AI implementation

AI automation for business — AI agents and AI integration services

AI agents · LLM integration · RAG · AI consulting and audit · EU AI Act and GDPR

We implement AI where it does real work: AI agents take over repetitive tasks, we connect language models with your ERP, CRM and online store, and we make company knowledge searchable through document search. We start with a process map and a prototype on your own data, measure quality before anything goes into production, and keep a human in the loop wherever a decision has consequences. We run our own AI agent systems day to day — we know their costs and limits from daily work, not from slide decks.

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What we build on

Own AI agentssystems we use ourselves every day

20+ yearsof experience in IT projects

Vendor-neutralmodel chosen to fit the task

EU · GDPRarchitecture for data processing in the EU

What we automate with AI agents

An AI agent is not a chat window but a program that carries out the steps of a task on its own: it collects data, analyses it, prepares a result and records it in your system — and, where needed, waits for a person to approve it.

Recurring reports and analyses drawing on several data sources
Handling requests and documents: classification, data extraction, routing
Content and descriptions to your own rules — with a quality check before publication
Keeping task registers, deadlines and statuses in your company systems up to date
Monitoring: detecting deviations and alerting the right person

AI integration services for your existing systems

We connect language models with the systems you already use — ERP, CRM, your online store, email and documents. We build search across company documents (RAG) where every answer points to its source, and internal assistants for your teams. We choose the model to fit the task, independent of the vendor, and where you require data processing within the EU, we design the architecture around that requirement.

AI consulting and audit

Before any code is written, we show which processes are worth automating, which model and architecture fit them, what they cost to run and which risks need to be addressed. We support compliance with the EU AI Act and the GDPR on the technical and organisational side; the legal assessment of your case stays with your legal advisers.

Where AI pays off — and where it does not

Not every process should be handed to AI. Good candidates share a few traits — we check them at the start so that budget is not spent on automation that will not work.

Criteria for choosing a process for AI automation
Criterion	Good candidate	Weak candidate
Repetition	the task comes back daily or weekly	it happens only occasionally
Input data	available digitally, in systems or documents	scattered, in people’s heads
Checking the result	the result can be verified quickly	errors only surface months later
Impact of an error	reversible, with human approval	irreversible, without oversight
Rules	can be described and tested	change with every case

How we work

Short stages with a visible result and a single project lead.

Conversation and process map: what to automate first
A prototype on your real data, not a demo
A test set and quality measurement before rollout
Rollout with human oversight wherever a decision has consequences
Monitoring of model quality and running costs
Further development: next processes based on measured results

What we do not promise

We do not quote savings percentages up front or promise that AI will replace your team — the effect depends on the process, and we measure it on your data. We do not guarantee legal compliance; we support it technically. We do not deploy AI where an error is irreversible and nobody checks the result. AI in marketing — visibility in ChatGPT and Google AI answers — is part of our separate SEO and AI SEO service.

What we can build

AI agents

Programs that carry out the steps of a task and report the result.

LLM integration

Language models connected to ERP, CRM, e-commerce and email.

Document search (RAG)

Answers from company knowledge, always with the source.

Internal assistants

Support for sales, service and administration teams.

AI audit and strategy

Process map, model choice, costs and risks.

Operation and measurement

Quality and cost monitoring, development after rollout.

How much does AI implementation cost?

It depends on the process, the amount of data, the number of integrations and the running cost of the models. We do not publish a price list; after a conversation and a process map we quote the prototype stage, and we show running costs separately before any decision on rollout.

How is an AI agent different from a chatbot?

A chatbot answers questions. An AI agent carries out a task: it collects data from several systems, prepares a result, records it or passes it on and — where needed — waits for a person to approve it.

What about data and the GDPR?

We design the architecture around your requirements, including data processing within the EU and limiting the data sent to models. We support compliance with the GDPR and the EU AI Act on the technical side; the legal assessment stays with your legal advisers.

Which models do you use?

We choose the model to fit the task, independent of the vendor — from commercial models to open models running on your own infrastructure. Quality on your test set, running cost and data requirements decide.

How do you check that the AI works correctly?

Before rollout we build a test set from your real cases and measure quality on it. After rollout we monitor results and costs, and decisions with consequences are left for a person to approve.

Will AI replace my employees?

We do not promise that. Most often AI takes over the repetitive part of the work, while people handle exceptions and decisions. What this means for your business we measure on the prototype before deciding on the scale of the rollout.