AI · All AI packages
AI we build, test
and run in your
environment
An AI audit is one to two weeks and a written decision: build, fine-tune, or stop. An AI production sprint is one search-and-answer system or agent in production in four to six weeks, with evaluations.
The facts
At a glance
Who it is for
A software company choosing which AI package fits.
Length
An AI audit is one to two weeks. An AI production sprint is four to six weeks. Managed AI operations is a monthly agreement.
What you get
A written decision, one system in production, a private model only if the hosted model falls short, monthly care, or a coding-agent setup.
What happens after
We say which package fits. A person in the team is consulting. A platform nobody owns is Platform Partner.
Pricing
Contact us for pricing.
One system
Tell us what
should be in
production
A workflow, a live AI system that nobody owns, or an engineering team without a shared way to use coding agents. We say which package fits.
Packages
Pick the shape of the work.
Fixed scope and a fixed price, agreed before we start. You own the code, the prompts and the data.
AI production sprint
Four to six weeks. One search-and-answer system or one agent in production, with a golden evaluation set, monitoring hooks, a runbook and a go or no-go to scale.
See the package →Retainer · Extends Platform PartnerManaged AI operations
Monthly care for AI already live: evaluations, regression when the model or prompt changes, drift, latency and cost, incident response and a monthly report.
See the package →Engineering teams · Your reposAI coding rollout
Repo context files, agent skills, security boundaries, continuous integration and evaluation gates, and training for the team. The same setup we run ourselves.
See the package →Fine-tuning · Decision firstPrivate model / fine-tuning
An open model tuned on your own data, run where your data must stay. It can start on its own or after an AI production sprint.
See the package →How we deliver
What every AI
engagement includes.
The package changes. These do not.
A golden evaluation set and a continuous integration gate
Every AI engagement ships a set of real examples and a check that fails the build when a model or prompt change gets worse. We agree the set before we call it done.
Your environment, by default
Private or virtual private cloud on Amazon Web Services, Microsoft Azure, Google Cloud or GleSYS, or on your own servers. Data stays in your environment, with audit logs. We do not train on your data unless you ask us to, in writing.
A build, not a workshop
We write the code in your repo and leave the system running. We do not sell a strategy workshop.
How a package starts
We name the system, the boundary and the price before we build.
One system, named
Internal search and answers, a customer or support agent, an operations agent, or the coding setup for one repo. If it is not one system, it is a different engagement.
Fixed scope, fixed price
You get the scope and the price before we start. Care after launch is a monthly agreement. New features are change orders, also at a fixed price.
In production, with a way back
Logging, permissions, the evaluation gate and a runbook ship with the system. You can turn it off.
One search-and-answer system
or one agent in production
AI production sprint
Weeks
Golden evaluation set,
monitoring hooks, a runbook,
a go or no-go to scale
How we work
We run on our own agents.
The packages are the same operating discipline we use ourselves. Our portal, and the work in sales, recruiting and engineering.
Our portal
The company runs on our own portal. Agents sit in the work, with permissions and a log of what they did.
Sales and recruiting
Agents help the people who sell and the people who hire. The boundary is the one we put in a client system: least privilege, and a human when the step needs one.
Engineering
Our engineers use repo context, skills and continuous integration gates. The AI coding rollout is that setup, written so your team can run it.
Why teams ship AI with us
Senior engineers in Gothenburg.
We build, and we stay for the care if you want that.
Forward-deployed
For automotive and connected products in Gothenburg, a senior sits with your team. The product engineering around the AI feature is part of the job.
Builders
You talk to the people who write the code. We work in your repo and follow your review rules.
You know what it costs
Fixed price for a sprint and for each change order. A monthly agreement for care. Nothing outside the agreement until you have approved it.
Also
A person in the team, or a platform.
The packages are fixed scopes. Some teams want an engineer in the team. Some have a platform nobody owns.
Related insights
More on AI in production.
Articles on taking AI from a pilot to something that runs.
All insights ↗Agent readiness before you scale AI
Agentic workflows fail fast when data access, ownership, and logging are unclear. A short readiness sprint surfaces the gaps before production.
Read insight →System Development · 5 min readAI coding tools need guardrails, not just licenses
Coding agents are getting fast and cheap, but review load and traceability become the bottleneck.
Read insight →AI Product Development · 8 min readAI pilot to production: a practical playbook for Nordic product teams
Most AI pilots never become product value. Here is a field-tested playbook for taking an AI initiative from demo to stable production impact.
Read insight →Ready to talk?
Tell us where it is stuck.
A demo that never shipped, a system nobody owns, or a team using coding agents without a standard. We say straight whether we are the right fit.
Questions
Common questions.
Short answers on how the packages fit together.
Which package do we need?
One system to put into production: the AI production sprint. Something already live that needs an owner: Managed AI operations. An engineering team that needs a shared way to use coding agents: the AI coding rollout. A private model on your own examples: Private model / fine-tuning, after an evaluation shows the hosted model falls short. A person in your team for longer: consulting. A platform nobody owns: Platform Partner, then the AI upgrade path.
What does it cost?
You get a fixed price before we start. A sprint is one fixed price. Managed AI operations is a monthly agreement. New features are change orders, priced one by one before we begin. Private model / fine-tuning can start on its own or after an AI production sprint. Contact us for pricing. We do not publish a rate card.
Where does our data go?
It stays in your environment unless we have agreed otherwise. Private or virtual private cloud on Amazon Web Services, Microsoft Azure, Google Cloud or GleSYS, or on your own servers, is the default, with audit logs. We do not train on your data unless you ask us to, in writing. Private model / fine-tuning is that written ask: a fine-tune on the examples you provide for one model, hosted where the data must stay.
How do we know it works?
A golden evaluation set of real examples, agreed before we call it done, and a continuous integration gate that fails the build when a change gets worse. After launch we watch quality, latency and cost.
What about the EU AI Act?
It depends on what the AI is used for. We help you see which category the system falls into, and we build in logging, traceability, documentation and human control. The legal opinion sits with your lawyer.
Who owns the solution?
You do. Code, prompts and data. We can keep it healthy on Managed AI operations, or hand it to your team with the runbook.
Ready to talk?
Let's talk about
what you're building
Or reach us directly at hello@aidoni.se
