Machine learning services
Machine learning consulting: what it covers and how to scope a first project
A plain guide for owners and data leads weighing outside help with machine learning. This is Kriv AI describing its own approach. Scope and price follow a short discovery call.
Machine learning consulting is outside help to decide where machine learning fits, prepare the data, build and test a model, and put it into use with the right controls. A good engagement starts with one business question, checks whether the data can answer it, and sets how results will be measured before any model is built. Ask who owns the model and the data afterward, how it will be monitored and who reviews it. Kriv AI works on machine learning with governance built in. Scope and price are set after a short discovery call.
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What does machine learning consulting cover?
It usually covers four things: choosing a use case worth doing, readying the data, building and testing the model, and running it safely afterward. Some consultants cover only one of these, so ask which parts a quote includes.
Not every problem needs machine learning. A good consultant should tell you when a rule, a report or a simpler tool does the job.
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How do you scope a first machine learning project?
Pick one question with an owner who will act on the answer. Check that the data exists, can be used for this purpose and is good enough. Agree how success will be measured on data the model has not seen, and what a human reviews.
Plan the path to use before the build: where the model runs, who watches it, and what happens when it is wrong. A pilot can stall when this step is left for later.
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What should you ask a machine learning consultant?
Ask who owns the code, the model and the data at the end. Ask how they test, and what they do if the results are weaker than hoped. Ask how the model will be monitored, who is told when it drifts, and how a person can override it.
Ask for the names of the people who will do the work. Treat any promise of a certain result as a warning sign.
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What governance should come with it?
A model that affects customers, money or health needs an owner, a review step, tested behaviour and a record of changes. Keep training data under the same rules as the rest of your data.
Kriv AI does not use one client's data to train models for other clients.
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Where Kriv helps
Kriv AI works with mid-market and regulated teams on machine learning with governance and evidence built in, from scoping through to monitoring. Scope and price are set after a short discovery call.
Kriv AI helps teams scope, build and govern this work with human review where it matters. Kriv does not replace the vendor documentation cited on this page, and your own owners hold the final say on what goes live.
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What this page does not cover
This page describes Kriv's approach in general terms. It does not compare consultants, quote prices or promise results, and it cites no outside statistics. Your own situation decides what is possible.
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Straight answers
Frequently asked questions about Machine learning consulting: what it covers and how to scope a first project
What is machine learning consulting?
Outside help to choose a use case, prepare data, build and test a model, and run it with the right controls.
What is a good first machine learning project?
One business question with an owner, data that can answer it, and a clear way to measure results on unseen data.
Who should own the model after the project?
Settle this in writing before work starts: the code, the model and the data should have named owners on your side.
Does every problem need machine learning?
No. A rule, a report or a simpler tool is sometimes the better answer, and a good consultant should say so.
How is Kriv machine learning consulting priced?
Work is scoped and priced after a short discovery call.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
Book a Discovery Call