What’s included in an Architecture Audit
The shape of a system decides what it can survive. We map service boundaries, data flow, and coupling as they actually are, then say plainly which of them hold at ten times the current load while changing them is still cheap.
- Service boundaries, data ownership, and coupling mapped as they actually are
- Automated analysis across the codebase and infrastructure config
- Senior engineer verification of every finding, with false positives cut
- Architecture risk mapping with business impact assigned to each issue
- Prioritized fix roadmap, sequenced and walked through live
Stacks We Review
- JavaScript/TypeScript, Node.js, React, Next.js.
- Ruby on Rails, Python.
- Cloud infrastructure and containerized environments.
- Monoliths, modular monoliths, and service-oriented systems alike.
Scoped on application
See all pricingWhere Structure Fails
The structural problems that survive automated scanning, because spotting them takes architectural judgment rather than a rule.
Premature service splits
Codebases split into services with no clear domain ownership or bounded contexts, calling each other in tight synchronous chains. Deployment complexity with none of the scaling benefits.
Query patterns that collapse under load
One query per item on a core endpoint means 51 database round-trips per request at 50 items a page and 14-second responses at 200 concurrent users.
Synchronous work on the request path
Email, PDF generation, webhook delivery, and third-party calls inside request handlers. No queue, no retries, no backpressure so failure rate rises non-linearly under load.
Services sharing one database
Services that own no data of their own, reaching into the same tables. The split looks like separation on the diagram; the coupling underneath is unchanged, and a single failure cascades across the request chain.
Load boundaries assumed, never proven
A design that carries the first hundred users with no evidence it carries ten thousand. The limit is assumed at build time and discovered in production.
Effort aimed at the wrong layer
Caching bolted on where indexing was missing. We find the layer the bottleneck actually lives in, so the work lands where it changes something one review traced the whole problem to a single service.
How it works
Map
We chart the system as built boundaries, dependencies, and data paths before deciding where to look hardest.
Stress
Structure and data access analysed at machine speed, then senior review of everything that survives the first pass.
Sequence
An architecture risk map ordered so the fixes unblock each other instead of competing for the same sprint.
Frequently Asked Questions
Clear, straightforward answers about scope, security, and what happens after the audit.
Yes. Vibecop works with private repositories and sensitive production systems under secure review workflows and NDA-friendly processes.
Build with
confidence.
AI builds the product. Vibecop makes sure it won’t break in production, fail under scale, or expose your users to risk. One audit. Fewer expensive surprises.