Support and managers answer the same questions by hand while the request queue keeps growing.
Artificial intelligence in business
AI integration for business
We embed AI into working processes: AI assistants for customers and staff, RAG bots grounded in your documents, automation of routine operations. We start with a pilot so the effect is measurable before scaling.
- AI assistants and chatbots
- RAG over your documents
- Routine work automation
- Integration with CRM and ERP
After analysing the task we propose a scenario, a model and an estimate: what will actually pay off in your case.
What AI integration is
AI integration means embedding AI models into a company's processes: an assistant answers customers and employees, a RAG bot finds answers in your policies and knowledge bases, and automation takes over routine work with requests and documents. A working implementation is more than "plugging in ChatGPT": data quality, integration with CRM and back-office systems, and post-launch support matter just as much.
When AI adoption pays off
Four signs that a task is a good fit for AI integration.
Company knowledge is scattered across documents, chats and people's heads: finding an answer takes hours.
Routine processing of requests, emails and documents eats team time that costs more than automation.
Leadership wants to use AI but it is unclear which task to start with and how to measure the effect.
Typical implementation scenarios
Four scenarios where working with AI in a company usually starts.
01
AI assistant for customers
Answers in the website chat and messengers, qualifies requests, hands complex questions to an operator and logs the history in CRM.
02
RAG bot over a knowledge base
Answers questions from your documents, policies and manuals, relying on sources rather than the model's general knowledge.
03
Request and document automation
Recognition, classification and routing of requests, emails and documents: routine triage moves from people to the system.
04
AI inside CRM and back-office systems
Draft replies, summaries of correspondence and calls, next-step suggestions right in the tools your team already uses.
See scenarios broken down by industry and task on the AI use cases.
What the work includes
The engineering side of an implementation we take responsibility for: from data analysis to support.
Task and data analysis
We pick a scenario with a measurable effect and check whether the data is sufficient to deliver it.
Model and architecture selection
Cloud LLMs or deployment inside your perimeter: we choose the option that fits your security requirements and budget.
Knowledge base preparation
We structure documents and data for RAG so the assistant answers from your sources.
Integration with systems
We connect the solution to CRM, ERP, your website and messengers via APIs and webhooks.
Pilot and testing
We verify answer quality on real questions and data before the production launch.
Support and growth
We monitor quality, update the knowledge base and improve scenarios under contract.
Data security in AI implementation
AI solutions work with a company's production data, so protection is part of the project by default.
NDA and contract
Confidentiality is secured legally before any data or access is handed over.
Access control
The model and integrations receive only the data needed for the task. Access is split by roles.
Your perimeter or the cloud
We deploy in the cloud or on the company's own servers: the choice depends on your data requirements.
Support under SLA
Incident response time and the maintenance procedure are fixed in the contract.
How we work
A transparent process from task analysis to launch and support. We work with companies in Uzbekistan, Kazakhstan and other CIS countries.
Step 01
Task analysis
We study the process, the data and the goals, and pick a scenario where AI delivers a measurable result.
Step 02
Design
We select the model and architecture, and agree on the scenario, quality metrics and pilot plan.
Step 03
Pilot
We run the solution on real data, verify answer quality and collect feedback.
Step 04
Integration and launch
We embed the solution into CRM, ERP and working tools, train your staff and launch into production.
Step 05
Support and growth
We monitor quality, update the knowledge base and improve scenarios based on metrics.
Cost depends on the scenario and the data
We name the exact cost and timeline after analysing the task. They depend on the scenario, the chosen model, data volume and quality, the number of integrations and deployment requirements. A pilot lets you verify the effect before investing in a full rollout.
Frequently asked questions
How long does AI implementation take? ＋
A pilot usually takes from 2 weeks; a full launch depends on the number of integrations and data volume. After analysing the task we give you a concrete plan and dates.
Can you integrate AI with our ERP, CRM or website? ＋
Yes. We connect AI solutions to 1C, Bitrix24, amoCRM, websites and messengers via APIs, webhooks and ready-made connectors.
Can AI run in the cloud or on our own servers? ＋
Both. We deploy solutions in the cloud or on-premise, inside the company's perimeter: the choice depends on your data and infrastructure requirements.
How is data security ensured? ＋
We sign an NDA, split access by roles and pass the model only the data needed for the task. On request we deploy the solution inside your own perimeter.
How do we control the cost of an AI solution? ＋
The architecture is transparent: you can see which requests cost what. We set usage limits and reporting, and scale only what delivers results.
Which tasks are most often automated with AI? ＋
Answering customer and employee requests, knowledge base search, triaging requests and documents, drafting replies and summarising correspondence.
What do you need from us to start? ＋
A short description of the task, access to sample data and a contact person on the business side. We then propose a pilot scenario and a work plan.
What happens after launch? ＋
We maintain the solution under contract: monitor answer quality, update the knowledge base and models, and improve scenarios based on your metrics.
Tell us about your task
Describe the process you want to strengthen with AI. We will propose a scenario, an approach and an estimate within 1 day.
Working hours: Mon-Fri 09:00-19:00 · Email: hello@axium.uz
What happens next
- Intro call: We clarify business goals and context. If needed, we sign an NDA immediately.
- Requirements analysis: We review your operational flow, identify technical risks, and map integration points (1C, CRM, ERP).
- Plan and estimate: We prepare a transparent proposal (SOW) with stages, timeline, and a fixed budget framework.
Confidentiality guaranteed • Engineer response, not a bot
Related services
AI implementation rarely comes alone: it usually goes hand in hand with systems integration and custom development.