Sales
Incoming inquiries, lead qualification, response preparation, CRM data capture and manager assistance
AI automation · Minsk and across Belarus
AI Business Process Automation
Show us a manual business process. We identify what AI should handle, what conventional automation can solve and which systems need to be connected
In short
We start with process assessment. For a suitable task, we build a first working AI prototype, validate the scenario and then define the production implementation scope
- sales and lead processing;
- customer service;
- documents, RAG and company knowledge;
- CRM, ERP/1C and APIs;
- repetitive employee operations
What we automate
Specific work: not “AI in general”
Customer service
Routine answers, knowledge-based AI assistance, inquiry routing and structured handoff to employees
Documents
Extraction, classification, reporting, search and controlled use of company knowledge
Internal operations
Recurring actions, reporting, notifications, cross-system data collection and synchronization
Marketing
Content preparation, marketing data processing, inquiry analysis and routine communication workflows
Delivery process
Assessment → prototype → integration → production
1. Map the process
We document employee actions, systems, incoming data, constraints and the required outcome
2. Find automation points
We decide where AI fits, where ordinary automation is enough, which APIs are needed and where human control remains
3. Build a prototype
For a suitable scenario, we create a minimal working automation and validate it against the actual workflow
4. Scale to production
We add reliability, security, monitoring, permissions, integrations and agreed support
First-stage outcome
What you receive
- a mapped business process;
- an automation map;
- a working prototype for a suitable task;
- clarity on what can be automated;
- clarity on where human control must remain;
- production implementation recommendations;
- a scope for the next development stage
We do not promise a complete production product in one session. Production scope depends on data, integrations, security and operating requirements
What happens after the prototype
If the prototype validates the hypothesis, we define the production scope: integrations, security, monitoring, permissions, quality controls and ongoing support
Integrations
Connect AI to the systems your business already uses
CRM and ERP
CRM, ERP/1C and other corporate systems when an appropriate API or approved integration method is available
Websites and messaging
Websites, Telegram and other channels that are part of the actual customer or internal workflow
Work tools
Google Workspace, Notion, spreadsheets, analytics systems and other SaaS platforms through available APIs
Platform examples
HubSpot, Salesforce, Notion, Google Sheets and other systems. These mentions do not imply an official partnership with AI24Solutions
Architecture
Technology and security follow the process requirements
We select the technical stack after understanding the workflow, data, integrations and operating requirements
Technology follows the problem
We use Python, REST APIs, webhooks, AI models, RAG, vector search, databases and low-code tools where they genuinely simplify and accelerate implementation
Python · REST API / JSON · Webhooks · LLM · RAG · Vector Search · PostgreSQL / pgvector · Cloudflare · Make / Zapier where appropriate · custom backend integrations
Business data stays under defined controls
We design access controls, data separation, data minimization and rules for external AI/API providers. Production integrations are built around the actual process and infrastructure requirements
Calculated scenarios
What a measurable workflow looks like
These calculation examples show the manual workload before implementation and are not reported results of a specific client
≈147 hours
80 inquiries per day × 5 minutes of initial handling × 22 working days
≈73 hours
25 documents per day × 8 minutes to extract and move data × 22 working days
88 hours
40 cross-system actions per day × 6 minutes × 22 working days
After launch
We can remain your external AI team
We monitor implemented solutions, develop workflows, connect new data sources and identify the next processes worth automating
Control
Logs, errors, stability and answer quality
Development
New scenarios, actions and integrations
AI capabilities
New models and tools only where they provide practical value
Team
Support for employees using the implemented system
FAQ
Frequently asked questions about AI automation
Can every process be automated?
No. We first assess repeatability, data quality, integration access, risk and where human decisions remain necessary
Can we start with one small task?
Yes. A bounded first scenario is usually the safest way to validate the hypothesis before broader production work
Can you connect our CRM, ERP or internal system?
Yes, when an API or another approved technical integration method is available and the required permissions can be provided
How is pricing determined?
After assessing the process, data, integrations and production requirements. We then define the first implementation scope and prepare a commercial proposal
Start with a process assessment
Show us the manual steps, systems involved and the outcome you need. We will define a realistic first implementation stage