AI & Automation
Chatbots, RAG and automations that take repetitive work out of the way, applied with technical judgment and measured in production, not adopted for hype.
AI where it solves, not where it impresses
Most of what is sold as artificial intelligence today solves a problem you do not have. We start from the opposite end: we look at your process, find where the expensive repetition lives, and only then ask whether a language model is the right tool. Often it is not, and a conventional automation delivers the same result at a fraction of the running cost.
When AI is the answer, it has to know your context. An assistant that replies from the model's general knowledge is of little use. We connect the model to your knowledge base, your documents and your systems, using RAG when the question requires retrieving the information before answering. The result is support that cites its source instead of inventing one.
Not every automation needs a model. A good share of the gain comes from connecting systems that today talk to each other through spreadsheets and email: an order arrives, triggers a check, updates the stock and notifies the right team. We build these flows with the same discipline as any of our software, with error handling, logging, and room for a person to step in when a case is ambiguous.
AI without measurement is a gamble. Before scaling, we define quality and cost metrics per interaction, and we track both in production. That lets us answer the questions that matter: is the assistant getting it right? Did it get more expensive after the last model swap? Is it worth changing providers? Without those numbers, any decision about AI is just an opinion.
- Assistants and chatbots with your business context
- RAG (retrieval-augmented generation) over your own data
- Process and workflow automation across systems
- Integration with models, providers and APIs
- Quality and cost evaluation per interaction
- Guardrails, error handling and human escalation
- Production monitoring and observability
- Documentation and knowledge transfer
Discovery
We understand the problem, the context and the constraints before writing the first line.
Build
We prove the value with a lean pilot, running on real data and with a metric defined from the start. If the pilot does not pay off, you find out early and cheaply, before scaling.
Evolution
We put it into production with continuous evaluation of quality, cost and security, and with the option to swap models without rewriting the application when the market moves.
When is RAG worth it?
When the assistant has to answer from your content rather than from the model's general knowledge. If the right answer sits in a contract, a manual or a support ticket history, RAG is the way. If the question is generic, a good prompt costs less and does the job. We assess this during discovery, before building.
How do you control cost and quality?
We measure. We define quality and cost metrics per interaction before the pilot, and we track both in production. Every change of model, prompt or provider is compared against the baseline. That turns a decision people usually make on instinct into one made with numbers on the table.
Will my data train another company's model?
It depends on the provider and the plan, and that is a decision we make with you during discovery. We work with configurations where your content does not feed third-party model training, and we put that in writing. When the data is sensitive, we assess running the model inside your own infrastructure.
What if the assistant answers incorrectly?
We assume it will, and we design for it. The assistant cites the source it used, so you can check. Questions outside its scope get an honest answer that it does not know, rather than a guess. And ambiguous cases escalate to a person, with the conversation history attached.
Do I need to replace my current system?
No. AI comes in as a layer on top of what you already have. We integrate with your system through an API, a database or a message queue, with no migration. If discovery reveals that the current system does not expose the data we need, we treat that integration as part of the scope.
Do you lock the project into one AI provider?
That is not what we aim for. We isolate the model call behind a layer of our own, so switching providers is a configuration change, not a rewrite. The model market moves fast, and today's cost per token is not tomorrow's. That flexibility is part of the design.
Let's get your idea off the ground
Investment is handled later, in the proposal, after the discovery call.