[01] Assistants that work for you
AI agents for business: development and deployment
We deploy artificial intelligence where it saves the team's time: handling requests and documents, searching the corporate knowledge base, preparing proposals and analytics. A mistake in such tasks is costly, so a human stays in the loop.
Can be complemented with: Integrations, bot, automation
[02] Description
An AI agent is not a chat, it is a task performer
An ordinary AI chatbot answers questions. An AI agent goes further: it receives a task, turns to your data and services, performs a sequence of steps and returns a result — a processed request, a prepared proposal, a found document. That is why we start not with choosing a model but with describing the process: what the agent should do, where to take data from and who checks the outcome.
Building an AI agent for business includes connecting it to your CRM, email, messengers and documents, setting up access rights and testing answer quality on your own examples. The agent can be embedded in a platform, a bot or the tools employees already use.
[03] Tasks
Where AI gives a practical result
- An AI assistant for employees: answers on regulations, contracts and internal instructions
- AI for customer service: sorting requests, suggesting replies, handing complex cases to a person
- An AI bot for request processing: classification, filling in cards, assigning owners
- AI for document processing: extracting data, cross-checking, preparing drafts
- Search over the corporate knowledge base — a RAG system that cites its sources
- AI data analytics: summaries, anomaly detection, answers to questions about reports
- Sales and PR agents: preparing proposals, texts and materials from company templates
[04] Description
Knowledge-base search and document work
Company knowledge is usually scattered across folders, email and employees' heads. A RAG system for business indexes your documents and answers questions about them, showing the source of every answer. An employee does not reread a hundred pages of regulations but checks a quote. Access to materials is separated exactly as in the original systems.
Document automation with AI helps where the flow is repetitive: invoices, acts, forms, requests. The agent extracts the needed fields, cross-checks them against the accounting system and prepares the result for review. A human makes the decision — the agent saves preparation time.
[05] Description
Introducing AI into business: step by step and under control
We do not promise that AI will replace a department. First we find one task where it applies, test the solution on a limited scenario and compare the result with what was before. Where a mistake is critical, we provide confirmation by a person, limits on the agent's actions and a log of its decisions.
Data analytics and custom BI systems are a separate line of work: dashboards for managers and a data visualisation platform to which an agent can be connected for questions in natural language.
[06] Client request
«I need to analyse requests, pick solutions and prepare proposals»
[07] Description
What you get in the end
A working agent inside your processes, not a demonstration of what a model can do. We measure the result on your data: how many requests the agent handles without corrections, where it makes mistakes and how much time it saves the team. On those figures you decide whether to extend the scenario.
You get a description of the scenarios, access settings, a log of the agent's decisions and an instruction for employees on how to check its answers. The agent stays manageable: any rule can be changed without restarting the project.
[08] How the work goes
How we deploy an AI agent
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Stage01
Finding the task
We study the processes and pick a scenario where AI gives a tangible effect and the result can be measured.
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Stage02
Data preparation
We define the sources, set up access and check the quality of the materials the agent will work with.
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Stage03
Pilot
We launch the agent on a limited scenario and collect errors and feedback from employees.
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Stage04
Control
We add human confirmation, limits and an action log.
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Stage05
Scaling
We extend the scenarios and connect integrations, automation and a bot.
[09] Experience
Case studies and products in this area
AI agent for request handling
The agent classifies incoming requests, finds solutions in the knowledge base and drafts replies.
Sales team agent
An AI agent that qualifies inbound leads, answers routine questions and prepares commercial proposals.
PR agent
Monitors the media landscape and prepares talking points and draft publications for the PR team.
Content agent
Produces content in your tone and to your rules: texts, posts, descriptions, with human review.
[10] FAQ
Frequently asked questions
Where do we start if we want AI but do not know where?
With a consultation and a process review. We look for repetitive tasks with a clear result — handling requests, searching documents, drafting replies — and test the solution on a limited scenario.
Can an agent be trusted with critical decisions?
Where a mistake is costly, the agent prepares the decision and a person confirms it. We set limits on the agent's actions and log its work.
Where does our data go?
We choose the deployment to fit your data requirements: by agreement, processing can run on your own infrastructure. Access to materials is separated, and we design personal data processing with 152-FZ in mind.
How is a RAG system different from ordinary search?
It does not merely find documents but composes an answer from them and shows the source, so the answer can be checked quickly.