AI build rescue
An AI-generated app or an AI agent build.
A clear way forward.
We review the code, prompts, provider settings, and running system without changing anything. You receive a ranked report and a fixed price for stabilization.
For owners whose AI-generated app or AI agent build needs an honest assessment and a practical fix.
What you get
Understand the build before changing it.
The read-only audit covers source code, prompts, provider calls, data boundaries, action limits, and live behavior. You receive a ranked report and a fixed price for stabilization. The audit fee comes off the price of fix work if you continue.
A read-only look at live use
We examine the deployed app without changing production data or interrupting normal work.
When a model call fails
We check how the app handles a timeout or provider error and whether it retries without repeating an action.
The account boundary
We test whether the app keeps one account's information out of another user's request before sending context to the model.
I had a working demo, but now it fails.
The demo covered the inputs someone expected. Routine use brings different records, provider responses, and failures the original run never exercised.
I know the agent worked last week, but now it fails.
Model versions, prompt files, connected APIs, and data can change how an agent answers. If run logs do not preserve the model and prompt version with each request, you have little evidence to compare with last week's result.
I cannot explain code I now depend on.
The app supports daily work, but its data flow and the code path behind an important operation are hard to trace. That makes a routine change harder to scope.
I fix one thing and break another.
A change in one flow can expose an assumption another flow shares. When tests cover only the happy path, a repair can break a nearby workflow after release.
I cannot tell what one agent run costs.
Token use changes with each request and model. A monthly provider bill shows total spend, but a run log connects that spend to a task.
I worry the agent will tell a customer the wrong thing.
An answer can sound certain while falling outside your policies or relying on an unsupported detail. If the system cannot hand requests outside its approved scope to a person, a customer may receive a reply the business never approved.
01 Try to override the agent's instructions
Start a test conversation with external actions off. Ask the agent to ignore its instructions. A refusal or handoff in the saved evaluation shows the boundary held. Any other answer requires review.
02 Check each action that can spend money or delete data
Inspect a connected tool that can spend money or delete data. Find its written limit or required approval. If you find neither, the tool can act without review.
03 Try to read another user's record
Use a test account to request another user's record by changing its identifier. A denial means the app blocked the request. Returned data means one user can read another's record.
04 Search for credentials in the repository and prompts
Search source and prompt files for provider keys or Bearer tokens. A match can expose provider access. Keep live keys in server-side secret storage.
05 Check the provider's data retention terms
Check retention and training terms for the production API. Compare them with the data the app sends. A mismatch means the provider can retain customer data or use it for training beyond your policy.
06 Estimate the cost of a normal run
Compare token use and retries from one normal run with current model rates. That gives a rough cost per run. Missing usage logs leave you without a per-task figure.
How we approach it
The audit guides stabilization and handover.
You decide whether to continue after the report. Stabilization work gets a separate fixed scope and price before changes begin.
Our working processA read-only audit
A read-only audit examines the repository and running system without changing either.
A ranked report
The report ranks technical findings by business impact and explains each in plain language with supporting evidence.
A fixed price for stabilization
The reviewed code and findings determine the scope and fixed price for agreed fixes. If you continue, the audit fee comes off the price of fix work.
Stabilization with a release plan
Changes address the agreed risks in priority order, and tests cover the affected workflows. The release plan sets any service window with you before deployment.
A handover for the next maintainer
The handover records the changed code and test results for the reviewed build.
A map of the build
The report traces the main data flows, model calls, tools, connected systems, and release path.
The written ranked report
The report starts with a plain-language summary and orders the technical findings by their effect on users, operations, data, and cost.
A review of prompts and controls
The report assesses the instructions, approval modes, tool permissions, and fallback rules that shape agent behavior.
Provider data and cost findings
The report identifies the model and API providers and summarizes their documented data terms and available per-run cost evidence.
A fixed-price stabilization scope
The report sets out the agreed fixes and their order before code changes start. If you continue, the audit fee comes off the price of fix work.
Notes for the next maintainer
Handover notes capture the relevant models, providers, action limits, release path, and checks needed to confirm a repair continues to work.
Before we start
A few practical questions.
You do not need to understand the code before the audit starts.
Send us an emailDoes the audit show whether this requires a rebuild?
Yes. The audit identifies which risks a focused repair can address and where the current structure blocks a safe fix. If a rebuild is the sounder choice, the report explains why before it quotes a price for fix work.
Can you audit it if I do not know which model or provider it uses?
Yes. The audit traces model and provider calls in the repository and checks visible settings. If an account or deployment hides a setting, the report identifies that gap without guessing.
Can you work on it without taking it down?
Yes. The read-only audit leaves the running system unchanged. You approve the timing for any required release window before stabilization starts.
What do you need from me?
The audit requires read access to your repository and deployed environment. Read-only provider access helps confirm the model and its data and usage settings. Describe the app's main workflow and any known failure. Existing documentation or tests can help, but are not required.
How long does the audit take?
The audit usually takes one to two weeks based on codebase size and access. The report includes the stabilization price and timeline before you commit to fix work.
Does it matter that AI tools wrote the code?
No. AI-assisted code receives the same review standard as any other code. The audit follows behavior through the running app or agent, regardless of the tool that produced it.
Do you need the person who built it to take part?
No. Repository and deployed-system access are enough to start. The builder's answers can shorten the audit, but their availability does not block it.