Reviews
Client reviews
What clients say about working with AIYatch. Attributions are anonymised until each client approves publication.
4.9 average rating · 9 reviews
The support assistant now handles the bulk of first-line questions and hands over cleanly when it should. Our team finally has time for the complex cases, and the weekly evaluation report means we can see exactly where it still gets things wrong.
What we valued most was the honesty: a clear baseline, a model card, and a number we could take to the board. When one of our ideas would not have worked, they said so in the first week rather than after the invoice.
They mapped our processes before proposing any AI. The automation that came out of it removed hours of manual work every week, and the exception queue meant our team stayed in control from the very first day.
A six-hour time difference and it honestly never mattered. Questions we sent in the evening had answers by the time we started, demos landed every Thursday, and nothing was ever hidden behind a status update.
The handover was the best part. Runbooks, architecture diagrams, a recorded walkthrough and two weeks of pairing with our platform team. We have run the on-prem stack ourselves since without a single call for help.
Twelve weeks from a deck to paying pilots. They pushed back on features we did not need, chose infrastructure our first hire could actually maintain, and instrumented accuracy from day one so we had evidence for investors.
The multi-agent system does the legwork the way a diligent junior would, and it never forgets to cite. Rollout took slightly longer than planned because we changed scope mid-way, which they absorbed calmly and without drama.
Fine-tuning felt risky to us. They spent most of the effort on the dataset and the test set, showed us the comparison against our existing model every week, and left a pipeline our engineers now rerun on their own.
Two weeks of plain data capture before any accuracy promise. That honesty set expectations right, and the defect overlays on the line are trusted by operators because every reject shows the frame that caused it.
Attributions are anonymised until each client approves publication.
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