ColabContentOne working system
One AI feature, from draft to release.
We need an engineer who can ship an AI feature and prove it works.
AI feature desk
One AI feature is taken from draft to release: each new version is run against a set of test questions, a failing answer is traced to its cause, and a version goes live only at the pass mark, with one click to go back.
What it does
Change it, test it, fix it, ship it, in four steps.
A cheaper version is put to the test
A new version reads fewer passages to cut the cost of each answer. Before it goes anywhere it is run against a fixed set of test questions, and it comes back one short of the pass mark.
The failed answer is shown with its cause
The assistant told a customer the old return period. The page shows the answer it gave, the answer it should have given, and why: the current policy was ranked below the passages this version read.
The fix is a new version, tested again
The passages it read came from a document marked superseded. A new version skips those documents, the same questions are run again, and every one passes.
It ships at the pass mark and not before
Shipping is locked below the pass mark and opens only now. The release log records which version replaced which, and what it scored at the time.
Ownership
You own it. All of it.
The test questions, the marking rules and the release log are the part of an AI feature that outlasts any one engineer. In a version built for you they sit in your repository and your accounts, so whoever works on the feature next inherits the proof along with the code.
Hire or own
What the hire costs, and what this costs
Read your post for the feature you actually need shipped. If it is one feature with a clear purpose, it can be built at a fixed fee with its tests and release gate included, and an engineer you bring in later starts from a working, measured baseline.
What this role is paid, and what that comes to with benefits, is on the AI engineer salary page.
When the hire is the right call
Hire the engineer when AI is your product, or when you expect a stream of new features and want that judgment in the building every week. A fixed piece of work suits one feature; a roadmap needs a person.
Read this first
What you are looking at, said plainly
Demonstration system, sample data
The outdoor supply company, its documents and its customers are invented. Answers on the recorded path are replayed from real runs of this code, the test set is small and marked by text checks, and there is no speed measurement in it.
What it costs
Three public prices. Nothing per user.
Jim Glaser Law takes reference calls.Ask in your note and we set it up.
One step
Send me a version customized for my company
Questions buyers ask
Before you ask for your version
Does this do an AI engineer's whole job?
No. It does the test, release and roll back loop for one feature, which is a core part of the job. Designing new features, choosing models and working with your own data stay with a person.
What is the feature in the demonstration?
An assistant that answers customer questions from a company's own documents and quotes the passage it used. The documents and the company are invented.
How does it decide an answer failed?
Each test question lists what a correct answer must say and must not say, and a script marks every answer against that. The page also shows where the right passage was ranked, so the cause can be read off the screen.
Is the failure in the recording staged?
No. The sample documents include an old policy and a current one, and the cheaper version really did read the old one first. The code does not fake the miss.
Can a release be taken back?
Yes. The release log keeps the earlier version, and one click makes it the live one again and records that it happened.
What goes into a version built around our feature?
The feature, the documents and the test questions, written to look like your business on sample data. It needs no access to your systems.