Aivanta Technology builds AI products end to end, and gets called in when a model that worked in testing starts failing in the real world. A hundred-plus engineers, one named account manager per client, and a written record of everything that changed.
Fixed scope before work starts · You own the code · No lock-in
AI that learns, adapts, and holds up
Three kinds of work take up most of our bench. Each is a place where AI earns its cost quickly — and where a model going quietly wrong is expensive.
Extraction, classification and review for contracts, claims, invoices and compliance files — with a confidence score and a human queue for anything the model should not decide alone.
Retrieval-backed support and sales assistants that answer from your own content, cite where the answer came from, and hand over to a person the moment they are out of their depth.
Demand, risk, fraud and equipment failure — models that flag the unusual early, and that report how confident they are instead of stating a guess as a fact.
Aivanta Technology is an AI engineering firm. We take projects from the first conversation to a running system, and we take on the harder job of repairing AI that is already live and no longer trustworthy. Both come off the same bench of more than a hundred developers and AI engineers.
Most firms will build you a demo. The distance between a demo and something a business can depend on is where projects die — evaluation, monitoring, failure handling, cost control, and the unglamorous work of making a model behave the same way on Tuesday as it did on Monday. That is the work we staff for.
Every client gets one named account manager who stays with the engagement from the first call to handover. With more than twenty of them, nobody waits in a queue to find out what is happening to their project.
Engage us for one of these or all six. Most clients start with an audit and decide from what it finds.
Scoped, built and shipped by a team that stays with the project.
We find the cause instead of swapping the model and hoping.
Smaller, faster and cheaper, without losing the quality that mattered.
An AI system you cannot measure is one you cannot improve.
Models are only as good as what reaches them.
Getting it live, and keeping it live, on your infrastructure or ours.
A hundred engineers is only useful if the right four are on your problem and somebody is accountable for the result. Four habits make that happen.
Every engagement is run by an account manager who learns your business well enough to argue with us on your behalf — including telling you when the honest answer is that you do not need what you asked for.
Book a free 30-minute consultYour team sits in the same planning and review sessions as ours. No progress is reported that your engineers have not seen for themselves.
We define what “working” means in numbers before anyone writes code, so success is something we can check rather than argue about.
Two-week increments against those numbers. You watch the system improve on the measures you agreed, not on a slide.
Evaluation suites, adversarial cases, load tests, and a documented list of what the system is known not to handle. That list is part of the delivery.
Regulated, data-heavy and operationally complex sectors are where careful AI engineering pays for itself fastest.
Each stage ends with something you can hold — a document, a prototype, a report. If it is not working, you are not locked in.
What the system must do, what it must never do, and how we will know.
Model choice, data sources, architecture, and a cost estimate you can budget against.
A narrow working slice on your real data, to prove the approach before the spend.
Two-week increments, your team in the reviews, measured against the agreed numbers.
Evaluation suites, adversarial cases, load and failure testing before anything ships.
Staged rollout with monitoring, and a rollback path that has been rehearsed.
Handover, training for your engineers, and an account manager who stays reachable.
A free 30-minute consult, and a written scope if it looks like a fit.
Plan your projectWe pick tools to fit the problem and the systems you already run, not the other way round. This is what we reach for most often.
It depends on scope, and we will not pretend otherwise. What we do promise is that the number is fixed and agreed before work starts. Most clients begin with a short paid audit — a few days of engineering time — which produces a written scope and a price for the work that follows.
You may not. A model that is measured, monitored and behaving is not a problem we need to solve. We are usually called when nobody can say with confidence whether it is still behaving — because there is no evaluation suite, no monitoring, and no record of what changed.
You do. Source code, fine-tuned weights, prompts, evaluation sets and documentation are yours at handover, with no licence back to us and no dependency on our infrastructure.
That is the usual arrangement. Our engineers work alongside yours, in your repositories and your review process. Part of every engagement is making sure your team can run the system after we leave.
It stays where you want it. We can work entirely inside your cloud account or on-premise environment, and we scope data handling, retention and access in writing before any data moves.
Handover includes runbooks, training for your engineers and the monitoring setup. Your account manager stays reachable after the project closes, and ongoing support can be arranged as a separate retainer if you want it.
Our engineering bench is deep. What we need now are the people who sit between a hundred-plus engineers and the clients who depend on them.
A project enquiry, a model that has started misbehaving, or an application for the account manager role — it all reaches us here, and a person replies.
Tell us what you are trying to do, or what has stopped working. The first thirty minutes cost nothing, and you will speak to an engineer.
