they're expensive.

Sprint velocity is tracked and shared: how many story points were committed, how many were completed, and what changed if there was a gap. Every sprint ends with a demo of the working build, not a status update, but a live walkthrough of new functionality in a staging environment you can access and test yourself. Performance monitoring via Datadog or a lightweight alternative like Better Uptime goes live from the first deployment so you have visibility into response times and error rates from the start, not retrofitted after a production incident. For AI-first products, evaluation runs are part of each sprint: prompt changes and retrieval modifications are validated against the quality baseline before the sprint is closed. By the time you reach launch, the product has been tested in a real environment for weeks, not hours. This matters for seed/Series A readiness: investors asking about your product's performance metrics expect you to have data, not anecdotes.

You own the repository, the infrastructure, and the code from day one. We deploy to your accounts, AWS, Vercel, or wherever, and you have full access throughout. When the project ends, nothing leaves with us.

The repository is yours on GitHub or GitLab under your organisation account from the first commit. Infrastructure is provisioned under your AWS, GCP, or Vercel account, not ours. All third-party service credentials (Stripe, SendGrid, Twilio, Sentry, PostHog) are registered to your accounts. We do not use shared API keys or shared infrastructure that would create a dependency on us to operate your product. The codebase is documented: a README covering local setup, environment variable descriptions, database migration instructions, and deployment procedures. The architecture decisions that affect your future engineers, why PostgreSQL over MongoDB, why the multi-tenant model was built as schema-per-tenant rather than shared schema, are recorded in ADRs (Architecture Decision Records) in the repository. When you hire your first engineer, they can be productive without a week of knowledge transfer from us. This is how we've built 100+ products since 2019 that founders could hand to their own teams without friction.