For Existing Projects

Technical Discovery Report — Illustrative Sample

This illustrative example uses a fictional client, 'Acme Booking,' a Next.js scheduling app. It shows the structure PiFlow uses for a Technical Discovery report; no real client data is included.

In this resource

  • A real report structure: findings, risks, and a prioritized plan
  • How risks are ranked by severity and effort
  • What a production-grade sign-off looks like in writing

Executive Summary

Acme Booking is functional and serving real users, but accumulating structural risk that is slowing delivery. The application can be stabilized without a rewrite. We recommend a focused two-week stabilization sprint addressing the three highest-risk findings below before resuming feature work.

Overall health: Fragile (12/25 on our internal checklist). The biggest risks are concentrated in deployment reliability and the billing path.

Findings — Ranked by Risk

Each finding is rated High / Medium / Low by severity (impact if it fails) and effort (cost to fix).

  • HIGH severity / LOW effort — No smoke test on checkout. A broken deploy can take payments offline silently. Fix: add an automated test on the book-and-pay flow gating deploys. (~0.5 day)
  • HIGH severity / MEDIUM effort — Authorization checked only on the client. A crafted request can read another tenant's bookings. Fix: enforce tenant scoping in server route handlers. (~2 days)
  • MEDIUM severity / LOW effort — Environment variables undocumented and unvalidated; new environments fail unpredictably. Fix: add startup validation and a documented env template. (~0.5 day)
  • MEDIUM severity / MEDIUM effort — Booking logic duplicated across three files; bug fixes don't propagate. Fix: consolidate into one module with tests. (~2 days)
  • LOW severity / LOW effort — Lint and type errors are ignored in CI, masking real issues. Fix: make them blocking. (~0.5 day)

Architecture Assessment

Data flow is mostly server-rendered, which is good. The weak point is that authorization decisions are made in client components and merely assumed on the server. The deployment pipeline works but has no safety net — there is no automated check between 'merge' and 'live.'

Nothing here requires re-platforming. The foundations are sound; the gaps are in guardrails and consistency.

Recommended Plan

We recommend addressing findings in risk order within a single two-week stabilization sprint, deploying after each fix so the app stays live throughout.

  • Week 1: smoke test on checkout, server-side tenant scoping, env validation.
  • Week 1–2: consolidate booking logic with test coverage.
  • Week 2: make lint/type checks blocking; document the deploy + rollback path.
  • Hand-off: written summary of what changed and what remains low-priority.

Production-Grade Sign-Off

A real assessment is reviewed against PiFlow's production-grade checklist using direct codebase evidence rather than an automated score alone. Recommendations are specific, prioritized, and paired with a rough effort estimate so the client can make a budget decision with clearer trade-offs.

The goal of this report is clarity: you should finish it knowing the three things to fix first, roughly what they cost, and what you can safely defer.

Continue with the right path

Related guides

Want a report like this for your own codebase? A $499 Technical Discovery delivers a production-grade audit, written report, and walkthrough call in 1–3 days.

See Technical Discovery pricing

Email me this resource

Prefer a copy in your inbox? We'll send this public link. Occasional PiFlow updates remain optional.

We'll email the resource link. Future PiFlow notes are optional.

Do not enter passwords, API keys, payment card numbers, health information, or private customer data.