AI Agents. API Integrations for Your Business
We develop AI agents for businesses that automate workflows, handle inquiries, generate marketplace product listings, integrate with 1C, CRMs, and enterprise systems, and much more.
Get an Analytics Report
Оставьте свой номер телефона и мы свяжемся с вами!
What Are AI Agents and What Can They Do?
What Are AI Agents and What Can They Do?
AI agents are software systems or robots equipped with artificial intelligence that can independently perceive their environment, make decisions, and perform tasks to achieve defined goals.
Capabilities of AI Agents
Data Perception
AI agents gather information from external sources such as sensors, cameras, textual data, audio, and more.
Information Processing and Analysis
They employ machine learning, deep learning, and natural language processing (NLP) methods to interpret data and understand context.
Decision-Making
Based on their analysis, they select optimal actions while considering predefined objectives and potential constraints.
Task Execution
They can automatically perform various operations: controlling robots, answering questions, conducting dialogues, planning routes, and optimizing processes.
Learning and Adaptation
Modern AI agents can learn from their mistakes and improve their performance without constant human intervention.
Use Cases
AI agents are software systems or robots equipped with artificial intelligence that can independently perceive their environment, make decisions, and perform tasks to achieve defined goals.
Capabilities of AI Agents
Data Perception
AI agents gather information from external sources such as sensors, cameras, textual data, audio, and more.
Information Processing and Analysis
They employ machine learning, deep learning, and natural language processing (NLP) methods to interpret data and understand context.
Decision-Making
Based on their analysis, they select optimal actions while considering predefined objectives and potential constraints.
Task Execution
They can automatically perform various operations: controlling robots, answering questions, conducting dialogues, planning routes, and optimizing processes.
Learning and Adaptation
Modern AI agents can learn from their mistakes and improve their performance without constant human intervention.
Use Cases
- Virtual assistants (e.g., Siri, Alexa)
- Delivery robots and autonomous vehicles
- Decision-support systems in healthcare and business
- Customer service chatbots
AI agents can be deployed across a broad spectrum of industries and business processes, encompassing areas such as:
-
Marketplaces:We SEO-optimize product descriptions to help your listings rank at the top of search results, provide granular sales analytics and breakdowns, support margin planning and identification of scale-up thresholds, automate the management of reviews and Q&A, and ensure instant handling of customer inquiries. -
Mining and Processing Industry:Integration of AI technologies into electrical prospecting, modernization of geological exploration through the fusion of artificial intelligence with electrical surveying methods. -
Marketing:Creation and automation of advertising campaigns, personalization of marketing messages, audience analysis and segmentation. -
Financial Sector:Analytics, processing and automation of accounting operations, forecasting, risk management, and budgeting. -
Human Resources (HR):Recruitment process automation, candidate assessment, assisting with employee onboarding and training. -
IT and Technical Support:Classification and resolution of technical requests, workflow automation. -
Legal Sector:Document and contract analysis, accelerating the processing of large volumes of information. -
Internal Process Automation:Project management optimization, planning, task allocation, and system integration. -
Logistics and Supply Chains:Inventory management, label generation, order tracking, and task delegation between teams. -
Creative Industries:Video and graphics generation, creation of production briefs and 3D models.
Request a Business Analytics Audit
The automation process includes:
Business analysis and requirements gathering
This methodology can be used when the organization maintains documentation that helps determine the client’s needs. Examples of such documentation include regulations, process descriptions, organizational structure, product specifications, various procedures, standards and instructions, document templates, and so on.
The identified requirements form the basis for further analysis and must be detailed. This methodology is applicable, for example, when automating well-established, regulated business processes within the organization.
The identified requirements form the basis for further analysis and must be detailed. This methodology is applicable, for example, when automating well-established, regulated business processes within the organization.
Technical solution and technical specification
A typical technical specification includes the following sections:
- Project purpose: describes the product’s user and business goals.
- User groups: includes the various categories of the project’s target audiences.
