AI integration and AI agents
AI integration for business and AI agent development for what already runs at your company. We start from the task rather than the technology: first we work out where a model genuinely takes work off people, and only then we build.
There is a lot of noise around AI, and both sides suffer from it. Some roll out a model because they feel they have to, and end up with an expensive toy nobody uses. Others stay away from the subject entirely and keep doing by hand what could have been dropped long ago.
We come at it from the other end. We look at where people time goes: where they retype the same thing, hunt for a document through ten folders, answer the same question for the twentieth time in a day. Then we count what that costs per month and compare it with the cost of the rollout and of running the model. If it does not add up, we say so.
A model is almost never a product on its own. It is useful inside what already works: in your CRM, in the customer account, in support, in the document management system. So most of the work is not prompts but integration, access rights, data and verification that the answers can be shown to people.
Data deserves a separate conversation. If it is sensitive to leaks, the model is deployed locally, on your servers, and nothing goes outside. Such a model can be fine-tuned for your processes and your terminology. It costs more than connecting to an external service, but the data stays with you.
Next to this task usually stand business process automation, bots for messengers and CRM and analytics. The model is embedded into corporate systems, into server software and into the backend. If it is not yet clear what to automate first and whether it will pay off, start with business consulting.
What the service covers
Any combination of the items below can be taken on its own.
An agent that walks through a chain of steps by itself: finds the data, fills it in, sends it, reports back. With exactly the permissions you granted and a log of every action.
Answers drawn from your knowledge base, contracts, policies and correspondence, with a link to the source so the answer can be checked.
Parsing incoming mail and tickets, classification, draft replies, prompts for the operator. A human stays in the loop wherever the cost of a mistake is high.
Deployment inside your perimeter, with fine-tuning for your processes where needed. Not a single document leaves your side.
CRM, mail, messengers, internal services, document management. The model is embedded where people already work rather than opening in a separate tab.
Sets of check questions, accuracy measurements, limiting answers to the boundaries of your data, logging and analysis of mistakes.
How we work
We count how much time and money the task takes today and how much will be left after the rollout. If the benefit does not add up, we say so before the work starts.
We build a working version on real material rather than on a demo set. This is where it becomes clear what the model can do in your case and what it cannot.
Integration into working systems, access rights, logging, help for the staff in getting used to the tool.
Runs against check sets, measurements and tuning of the limits, so answers stay within the boundaries of your data.
We watch the quality and the costs, update for new model versions and add scenarios.
Stack and tools
Frequently asked questions
With one task where people time is visibly and measurably spent. In two to four weeks we build a prototype on your data, and the decision is then made on facts rather than on a presentation.
If that is a condition, we deploy the model locally, inside your perimeter. Then the documents never leave your servers at all. If there are no such restrictions, we connect to external models, which is cheaper.
We limit answers to your data, require a link to the source, keep a human in the loop wherever the cost of a mistake is high, and keep a log that lets a mistake be traced.
Usually not. Most tasks are covered by an existing model plus properly arranged access to your data. Fine-tuning makes sense when you have your own terminology or your own settled processes.
Everything is set by the contract, and we put it in writing at the specification stage, before the work starts. During the project the team works either on our infrastructure or on yours: the choice depends on the complexity of the project and on whether you already have infrastructure in place. Every approved stage is transferred to your servers and your repository, so the work accumulates on your side rather than ours. Once the project is closed, all code, materials and documentation are handed over to you together with the rights to them.
Yes, under a separate agreement: quality monitoring, control of model costs, updates and new scenarios.
Related services
Shall we discuss your task?
Tell us what needs to be done and we will come back with an estimate, the team composition and a plan for the first two weeks.