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
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