- Content overview: a major section of the specification that covers features and use cases.
- Interaction with other components: describes how and with which third-party components the product will integrate (for example, Yandex Maps).
- Interface overview: includes preferences for page structure and design, such as using the client’s brand style.
- Security: lists all mandatory security systems for the product, such as firewalls, antivirus solutions, and encryption systems.
- Development: if the client has technical preferences (e.g., a preferred programming language), they should be specified here.
- System environment: technical specifications of the servers and devices on which the product will be deployed.
Development, device assembly, machine learning
This is how representatives of leading tech companies and research institutions define machine learning:
- NVIDIA: “It is the practice of using algorithms to analyze data, learn from it, and then identify or predict something.”
- Stanford University: “It is the science of getting computers to work without explicit programming.”
- McKinsey & Company: “Machine learning is based on algorithms that can learn from data without relying on rule‑based programming.”
- University of Washington: “Machine learning algorithms can figure out how to perform important tasks on their own by generalizing from the examples they have.”
Implementation and debugging
Implementation and debugging of automation involve configuring automated systems, identifying and resolving errors, and monitoring process performance. The goal is to increase efficiency, minimize manual effort, and accelerate workflows.
Stages of automation implementation:
Stages of automation implementation:
- Rollout and staff training — gradually introducing automated processes into the company’s operations while training employees in parallel.
- Monitoring and optimization — continuously tracking the performance of automated systems to identify bottlenecks and opportunities for improvement.
- Diagnosing robots and triggers — analyzing condition logic and verifying the correctness of field and variable settings.
- Performance optimization — minimizing the number of actions within a single robot, using conditions to avoid unnecessary executions, and grouping similar operations.
- Logging key events — recording process start and end, capturing the values of key variables, and marking the attainment of important milestones.
- Creating process backups — making copies of processes before introducing changes.
The integration of AI agents is becoming a key strategic solution, universal for any company. Their implementation drastically enhances overall operational efficiency, transforming areas such as marketing, sales, finance, and human resource management. This goes beyond mere automation—it represents a fundamental improvement in the quality of operational activities through data-driven processes. Ultimately, businesses gain the ability to confidently scale their operations, enabling growth without traditional operational overhead.
How Business Analysis Turns Problems into Automation and Growth
How Automation is Born
Automation often begins with a specific business "pain point" that is clearly identified and articulated by the client themselves. This is the ideal starting point.
Such a request is not a problem, but an opportunity to launch a project effectively and serves as a substantive basis for the initial meeting.
- Example of a client's benefit statement: "I see a direct financial drain: high salaries for routine tasks and inefficient logistics spending. Automating one key process would deliver a rapid economic effect."
Such a request is not a problem, but an opportunity to launch a project effectively and serves as a substantive basis for the initial meeting.
The Power of Professional Business Analysis
To pinpoint the most accurate and effective solution, we immediately engage a team of analysts (business + systems analyst). Their task is to dive deep into the client's situation.
Analysts take on the challenging work of structuring chaos. Through a series of clarifying questions, they:
Analysts take on the challenging work of structuring chaos. Through a series of clarifying questions, they:
- Thoroughly clarify the root cause of the problem (the "pain point").
- Formulate specific requirements for the future solution.
- Identify hidden optimization opportunities.
From Business Talk to Technical Specifications (SRS)
Most clients cannot and do not want to prepare a detailed technical specification themselves. This is daunting and slows down processes.
- You don't need to be a technical expert!
- It is enough for the client to speak openly about their business and problems in meetings with analysts. This very dialogue becomes the raw material for the future technical specification.
Not Just a Solution, But a New Business Perspective
Involving analysts from the very beginning creates a powerful synergistic effect:
- The client begins to see their processes in a new light. During the collaborative work, non-obvious optimization ideas often emerge, which the client themselves is ready to propose.
- We transform these valuable insights into technologically sound solutions and develop a concrete, realistic action plan.
Case Studies
We'll find the right solution for your business