The situation
You have an AI-built solution that seems to work.
It runs on someone's machine and does what they asked for. The open question is what the code actually does, and whether it survives contact with real operations, with an auditor, with a bad day. In a regulated business, that gap is where money and reputation are lost.
The market is loud with AI enthusiasm. What helps is someone who has stood at this crossroads before and can tell you which way it usually goes.
Patterns
What I keep seeing.
The same few failures, across different firms and different use cases. None of them show up until it is running for real.
Getting it working once is the cheap part.
The build gets quoted. Running it, monitoring it and answering for it does not. A feature that costs little to prototype can be expensive to operate safely in a business-critical system. Price a year of running it before you commit.
Nobody owns the running cost.
AI costs drift when no one is accountable for them. The largest model gets used for work a smaller one would handle. Context gets re-sent instead of reused. Experiments stay running in live systems. Put a number on cost per case before you scale, and read the bill every month.
Lock-in hides in the integration.
Teams worry about picking the wrong model. The cost of leaving is rarely the model. It is the data, the prompts and the workflow integrated so tightly with one vendor that moving means starting over. Keep a thin layer between your systems and any single provider, favour portable data and put exit terms in the contract.
The evidence gets built last.
Proof that the model works and proof that it complies are treated as two projects, and the second one starts after go-live. Build them as one deliverable. Under the EU AI Act that evidence is what an auditor asks to see. Assembling it afterwards turns a review into a project of its own.
Existing controls do not cover the new attack surface.
Prompt injection is the gap that surprises people: hidden instructions inside a document, a web page or a user message that push the model to act against your intent. Treat every input the model reads as untrusted, limit what it can do on its own and keep a person in the loop for anything that moves money or data. Convincing text, voices and images are cheap to produce now, so identity checks that once relied on appearances need rethinking.
No one took a baseline.
The gain is real and it cannot be shown. Decide what you are measuring before you start, measure the same thing after and count the cost of building and maintaining it. A return your finance team would sign off without conditions is the only kind that counts.
AI gets added to a broken process.
That gives you a faster broken process. Map how the work flows today, find the step where volume, delay or error accumulates and redesign the flow around what the model does well. Keep your people on the judgment and the exceptions.
What you get
Two steps, in order.
First · my view of the situation
You bring a real decision. I tell you where you are, what is doable for a firm at your stage and the crossroads you are actually standing at. What I have seen go right and wrong, applied to your situation in an hour.
Then · specialist depth
When I see that a decision needs expertise from the multidisciplinary team, I know who to bring in and when. That might be the expertise to get a complete understanding of business needs, customer expectations, system constraints and solution delivery. And not to forget AI model output validation, cybersecurity and the evidence your team and an auditor will ask for. This is how you make a decision with confidence.
The honest part
Sometimes the answer is no.
Sometimes the honest advice is to wait, or to walk away. I can say that and keep the retainer. My fee is the same whether you build or hold, so nothing is pulling me toward a yes.
That is the point of the retainer.
How it starts
How it starts.
The first session is where we both decide whether a retainer makes sense. You bring one real decision you are facing now. You leave with a clear view of where you are and what to do next, whether or not we go further. The first session carries no fee. What it asks of you is a real decision with real impact.
If it earns a retainer, we continue month to month. One working session a week and quick calls between. A fixed monthly fee, from 5000 EUR plus VAT, for as long as it is useful for you.
Who I am
Who I am.
I am Allan Valm, a Business Area Leader at Helmes, a software group of around 1,500 people across the Baltics and beyond. Watching business-critical software succeed and fail taught me to see the difference early.
It is me you work with. When you decide to build, Helmes delivers as the firm. You get one person's judgment with an institution behind it.
Track record
Where this comes from.
Twenty years in business-critical software, most of it in regulated industries.
Helmes · 2025 to now
Leading a business area with two product teams, around twenty senior specialists, building AI-augmented workflows and business-critical systems for regulated clients, among them the Estonian Tax and Customs Board, the Ministry of Foreign Affairs and Enterprise Estonia.
Digital Elegance · 2023 to now
Running a four-year service design masterclass with Enterprise Estonia for eighty Estonian companies, including teams at the Port of Tallinn. The work was helping them design and validate customer-centric B2B services for export before committing to build them.
Mobi Lab · 2017 to 2023
Led product teams delivering mobile apps from concept to launch for Telia, Inbank, Veriff, Apollo Group, MyFitness and Ühisteenused. Founded Reality Maker, an augmented reality platform for education, secured over 600,000 EUR in funding and scaled it with Ericsson into the US and UK.
Inbank · 2015 to 2017
Built and led the bank's first software development team. The Scrum, quality assurance and DevOps processes we put in place passed external compliance audits on the first attempt. Its first deposit product launched in three months, met its financial goals within two weeks and scaled to Poland and Latvia within six months.
Playtech · 2010 to 2014
Designed responsible-gambling and loyalty features for a casino management platform used by Coral, Ladbrokes and William Hill. Introduced a testing framework that cut defects by half across major releases.
The first session
The questions I will ask you.
These are the questions the first session works through. Take them into your own meeting and use them without me.
What decision are you actually making, and by when?
Many AI conversations contain no decision at all. Naming it helps the team focus on what matters.
Who else has to agree, and what will they need to see?
The decision is rarely one person's. Each person who has to agree needs something different, and finding that out late is what causes the delay.
What happens when the model is wrong?
How often, how visibly and what it costs. Every model is wrong sometimes, so plan for the day it is.
Who answers for it?
Approval and accountability get confused. Several people can approve an AI system. One person has to answer for it, and should know that before go-live.
What would you have to show an auditor, and could you show it today?
Under the EU AI Act this question arrives late and costs the most when it does.
What does a year of running this cost?
The build gets quoted. The cost of operating, monitoring and fixing it rarely does.
Who owns it once it is live?
Models drift and data changes. Watching for that is a standing job, and it needs an owner.
What would make you stop?
Agree the answer before you start. It is the hardest question here and the most useful one.
If the answers come easily, you are in better shape than most. If two or three of them stall, that is the conversation worth having.
Contact
One decision is enough to start.
Bring one real decision you are facing now so we can find a way to move faster without losing control.
The first session carries no fee. I read these myself and reply within one working day.