CTO advisory playbook: MVP to production in 90 days

Here’s the 90-day, CTO-level plan I use to take teams from scrappy MVP to production-grade: opinionated where it matters, measurable at every step, and relentlessly focused on database design and optimization, Tailwind CSS UI engineering, and generative AI product development that doesn’t implode at first traffic spike.

Days 0-7: Alignment and architecture decisions

Set constraints early so choices compound, not collide.

• Define business outcomes and SLOs: p95 latency under 200 ms, 99.9% uptime, cost ceiling per user, and a crisp “not building” list.
• Choose a boring, proven core: Postgres, Node/TypeScript, Next.js, and a cloud you know. Favor managed services; reserve Kubernetes for multi-team scale.
• Sketch the domain model with aggregate boundaries; design tables around write paths, not dashboards. Start with tenants, users, permissions, events, and audit logs.
• Pick the AI pattern now: retrieval-augmented generation with pgvector, or fine-tuned APIs. Decide grounding sources, chunking, and private eval datasets.
• Security baseline: SSO, least-privilege IAM, encrypt PII with application keys, and vendor DPAs. Threat-model prompts and embeddings like any untrusted input.
• UX posture: adopt Tailwind CSS UI engineering with design tokens, a spacing scale, and component conventions before anyone writes a div.

Days 8-30: Build an MVP that isn’t throwaway

Deliver the smallest vertical slice that can survive production chaos.

• Database design and optimization first: encode constraints, add composite primary keys where lookups demand it, and write one migration per change with idempotent seeds.
• Index for reality. Example: orders(tenant_id, created_at desc) for recent queries; partial indexes on status where active=true; avoid wildcard prefix searches.
• Partition by tenant or time if rows surpass ~50M in 12 months. Use pgbouncer and connection pooling before you touch read replicas.
• Tailwind CSS UI engineering: set tokens in config, enforce via ESLint and Stylelint, document components in Storybook, and ship a11y-clean defaults.
• Interaction quality matters: optimistic updates, skeletons, and latency budgets per view; aim for Largest Contentful Paint under 2.5s on median devices.
• AI slice: define a contract. Input schema, grounding store, prompt template with variables, and response schema. Log every token, vector hit, and latency.

Days 31-60: Scale readiness and AI productization

Prove the system meets budgets before you market it.

• Load-test with k6 or Locust at 3× expected peak; fail fast using autoscaling off. Keep p95 under target without caches, then add Redis.
• Tighten queries using EXPLAIN ANALYZE; kill N+1s; add partial and covering indexes; materialize read models where UX demands instant lists.
• Vector strategy: store embeddings with pgvector, chunk by semantic units, and dedupe via MinHash. Build offline evals with golden answers and pass@k targets.
• Guardrails: prompt sanitization, PII redaction in context, and allow/deny tools for function-calling. Provide a “why” trace to every AI output.
• Observability: OpenTelemetry traces, structured logs, RED metrics, slow query dashboards, and prompt+embedding analytics side by side.
• Security posture: enforce RLS per tenant, rotate secrets, run SAST/DAST in CI, and rehearse incident response with a 30-minute mock outage.

Days 61-90: Production hardening and launch ops

Now you freeze scope, pressure-test the runway, and make on-call boring.

• CI/CD: trunk-based flow, preview apps per PR, canary to 5%, blue/green for major schema shifts, and automated rollback within two clicks.
• Backups and migrations: nightly logical backups, weekly restores to a staging clone, zero-downtime migrations using expand-migrate-contract.
• FinOps: tag everything, set budgets and anomaly alerts, model unit economics per tenant, and load-test cost at 10× to find cliffs.
• Performance budgets: CSS under 150 KB, JS under 250 KB, purge unused Tailwind, prefetch critical data, and measure TTI against a reference Android.
• Support readiness: SLAs, runbooks, pagers, and dashboards per service; tie errors to Jira; announce a public status page before GA.

Case study: B2B analytics SaaS in 11 weeks

We shipped a tenant-aware reporting MVP in 26 days, then hardened for GA by week 11.

• Database: Postgres with RLS, partitioned events by month and tenant_id, JSONB for flexible dimensions, and a CQRS read model for dashboards.
• Optimization wins: killed a 900 ms join via a covering index and replaced a hot aggregate with a nightly materialization.
• Tailwind CSS UI engineering: a tokenized theme, table virtualization for 50k rows, and a11y-first modals with focus traps and ESC handling.
• Generative AI product development: RAG over customer docs with pgvector, embeddings cached per version, eval suite targeting 85% grounded answers.
• Outcomes: p95 140 ms, 99.95% uptime first month, $0.23 gross margin per dashboard view, and NPS +24 at GA.

Team, cadence, and partners

Staff lean: fractional CTO, tech lead, two full-stack engineers, one MLE, and slashdev.io for surges support.