Solutions
Audit formats built around how your platform actually grows.
Not every platform needs the same depth of review. We work in four formats, scoped to where a team is in its growth and what's driving the need for a closer look.
Focused Review
A single-layer deep dive, such as the payment path or the queue architecture, when a team already has a specific concern in mind.
- One infrastructure layer
- Short engagement window
- Targeted findings document
Full Infrastructure Audit
The complete six-layer review, covering database, caching, queues, payment paths, third-party dependencies, and edge routing.
- All infrastructure layers
- Full findings report
- Prioritized risk summary
Pre-Peak Readiness Check
A compressed review ahead of a known high-traffic period, such as a seasonal sale or a major product launch.
- Time-boxed to your calendar
- Focus on likely stress points
- Short-form action list
Ongoing Advisory Check-ins
Periodic follow-up sessions after an initial audit, to see how findings were addressed and whether new risk has emerged.
- Quarterly or biannual cadence
- Builds on prior findings
- Lightweight documentation
Deep dive
Payment path reviews
Payment flows sit at the intersection of user experience, third-party dependency, and financial accuracy. A review here traces how a transaction moves from checkout button to confirmed charge, and what happens at every point where it could stall.
We look closely at retry behavior. A processor that times out once every few thousand requests is normal. What matters is whether the retry logic creates duplicate charges, silently drops the transaction, or queues it in a way nobody monitors.
We also check idempotency keys, webhook handling for asynchronous payment confirmations, and how the system behaves if a processor's API responds slowly rather than failing cleanly.
Deep dive
Queue and async job reviews
Message queues and background workers tend to fail quietly. A job that stalls doesn't throw an error on a customer's screen; it just sits there, sometimes for hours, until someone notices a backlog.
We review how queues handle backpressure when producers outpace consumers, whether dead-letter handling exists for jobs that repeatedly fail, and how worker pools scale when volume spikes. We also check monitoring: is there any alert that fires before a backlog becomes a customer-facing delay?
Not sure which format fits?
Describe your platform and what's prompting the review. We'll suggest a format that matches the scope, without pushing you toward the largest option by default.