Audit the flows
We sit with the people doing the work and map how it actually happens, not how the handbook says it does. Then we mark where AI pays, where plain automation is enough, and where neither helps.
AI
AI that earns its place.
Most companies do not need an AI strategy. They need two or three things that genuinely work, chosen well, built safely, and owned by their own people. We find where AI pays in your business, pick and secure the tools, put them into real work, and train your team to run them without us.
Where most companies are
Everyone is experimenting. Few are getting value.
Licences bought, pilots run, a few enthusiasts doing impressive things in a corner. Meanwhile the work that actually costs your week is untouched, and nobody can say exactly which data went where.
We start from the other end: your real flows, your real costs, your real rules. Then we only build what survives that look. Sometimes the honest answer is that a process needs fixing before any model touches it.
How we work
From a vague idea to something your team runs.
02 · Choose
Pick tools, not hype
Which model, bought or built, running where, costing what at real volume. You get the reasoning in writing, including what we ruled out and why.
03 · Integrate
Put it into real work
Inside the systems your people already use, with guardrails, logging, and a way to switch it off. A tool nobody opens is not a result.
04 · Hand over
Make your team the owner
Training on your own work, a written policy people can follow, and documentation that outlives us. If you need us afterwards, it should be because you want us, not because you are stuck.
What we cover
Six ways we help with AI.
Take one of them or all six. Most companies start with the audit, because it tells you which of the others are worth paying for.
01
Flow audit and opportunities
A fixed-scope look at how your company actually works, ending in a plain-language report: where AI saves real hours, what it would cost, what it would risk, and what to do first. Including the parts we recommend leaving alone.
- Process mapping
- Cost modelling
- Quick wins
- Roadmap
02
Tools, models and hosting
Which assistant, which model, bought off the shelf or built, and where it runs. We care about what it costs at full volume and whether you can move away from it later, not about who is trending this month.
- Model selection
- Build or buy
- EU hosting
- Cost control
03
Safety and governance
Where your data goes, who may use what, and how you prove afterwards that it behaved. Written down in language your board and your data protection officer can both read.
- Data boundaries
- Access control
- GDPR
- EU AI Act
- Audit logs
04
AIO, AI search optimization
People now ask assistants about your market before they ever reach your website. We check what the models say about you today, fix the sources they read, and keep watching as the answers move.
- Answer audit
- Structured data
- llms.txt
- Source coverage
- Monitoring
05
People and training
Role by role, on your own work, with your own rules. Not a webinar nobody remembers: short sessions, real tasks, and a written policy that tells people what is allowed and what is not.
- Workshops
- Role-based training
- AI policy
- Internal champions
06
Building it
When something needs to be built, our own AI engineers build it: assistants that know your material, agents that finish a task, and the evaluation that proves they still work next month.
- Assistants
- Agents
- RAG
- Evaluation
- Integrations
AIO
When someone asks an AI about your industry, what does it say about you?
Search used to send people to your page. Increasingly, an assistant answers for you, and you never get the visit. Being described correctly in that answer is the new front page.
Find out what they say
We put your brand, your products and your competitors through the assistants your customers actually use, and write down what comes back: what is right, what is wrong, and what is simply missing.
Fix what they read
Those answers are assembled from sources: your own pages, structured data, documentation and third-party mentions. We make them clear, consistent and machine-readable, including an llms.txt that states plainly how your company should be described.
Watch it move
Models change and so do their answers. We re-run the checks on a schedule and report what shifted, so your presence in AI answers is maintained rather than guessed at.
We did it here first
This site is our own proof: server-rendered pages an assistant can read without running scripts, structured data on every page, and a maintained llms.txt. We will show you ours before we touch yours.
Safety
AI you can defend to your board, your customers and your DPO.
Safety is not a slide at the end of the project. It is the shape of the thing we build.
Where your data goes
Named providers, named regions, written agreements. EU hosting where it matters, and a clear answer to the question everyone eventually asks: is our material being used to train somebody else's model.
What the model may touch
Access scoped per use case, not handed out per enthusiast. The assistant that drafts offers has no business reading the salary file, and we set it up so it cannot.
Proof that it behaved
Logging where it matters, so months later you can answer what the system was asked, what it replied, and who acted on it. Without that, every incident becomes a matter of opinion.
Rules in plain words
GDPR from the first sketch, and the EU AI Act obligations that genuinely apply to your use cases, explained without the legal fog. Most business uses are low risk, and we will tell you when yours is not.
An off switch
Everything we build can be turned off or rolled back without taking the business down with it. A system you cannot stop is a system that owns you.
Your people
AI should make your team better, not smaller.
The companies that get real value are the ones where ordinary staff, not only the technical few, know what these tools are good at and where they quietly get things wrong. That is a training problem long before it is a technology problem.
We would rather leave you with twenty people who use AI well than one clever system nobody trusts. And if a project looks like it would hollow out a team instead of lifting it, we will say that out loud before you spend the money.
Start
A flow audit, fixed scope
You get
A roadmap in plain words
Then
We build it, or we tell you not to.
Common questions.
Do we have to replace the tools we already use?
Usually not. Most value comes from putting AI inside the systems your people already open every morning. If something does need replacing we will show you the arithmetic first.
Is our data used to train somebody's model?
Not in anything we set up. We select providers and plans where business data is excluded from training, confirm it in the agreement, and write down exactly which data leaves your environment and where it lands.
We already picked a vendor. Can you still help?
Yes, and it is a common starting point. We work with what you have, make it safe and actually used, and only suggest changing it if the reason survives being written down.
How long before we see anything real?
The audit is a matter of weeks, not quarters, and it ends with something you can act on. Smaller builds often follow in weeks rather than months, because we start with the narrow use case that pays.
Are you reselling somebody's AI platform?
No. We take no commission from vendors, so our recommendation costs you nothing extra and owes nothing to anyone. If the right answer is a cheap off-the-shelf tool, that is what you will hear.
Start with one honest look.
Tell us what your team spends its week on. We will tell you where AI helps, where it does not, and what it would cost to find out properly.
Email hello@codyn.se