AI assistants
Corporate assistants for employees and clients that work with a defined context, internal data, and connected business systems.
AI and automation
AI implementation for business
We integrate artificial intelligence into existing business processes and software systems: operations automation, data and document processing, intelligent search, AI assistants, analytics, and work with the company’s internal information.
First we define a specific business task, and only then choose the architecture, models, data sources, and how AI will integrate into the existing infrastructure.
Implementing AI in business processes
Artificial intelligence is worth implementing where it solves a measurable task: reducing manual work, speeding up information processing, helping employees find data, classifying inquiries, analyzing documents, or automating selected process stages.
Before development we define the current process, input data, expected result, acceptable error level, and how AI output will be controlled.
AI solutions that can be implemented in business
Document processing
Extraction, classification, verification, and structuring of information from documents, requests, files, and text data.
Intelligent search
Search across internal knowledge bases, documents, instructions, and corporate information, taking the user’s query context into account.
Inquiry automation
Classification of client requests, determining topic, priority, and the further route for handling the inquiry.
Data analysis
Using AI to process large volumes of textual and structured information, find patterns, and prepare results for employees.
AI inside business systems
Integrating AI functions into CRM, ERP, corporate platforms, client portals, and other company software products.
Business automation with artificial intelligence
AI automation is used where ordinary rules and scenarios are not enough. The system can analyze unstructured text, documents, inquiries, and other data, then pass the result to the next stage of the business process.
Where needed, AI works together with classic automation: the model analyzes data and forms a result, and the software system performs specific actions by predefined rules.
A conversational interface for the website and channels: chatbot development.
AI and classic business process automation
Not every task needs artificial intelligence. If a process can be reliably described with fixed rules, ordinary automation is often the more predictable and economical solution.
AI is appropriate where you need to work with language, context, documents, large volumes of information, or tasks that are hard to fully describe with hard conditions.
Classic automation without AI: business process automation.
AI for working with internal company data
An AI system can work with corporate documents, knowledge bases, CRM data, instructions, catalogs, and other internal information if the project requirements and access policy allow it.
Architecture must account for which data a given user can access, where it comes from, and which limits apply when passing information into AI components.
Intelligent search and knowledge-base work
For corporate information, a system can find relevant documents or data fragments and pass them to the model as context for forming an answer.
This approach uses the company’s current internal information and reduces how much the answer depends on the model’s general knowledge.
Integrating AI with CRM, ERP, and corporate systems
An AI component can be built into existing CRM, ERP, corporate platforms, websites, client portals, and internal services. Employees keep working in the familiar system, and AI becomes part of a specific work scenario.
For example, the system can analyze an inquiry, find related information, draft a reply, or classify a request, then pass the result to an employee or the next process stage.
More: CRM development, ERP development, and corporate systems.
AI model and API integration
Depending on the task, the system can use external AI APIs or a separate model deployed in suitable infrastructure. The choice depends on data requirements, speed, result quality, request cost, and project specifics.
Architecture should not hard-bind business logic to one specific AI provider if the project may later replace or compare models.
AI implementation is one of the directions of our software development.
An AI assistant can run in a messenger as an AI bot in Telegram.
AI features inside a mobile product: iOS and Android app development.
Solution layer
A.01Task
A.02Data
A.03Model
A.04API
A.05Access
A.06Control
Result control and employee involvement
In critical processes the AI result does not have to trigger an action automatically. The system can first pass the result to an employee for review and confirmation.
The automation level is set by error risk: some processes can be fully automatic, others require mandatory human control.
Data access and security
When implementing AI it is necessary to define in advance which data the system may use, who has access to it, and which limits apply to individual users and processes.
AI functions must follow the existing access-rights model and must not give a user information they cannot access in the source system.
AI implementation stages
01
Task analysis
We define the business process, current employee actions, input data, expected result, and criteria for the usefulness of the AI solution.
02
Data review
We analyze data sources, information quality, document structure, access limits, and whether the data can be used in the chosen scenario.
03
Prototype
We build a limited prototype to check model quality and whether AI actually solves the stated task.
04
Integration
We connect AI to existing systems, APIs, databases, or interfaces and implement the required business logic around the model.
05
Testing
We check result quality on real scenarios, edge cases, error handling, access rights, and how integrations work.
06
Launch and evolution
After launch we analyze how the solution works, adjust scenarios, connect additional data sources, and expand automation.
Validate the hypothesis first, then scale
For AI projects it is not always rational to build a large system immediately. At the first stage it is better to test the key hypothesis on a limited data set and real scenarios.
If the prototype shows sufficient quality and economic value, the solution is integrated into the working infrastructure and scaled gradually.
Cost of implementing artificial intelligence
The cost of an AI project depends on task complexity, the number of data sources, required integrations, quality requirements, the scope of automation, and the chosen solution architecture.
The cost of using external AI APIs or the required infrastructure may be counted separately. At the first stage we define the scenario and can estimate a prototype, after which the scope of full integration becomes clear.
An AI solution around your company’s processes
We do not start a project by picking a fashionable model or technology. First we define the process and a specific business task, then choose the automation approach, and only after that design the AI components.
This approach uses artificial intelligence where it actually delivers a practical effect, and avoids complicating processes without need.
Frequently asked questions about AI implementation
The cost depends on the task, data volume, integrations, quality requirements, and automation level. At the first stage a prototype can be estimated, then the scope of full integration is defined.
Yes. AI can be built into existing CRM, ERP, corporate platforms, and other systems via API and internal business logic.
Not necessarily. In many cases AI can be connected as a separate service or module that talks to the existing system via API or another available integration mechanism.
Yes, if the project architecture and access rules allow those data to be used. In design it is important to define information sources, user rights, and limits on data transfer.
It depends on error risk and process requirements. Some tasks can be processed automatically; for others the AI result must first be reviewed by an employee.
It is better to start with one specific task and a limited prototype. This lets you check AI quality on real data and understand the practical value of the solution before large-scale development.
Let’s discuss a task for AI automation
Tell us which process takes a lot of time, what data employees work with, and what result is needed. We will help determine where AI actually makes sense and which prototype to start with.