AI system rescue and takeover
Find out what you actually inherited
Two weeks inside the code, and you get a written verdict: what the system does, whether it is correct, and whether fixing it beats replacing it. The assessment is yours either way.
The short version
4 things that decide this
- 01The first question on an inherited AI system is not how to fix it. It is whether anyone can prove what it currently gets right.
- 02Almost every rescue we are called into shares one gap: no graded test cases, so nobody can tell a fix from a regression.
- 03A system with no evaluation set cannot be safely changed by anyone, which is why the first work we do is usually measurement rather than repair.
- 04Sometimes the honest verdict is to stop, and we would rather tell you that in week two than in month eight.
The demo was signed off. Then it met real users
The pattern repeats. A prototype impressed everyone in a meeting. Budget followed. The team that built it moved on, or was let go, or simply stopped replying.
Now something is live and nobody can answer basic questions about it. How often is it wrong? Which changes made it worse? What happens when the model it calls gets retired? Nobody asking is being difficult. There is simply no way to find out.
MIT's 2025 State of AI in Business report found that 95% of enterprise AI pilots show no measurable impact. The work we get asked to rescue matches that pattern, and the idea is rarely what failed. Nobody planned how the thing would be checked, watched or owned.
The two-week diagnostic
Paid, fixed scope, and it ends in a document rather than a proposal.
- 01
Map what exists
What is deployed, where it runs, and which model it calls. What it is allowed to touch. Which parts nobody has opened in a year.
- 02
Measure what it gets right
We pull real traffic and have your experts grade a sample. This usually produces the first honest accuracy figure the system has ever had.
- 03
Find the cliffs
Model versions with announced end dates. Credentials in the wrong place. Actions nobody meant to allow. Costs that scale badly.
- 04
Give you a verdict
Fix, replace, or stop. Written down, with the reasoning, and a fixed price for the work that follows.
- CorrectnessGraded cases. Usually the first ever.
- SafetyWhat can it do without a human?
- TraceabilityCan a bad run be replayed?
- DependenciesModel end dates, keys, integrations.
- Cost shapeWhat happens at ten times the volume.
- OwnershipWho is responsible on a Sunday.
The first station decides everything after it. Without graded cases there is no way to prove a fix improved anything, so measurement comes before repair on every rescue we take.
Sometimes the answer is stop
Not every inherited system deserves saving. Some were built for a problem that has changed. Some solve a problem a simple rule solves better. Some are fine and the real fault is upstream, in data nobody has cleaned.
We say so in the assessment, and it is the reason the diagnostic is paid work rather than a sales call. A firm hoping to win the rebuild has an obvious incentive to recommend one. Charging for the verdict is what lets us hand you a document that says do not spend this money.
- Scoping conversations cost nothing. The paid diagnostic applies only where we must go inside the code.
- You keep the written assessment, and the fixed price for the work it recommends.
- We are not the cheapest bid, and we say so before you ask.
- Where the verdict is fix, the price that follows is fixed rather than a range.
The four things wrong with almost every inherited build
Different codebases, the same gaps.
No graded cases
Nobody wrote down what a correct answer looks like, so no change can be proven better or worse. This is the most common finding and the one that blocks everything else.
No trace of what happened
A user reports a bad answer and there is no record of what the system retrieved or which tools it called. The bug cannot be reproduced, so it gets closed.
A model id hard-coded in five places
The provider announces a retirement and what should be a config change becomes a hunt through the codebase. Anthropic gives at least 60 days' notice, which only helps if you can act on it.
Nothing stopping an irreversible action
The system can send, refund, delete or file without a person approving it, usually because nobody ever listed which actions were dangerous.
Stacks we have inherited
Models and protocols
Application
No-code and hybrid
Infrastructure
Systems we took over or rebuilt properly
Go4Gr8
Custom AI sparring-partner platform for leadership coaching.
Read the case study →
PremiumAudit.io
AI automation for smarter insurance premium audits.
Read the case study →
TrialTriage
AI clinical trial matching for oncology nurses and insurers.
Read the case study →
ZhoopZhoop
AI receptionist and parts procurement for a multi-branch auto repair business.
Read the case study →
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
A rebuild pitch against a diagnostic
Both start with a look at your system. They differ on what the firm is paid to conclude.
| Criterion | The free rebuild pitch | The paid diagnostic |
|---|---|---|
| What it costs you | Nothing, and the incentive is a rebuild. | A fixed fee, and the incentive is an honest verdict. |
| What you get | A proposal. | A written assessment you keep either way. |
| Can it say stop | Rarely, since that ends the sale. | Yes, and it sometimes does. |
| Accuracy today | Not measured. | Graded against real cases by your experts. |
| The price afterwards | A range that moves once work starts. | Fixed, because the unknowns were removed first. |
01We inherited an AI codebase that does not work. Where do we start?
Start by establishing what it currently gets right, because until that exists no fix can be shown to be an improvement. We take a sample of real traffic and have your own experts grade it. That usually produces the first honest accuracy number the system has had. Everything else in the rescue depends on that baseline.
02Why is the diagnostic paid when your scoping calls are free?
Conversations cost nothing because talking is cheap and useful. Going inside somebody else's codebase for two weeks is senior engineering time, and charging for it is what makes the verdict independent. A free assessment is paid for by the rebuild it recommends.
03What if the verdict is that we should stop?
Then you have saved the budget you were about to spend, and you have a document explaining why to whoever approved it. This happens. Some systems were built for a problem that has since changed, and some solve something a plain rule handles better.
04Can you work with a system built on Bubble or a no-code tool?
Yes. Go4Gr8 came to us after outgrowing TypingMind Custom. We rebuilt it as a custom multi-tenant platform. Onboarding a user dropped from over two hours of manual setup to under 15 minutes. PremiumAudit runs its Claude-based automation on Bubble today. The tool is rarely the deciding factor.
05The original developers are unreachable. Does that stop you?
No, and it is the normal starting position. The diagnostic exists to reconstruct what nobody can tell us: what is deployed, what it calls, what it may do unattended. Missing documentation is a cost, not a blocker.
06How is this different from your AI consulting diagnostic?
AI consulting asks whether you should build a thing at all. This page assumes something already exists and asks whether it can be trusted. The two engagements share a shape and answer different questions, so the scoping call decides which one you actually need.
07Will you keep running it afterwards?
If you want us to, under an agreed service level, or we document a handover so your own team can. What we will not do is fix a system and disappear, because that recreates the exact position you are in now. Ownership after launch is part of the verdict, not an afterthought.
08We are a startup and the build was our whole budget. Is a diagnostic worth it?
It is usually cheaper than continuing to pay for something nobody can vouch for. At small scale the assessment is narrower, because there is less system to read. The alternative is funding a rebuild on the same missing information that produced the first one.
Related
- AI prototype to production →Hardening a prototype that never reached a production standard.
- AI consulting →Whether to build it at all, before anyone does.
- Why do AI projects fail →The failure modes behind most rescues.
- What to ask an AI vendor →The questions that would have prevented this.
- Software maintenance →Keeping it healthy once it is worth keeping.
