AI Automation
AI Automation
Intelligent automation of business processes: AI agents, RAG knowledge bases and workflows that take over routine operations and free your team for decisions.
Who it's for
- Companies with repetitive operations and large volumes of routine
- Sales, marketing, support and analytics departments
- Businesses that want to cut operational costs
Outcome
- Automated processes: from request to report
- Employees work on tasks, not routine
- Transparent automation efficiency analytics
Problems it solves
Ad-hoc automation — scripts and macros — does not scale. We build AI automation as a system: processes, agents, integrations and control.
Who it's for
Employees manually move data between systems
Processing requests and leads takes hours every day
Reports are assembled manually and become stale
Automation rules break when formats change
No single picture of where AI actually saves time
When you need it
A process repeats with the same steps more than once a week
You have structured data: requests, documents, leads
You need integration: CRM, Telegram, email, spreadsheets
You want to measure time savings in money
Approach
We start with process diagnostics, calculate savings, then build automation in stages: from a single process to a system.
01
Discovery
Research, requirements and AI opportunity analysis
02
Architecture
AI system design: agents, RAG, data layer
03
MVP
Working product delivered in weeks, not months
04
Production
Deployment, monitoring, scaling and iteration
Technical expertise
Automation technologies proven on our products.
AI Agents
Agents for processing requests, data and reports
OpenAI / Claude / Gemini
LLMs for analysis, classification and generation
OpenRouter
Flexible model selection per operation
RAG
Knowledge base for automation with context
React / Next.js
Dashboards and management interfaces
PostgreSQL
Process data and metrics storage
Expert articles
How we approach this in our own products — from the knowledge base.
FAQ
Which processes are best suited for AI automation?
Repetitive data operations: request processing, lead qualification, report preparation, document flow, first-line support. We start with the processes with the highest frequency and clearest rules.
How does an automation project begin?
With diagnostics: we review your processes, calculate the time and cost of manual operations and select processes with the highest ROI. Only then do we design the solution.
Will AI automation replace our employees?
No. AI takes over routine, and employees gain more time for client work and decisions. The project report shows the redistributed hours.
Which systems can you integrate with?
CRM, Telegram, email, spreadsheets, internal APIs, databases. If a system has an API — we connect it. Access and permissions are configured by role.
How do you measure automation results?
Before kickoff we record process metrics: time, cost, errors. After implementation we compare and show the savings. The automation dashboard stays with you.
How quickly does AI automation pay off?
It depends on the process. A typical case: a process with 20-40 hours of manual work per week is automated in 4-8 weeks and pays off within the first months.