Analyst
Someone spends every Monday assembling the same report from the same five places.
The agent gathers the data, spots what changed and sends a short summary on a schedule.
Reporting Price monitoringAI agent development
for business
end to end
An AI agent takes the routine off your team and keeps the operations tidy: files a lead into the CRM, gathers company research, prepares documents or updates the tracker.
Get in touchAI and computer vision projects100+
Clients from30+ countries
Clutch rating5.0
Upwork rating5.0
Digital coworkers:
the roles agents take
A digital coworker is an agent with a role: its own slice of work, access to the systems it needs and a clear limit on what it may do. These are the roles clients ask for most often.
Sales assistant
Leads arrive from a dozen channels and half of them are entered by hand, late.
The agent reads the request, enriches it, files it into the CRM and assigns the owner.
CRM Lead routingDocument handler
Repetitive contracts and specifications drafted from a template, then checked by hand.
The agent drafts the document from your template and flags what deviates from the standard.
Contracts ChecksResearcher
Market and competitor research eats days and is stale by the time it is finished.
The agent searches, cross-checks the sources and returns findings with the links attached.
Web research CompetitorsHow it works
The agent takes the task, goes into your systems with it and comes back with the result along with an account of what it actually did there.
A weekly summary
What changed in competitor pricing this week?
Three of nine tracked SKUs moved. Two competitors cut entry-tier prices by about eight percent; one raised shipping. Full table attached.
Collected from 9 sourcesA new inbound request
A request just came in through the contact form.
Filed as a lead, matched to an existing account, enriched with company size and industry, and assigned to the owner for that region.
CRM: lead 8842 createdA contract draft
Draft the supply contract for this customer.
Draft ready from your template. Two clauses deviate from the standard: the payment window and the liability cap. Both are marked for review.
Template: supply v4What AI does for the business
Gartner
Harvard Business Review
Yakov & Partners
We will show you where AI pays off in your business — in one hour, free of charge
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How we build
an AI agent
4 Stages
Stages
4
- 01
Pick the process
We choose one routine with a measurable cost and describe what "done" means for it.
- 02
Design the agent
We define its role, the systems it may touch and the limits of what it can do unattended.
- 03
Build and connect
We wire it into your systems, add validation in code and a human checkpoint where an error is expensive.
- 04
Run and extend
We start it on a narrow scope, watch the results, then widen it to neighbouring tasks.
How the solution is built
We keep it lawful and sensible: data is processed to your compliance requirements, and the model and the hardware are chosen for the task, without overpaying for tokens or idle servers.
Your data stays yours
Documents and conversations never leave your perimeter. That keeps the solution compatible with GDPR, HIPAA and internal security policies.
On-prem or cloud
We deploy on your servers or in your cloud, whichever your data policy requires. We size the hardware for the load or fit into what you already run.
The model fits the task
Cloud LLMs or open-source models inside your perimeter. We test candidates on your own scenarios during the first stage and pick by result, not by hype.
Get in touch
Frequently asked questions
01 How is an agent different from an assistant?
An assistant answers questions. An agent acts: it goes into your systems, performs the steps and reports what it did. That is why an agent needs explicit boundaries, permissions and, for costly actions, a human confirmation.
02 Can it act without supervision?
For low-risk steps, yes. For anything that costs money, touches a customer or changes a record that matters, we put a human checkpoint in front of it. Where the boundary sits is a decision we make together, per scenario.
03 What does it integrate with?
Any service with an API. For systems that need a richer contract we build connectors on the Model Context Protocol, which is the standard way for a model to reach outside tools.
04 How do you keep it from doing damage?
Narrow permissions, a written list of allowed actions, validation in code before anything is written back, and a full log of every step. If a step fails its check, the agent stops and hands the task to a person.
05 Can we run everything on our own servers, without external clouds?
Yes, though it usually costs more: a self-hosted model needs a GPU server, rented or bought. So while the task involves no sensitive data, most teams start on cloud LLMs. When the data is sensitive, we deploy an open-source model inside your perimeter: the model, the knowledge base and the logs never leave the company, which is what GDPR and internal security reviews ask for. We do it end to end, including the servers, the deployment and the access rules.
06 What happens after launch?
Business processes change and the solution grows with them: we refresh the knowledge base as your policies and products change, extend the scenarios to neighbouring tasks, and move to stronger models as they ship. You can run this yourself or hand it to us as support.
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