30/60/90 startup scaling plan
A sample deliverable format showing the structure and specificity of a typical 30/60/90 engagement. Every example here is sanitized and NDA-safe by design.
context
Client profile: [Redacted] C-Corp
Seed to Series A product team with early traction, aiming to scale from prototype to production while controlling infrastructure costs and reducing security risk.
Primary objective: ship a production-grade system and weekly execution cadence that scales cleanly from 0 to 100K MAU without emergency migrations.
Inputs needed (minimal)
- Access to staging logs and metrics, or sanitized exports
- Database schema snapshot or ERD, even a rough one
- Top endpoints by traffic
- Monthly spend breakdown by category
- Threat model constraints: payments, uploads, OAuth, webhooks, admin
summary
The constraint we are solving
- Hot-path latency with no instrumentation
- Cost spikes from runaway queries, egress, and invocations
- Data model debt, so analytics and discovery become guesswork
- Auth and session risk: OAuth edge cases, cookie flags, CSRF
- Shipping stalls with no repeatable weekly process
90-day outcomes: what done looks like
- Stable foundation: Postgres system of record, clear boundaries, predictable deploys
- Cost governance loop: drivers identified, budgets set, optimizations with measured ROI
- Discovery inputs: event schema plus aggregates for ranking and experimentation
- Security baseline: hardened auth and session surfaces with regression checks
- Operator cadence: weekly plan, ship, review loop with measurable progress
actually get
- Constraint map plus 30-day action plan: top levers with evidence
- System map, current to target: services, boundaries, data flow
- Backlog plus milestones: scoped phases and sequencing
- Cost driver report: top spend categories plus mitigation plan
- Security baseline: auth, OAuth, sessions, abuse controls
- Delivery estimate ranges, so leadership can plan
plan
Intake and baseline
Goal: establish reality — what is fragile, expensive, and risky — and define success metrics.
- Architecture inventory and hot paths
- Baseline p95 latency, error rates, spend drivers, and security surfaces
- Decide guardrails: speed versus cost versus quality
Stabilize and make it measurable
- Observability foundation: logs, metrics, alerts
- Data model sanity and migration discipline
- Cost governance loop and quick wins
- Security baseline hardening: auth, redirects, rate limits
- Weekly shipping cadence with a decision log
Build the growth-ready core
- Events and analytics pipeline, lightweight and real
- Discovery v1: heuristics first, measurable outcomes
- Release process, rollback flow, runbooks
Scale, de-risk, automate
- Load testing and capacity planning
- Budgets and a cost-per-active-user model
- Security regression workflow
- Documentation that gets used: debug map, runbooks