Overview
A successful MVP proves the idea. Version 1.0 proves the business. We take your prototype — whether built with us or elsewhere — and harden it for real customers: scalable architecture, robust error handling, production-grade AI pipelines, and the feature set that moves you from demo to paying users.
Why this matters
The leap from MVP to V1.0 is where most AI products die — not because of AI, but because of everything around it: auth, permissions, multi-tenancy, audit logs, billing, compliance, observability. We've seen teams burn 6 months rewriting an MVP that could have been hardened in 6 weeks if the right patterns had been in place from the start.
How we run it
Audit & Prioritize
Week 1: production readiness audit across architecture, security, AI reliability, and UX. Rank gaps by customer impact and shipping risk.
Harden the Core
Weeks 2–4: multi-tenant data isolation, auth flows, permission models, error handling, retry logic, and observability baked in.
Ship the Conversion Features
Weeks 5–8: onboarding, activation, admin tooling, reporting — the features that turn paying users into loyal ones.
Scale & Monitor
Weeks 9–12: load-test AI pipelines, tune costs, set up production monitoring (Datadog, Sentry, PostHog), and document everything.
What you get
- Architecture review and hardening for production scale
- Feature completion based on validated user feedback
- AI pipeline optimization — latency, cost, and quality
- Security, compliance, and data handling review
- Onboarding flow and user experience refinement
- Staging and production environment setup
Our technology choice
We design for the scale you'll hit in 18 months, not 18 weeks. Typical stack: Next.js + Node/FastAPI + Postgres + managed vector DB + Kubernetes or serverless (based on your scale curve). We avoid exotic tech unless the product demands it.