AI Business: enterprise solution

More objective data for management decisions: employee statistics, call quality analysis, and agreement compliance analysis

More time for high-value work by cutting routine tasks by 1-2 hours a day for everyone, from frontline employees to top executives

Higher employee satisfaction: faster onboarding, fewer repetitive questions, fewer routine tasks

Implement an AI assistant

Executive efficiency

How does AI improve manager efficiency?

1
No need to repeat yourself

Feedback delivery is automated
2
Automatic control of procedures and routine work
3
Manage procedures in plain language

unlike standard chatbots and bots
4
Employees ask questions to AI

instead of turning to their manager for every issue

Team time

Your team is losing time on these tasks

1
Eliminate routine work

Report and minutes generation is automated. Standard forms are available with one click in a user-friendly interface
2
Direct conversation with information

To get analytics and draw conclusions, you do not need an intermediary analyst.
3
Fast feedback delivery

an objective operating system based on current rules and procedures. It is easier to eliminate errors when the rules are clear and visible
4
Questions can be asked to a fast AI instead of an always-busy manager

and get an answer right away.

AI Assistants

AI assistants are personal helpers for managers and employees

AI assistants are personal helpers for managers and employees who know everything about your processes, clients, and employees and can work with that information. For example...

Help prepare for a call

The AI CALLS assistant will find any information about previous interactions with a client or team and provide a brief summary of agreements.

Learn more about AI CALLS

Discuss implementation

What holds businesses back

What is stopping businesses from introducing AI assistants into their processes?

Off-the-shelf solutions do not fit

Existing off-the-shelf solutions do not match business goals and processes, and customizing them requires understanding how AI works under the hood

Confidential data

Documents and calls contain a lot of confidential information that businesses cannot store in third-party cloud services

Long retraining

Using an off-the-shelf solution would require extensive retraining and adding extra steps to daily routines

The foundation of AI agents

We build the AI layer on open source and proven solutions

Our goal is not to add a trendy chat interface, but to build a controllable foundation for AI agents: with data control, transparent logic, an activity log, and the ability for your team to evolve the system.

1

Open source at the core

We build the foundation of the agent infrastructure on open source components that can be deployed within the company's environment, tested, customized, and maintained without depending on a closed-box product.

2

Proven solutions instead of experiments

We use only tools that have been practically validated: models, knowledge bases, orchestrators, RAG, monitoring, action logging, and security controls.

3

The company becomes AI-agent powered

Agents get access to procedures, communication history, CRM, tasks, and corporate systems so they can do more than answer in chat and instead take on repetitive managerial and operational scenarios.

Solution

We know how to bridge the gap between your processes and the technologies available on the market

Seamless integrations into your workflows

you will work with familiar interfaces but get more from them: in a chatbot, spreadsheets, CRM, and so on

Data security

We will ensure this by deploying language models on your isolated servers and adding data anonymization and a security guard

We implement on your stack

any language and framework that your IT team can easily maintain.

We will fine-tune the tools to your unique requirements

Any call evaluation criteria, any integrations with databases and document repositories, and integrations with any chatbots and enterprise systems.

Assistant catalog

We implement AI assistants

Call Analytics

Deployment of an AI assistant that takes meeting minutes, analyzes compliance with regulations, records agreements and checks against historical information. Ask any question about a specific call or category and get detailed answers.

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Compliance with Regulations and Rules

With this AI assistant, all your company's current regulations, rules and instructions are easy to find and follow.

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Tender assistant

The tender AI assistant gathers all suitable tenders for you, assesses how promising participation is, and helps break down and estimate the customer's specifications. Grow your tender participation funnel by 3-10x and your tender-to-order conversion by 10% or more.

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Quality and Deviation Control Agent

The agent records deviations from procedures and agreements, asks the employee for the reason, checks it against the client communication history, and suggests an explanation for the manager: what happened, why it likely happened, and what next step reduces the risk of recurrence.

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Tender Assistant for the Procurement Department

An intelligent assistant for companies that participate in government and corporate tenders as buyers of goods and services. The system automates the entire tender procurement cycle: from finding suitable procedures to submitting bids.

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AI layer

An AI assistant must take action, not just reply with text

Core thesis of the AI block: a pilot with measurable impact, private data under control, agent actions logged, quality passes evals before scaling.

1-2 hof daily routine the assistant takes off an employee or manager

2-4 wksenough for a pilot on one process with an impact metric

40%of agentic AI projects Gartner expects to be canceled without clear value

Assistant ≠ chatbot

A chatbot answers; an assistant checks the regulations, queries systems, records the deviation and proposes the next step.

Control plane

Agent registry, owner, permissions, memory, evals, trace logs, kill-switch and budget at the enterprise-layer level.

Data

RAG returns an answer with a source citation; LLM Gateway obfuscates personal data before the model and restores it after the response.

Processcorporate memoryagentaction in the systemlogs and evals

Internal links

Agent governance layer RAG with sources LLM Gateway and personal data

Sources

Anthropic: Building effective agents Gartner agentic AI cancellations Chip Huyen: AI Engineering

Contacts

Let's Discuss Your Project

Leave your current contact details and describe your task. We will come back with clarifying questions and a proposal for the next step.

AI assistants for large and mid-sized businesses

How does AI improve manager efficiency?

No need to repeat yourself

Automatic control of procedures and routine work

Manage procedures in plain language

Employees ask questions to AI