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AI and ML solutions

This is the category with the widest gap between what gets demonstrated and what survives contact with production. We work the other way round: start from a decision your business makes repeatedly, and build the smallest thing that measurably improves it.

What it covers

We start from the decision you want to improve and work backwards. Sometimes that is a fine-tuned model, often it is retrieval over your own documents, and occasionally it is a well-placed rule that saves you the model entirely.

  • Document search and retrieval (RAG)
  • Forecasting and classification
  • Computer vision for inspection and OCR
  • Model deployment and monitoring

How we approach it

We start from the decision, not the model
What gets decided, how often, by whom, and what a wrong answer costs. That determines everything downstream — whether you need a model at all, how accurate it has to be, and where a human stays in the loop.
Retrieval before training, rules before both
Most business problems labelled 'AI' are answered by good search over your own documents. Some are answered by a rule someone can read. We reach for the cheapest thing that works and escalate only when it doesn't.
Evaluation is part of the build
A model without a test set is a guess with a confidence interval. We build the evaluation alongside the system so you can see accuracy on your own data, and see it change when something drifts.

Where we differ

Why bring this to us rather than anyone else.

We use this in our own work every day
AI-native delivery isn't a service line we added because clients started asking. It's how we build everything, which means our advice comes from running these systems ourselves rather than from a vendor's slide deck.
We'll talk you out of it when it doesn't pay
Plenty of AI projects cost more than the problem they solve. If a well-placed rule, a better form, or an off-the-shelf tool gets you most of the value, that's the recommendation you'll get — before an invoice, not after.
Built to run, not to demo
Monitoring, cost ceilings, fallbacks for when a provider has an outage, and a clear answer for what happens on a bad input. The demo is the easy half; we're interested in the half that has to hold up at 3am.

When to call us

You probably need this if…

  • Staff spend hours reading documents to pull out the same handful of fields
  • Institutional knowledge is buried in files nobody can search properly
  • You're forecasting something important in a spreadsheet, by feel
  • You've had an AI pilot that impressed everyone and never reached production

Questions

Does our data get used to train someone else's model?
Not unless you choose a setup where that's the case, and we'd flag it explicitly. We default to arrangements where your data stays yours, and we'll tell you exactly which providers touch it and under what terms.
What if the model gets it wrong?
It will, sometimes — that's why we design for it. Where errors are costly, the system routes uncertain cases to a person instead of guessing confidently. We'd rather build something that knows what it doesn't know.

Also from us

Next step

Tell us what you are trying to fix.

A couple of paragraphs is plenty. We will tell you honestly whether this is the right service for it, and whether we are the right team — usually within two working days.

Start a projector email connect@riftwaretechnologies.com