Rescue Audit · fixed price · five business days
You have an app that exists but is not shipping: a prototype built with Lovable, Bolt, Replit, v0 or Cursor, a Bubble app that has hit its limits, an AI feature that works on good days, or a build another team left half-finished. In five business days we read the code and the infrastructure, test the parts that matter, and hand you a written report: what is sound, what is dangerous, what it costs to fix, and whether to fix, finish or rebuild. $2,900, fixed, credited to the work if we do it.
AI app builders and no-code platforms are very good at the visible part of software: screens, forms, a working demo. The parts that decide whether an app can take real users are invisible in a demo — who is allowed to read which rows, whether a webhook can fire twice, what happens when the model times out, where the API keys live, whether anyone can rebuild the environment from scratch. Those are the parts that fail first, and they fail quietly.
The risk is not theoretical. In May 2025 a CVE (CVE-2025-48757) was published for sites generated with Lovable up to April 2025, describing insufficient database row-level security that allowed unauthenticated users to read or write application tables. The platform disputed it on the grounds that each customer is responsible for protecting their own application's data. Whichever side you take, the responsibility lands with the owner of the app, which is why the audit starts with access control.
A rescue audit is the step before spending money on fixes: an engineer who did not build the app reads it the way an acquirer or a senior hire would, and writes down what they find.
Six areas, each written up with specific findings rather than a checklist tick.
Authentication, authorisation and row-level policies; endpoints reachable without a login; secrets in client bundles or repositories; dependency vulnerabilities; what a logged-in user can see that they should not.
Schema and migrations, multi-tenant boundaries, constraints and indexes, backups and restore, and whether the data model can carry the next year of features.
Stripe or in-app billing state, webhook handling and idempotency, subscription edge cases, CRM and email syncs, and how third-party failures are handled.
Prompt and model handling, structured outputs, evaluation coverage, cost per request, rate limits, and what the user sees when the model is slow, wrong or unavailable.
Hosting, environments and configuration, CI and deployment, error tracking and logs, and the monthly bill for what is running.
Whether you own and can rebuild everything, test coverage on the flows that matter, documentation, dependency currency, and how hard it is for a new engineer to contribute.
A 45-minute call to learn what the app does, who uses it and what worries you, then read-only access to the repository, hosting and a staging or redacted database. We sign an NDA first if you want one.
Static review of the code and configuration, hands-on tests of sign-in, permissions, billing and the main workflows, and a sample evaluation of any AI feature against real inputs.
Findings rated by severity, the architecture as it actually is, and the recommendation — fix, finish or rebuild — with a fixed-price estimate for the next step.
A call to go through the report, answer questions and agree what happens next. Anything urgent, such as a leaked key, is reported the day we find it rather than at the end.
If you ask us to do the work and start within 30 days, the audit fee comes off the price. If you take the report to your own team or another vendor, it is written to be usable without us.
Everything is written down; nothing lives only in a call.
Each issue with a severity (ship-blocker, risk or improvement), where it is, why it matters and what fixing it involves.
The components, services and integrations as they exist today, with the fragile joints marked.
A recommendation with the reasoning, including what to keep. Rebuilding is rarely the answer, and we say so when it is not.
A quote for the next step, scoped to the findings, so the report leads to a decision rather than another conversation.
The short list of things you or your team can change the same day, such as rotating exposed keys or closing a public endpoint.
45 minutes with the engineer who did the review, recorded if you want it.
One fixed fee, paid into Upwork escrow and released when you have the report.
A five-day review of a stalled, AI-built or no-code app, with a written plan to fix it, finish it or rebuild it.
$2,900
5 business days · fee credited to the follow-on work
Users are signing up, but nobody has checked whether one customer can read another's data, whether the Stripe webhooks are safe to retry, or where the keys are. The audit answers those questions before growth makes them expensive.
Workload costs rising, billing edge cases handled by hand, and investors asking about the stack. Bubble cannot export code, so the review covers the data model, privacy rules, workflows and integrations, and what a migration would keep.
The chatbot or agent is live, but support still double-checks it. We measure it against real inputs, look at how it fails and what each request costs, and tell you what it would take to make it dependable.
The repository is there, the documentation is not, and the last deploy was done by someone who has left. We work out what exists, what runs and what it would take to carry it forward.
The fixed fee covers one application with one backend and up to three third-party integrations, which is most prototypes and no-code apps. Larger estates, several services, or a mobile app plus a web app are quoted after a short call.
The audit is a code, configuration and architecture review with hands-on testing of the flows that matter. It is not a penetration test or a compliance certification, and we do not fix things during the five days beyond flagging emergencies. Fixes are quoted separately, and the audit fee is credited if you start within 30 days.
The signs you have outgrown Bubble, what you can and cannot take with you, and a step-by-step plan for moving live users, data and Stripe subscriptions to a coded stack.
The gap between an AI demo and a feature you can ship: evaluation sets, judges you can trust, structured output, permissions, cost tracking, fallbacks and a gradual rollout.
The engineering playbook we use to ship LLM features inside existing products: what to build first, the architecture defaults, tenancy, privacy and rollout.
Plain definitions, a side-by-side comparison and a decision table — plus why most good "AI agents" are mostly ordinary automation with AI in two or three steps.
Tell us what you are building or what needs fixing. You will hear back from an engineer — not a sales rep — within one business day, usually with questions or a rough approach.
Your enquiry goes straight to our engineering team — the people who will scope and build your project, not a sales desk.
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