AI agents & assistants
We are not tied to one model vendor. The right choice depends on the task, the data, the latency you need and the budget, and it changes every few months.
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Why model choice matters
Models are genuinely different from one another. Some are stronger at long-document reasoning, some at structured extraction, some at code, some are dramatically cheaper for high-volume classification where the task is simple enough not to need the expensive one. Some can run on your own hardware. Committing a whole company to one vendor because a demo went well is how organisations end up paying several times more than necessary, or discovering a capability gap they cannot work around. We build so the model is a configuration choice, not a rewrite, because the frontier moves every few months and today's best choice will not be next year's.
Evaluation before rollout
The hardest part of shipping AI is not building it, it is knowing whether it is good enough. So we build an evaluation set first: real examples from your business with known correct answers, scored automatically. That turns "it seems to work" into a number you can defend. It also makes changing model a measured decision rather than a leap of faith, and it catches regression when a provider silently updates a model underneath you, which happens more often than people expect.
Agents that act, with limits
An assistant answers. An agent does things: looks up an order, drafts a reply, raises a ticket, updates a record. That is where the value concentrates and where the risk does too. So actions are scoped explicitly, logged, and reversible. Anything with financial or contractual consequence proposes rather than executes, with a person approving. We would rather ship something narrower that people trust than something sweeping that gets switched off after one bad week.
What you get
Model chosen per use case
OpenAI, Claude, Gemini or open-weight. The model is a configuration choice, not a rewrite.
Measured, not assumed
An evaluation set from your real cases with known answers, so quality is a number rather than an impression.
Grounded in your data
Answers from your systems and documents rather than a model's general knowledge of your industry.
Actions scoped and logged
What it may do is explicit and recorded. Anything with financial consequence proposes for approval.
Running cost modelled
Token consumption is a real operating expense. We size it before building, not after the first invoice.
Protected against silent drift
Providers update models underneath you. The evaluation set catches it before your users do.
Technologies
Frequently asked questions
Which AI model should we use?
It depends on the task, and anyone answering without knowing the task is selling something. Long-document reasoning, structured extraction, high-volume classification and code generation have genuinely different best answers, and cost varies by an order of magnitude. We evaluate candidates against your actual cases.
Where does our data go?
Wherever you decide. Commercial APIs with contractual commitments not to train on your data, deployed in a region you choose, or open-weight models running on infrastructure you control. Companies with genuine data sovereignty constraints have real options now.
What does it cost to run?
Token consumption, which varies enormously with the model and how much context each call carries. This is a real operating expense that should be modelled before building. It is also where a cheaper model for the simple half of the work often halves the bill.
Find the right model for the job, not the one from the demo
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Serbia