«Pizza Surgut» Reclaimed Up to 6 Hours of Workday: How 8 AI Agents Automated Routine Tasks

At the «Pizza Surgut» pizza chain, routine operations such as document processing, recruitment, and financial control used to consume hours of working time daily. Today, the same processes are completed many times faster: document workflow that once took 6-7 hours is now finished in 30 minutes, and processing hundreds of candidate applications has been reduced from several hours to mere minutes.

In any company, especially one with a distributed structure, routine tasks imperceptibly eat away at the budget and time. Tasks get lost between departments, errors are discovered too late, and managers spend hours on oversight instead of development. This leads to chaos, financial losses, and stress. But this chaos can and should be organized, and today there are effective tools to do so.

«Pizza Surgut»'s Pain: Routine, Chaos, Wasted Time

«Pizza Surgut» is a regional chain of restaurants with a rather complex structure: multiple legal entities, dozens of employees, and a huge volume of daily routine operations. Before the implementation of AI agents, the operational department was literally drowning in manual chaos.

Employees had to manually process hundreds of documents: open each file, extract the necessary information, rename it according to templates. Important tasks and information were lost in group chats among hundreds of messages. Recruiters spent hours sifting through 100-200 applications daily to find relevant candidates. All of this meant that managers, instead of focusing on strategic tasks, were constantly forced to control and clarify.

The Path to AI Agents: Why Traditional Automation Wasn't Enough

A simple script-based solution wouldn't work here. The tasks were too diverse, and the data too unstructured. What was needed was a system that could not just follow rigid rules, but also adapt, understand context, work with natural language, and make decisions within established regulations.

Thus, the company arrived at the idea of a comprehensive ecosystem of AI agents. The goal was ambitious: to create a scalable platform for the full automation of key operational and administrative processes, to free employees from routine and liberate managers' time.

How the AI Agent Ecosystem Was Designed

It was decided to create eight specialized AI agents, each responsible for its own area, but together forming a single, seamless system. Each agent was designed with specific tasks in mind and could operate autonomously, as well as interact with other agents.

The main functional blocks that formed the basis of the agents:

• Document Workflow Automation. The agent was to track file arrivals, standardize them, perform OCR, classify them (invoices, bills, acts, contracts, and other types), extract key details — counterparty, date, amount — and automatically rename files according to a predefined template.
• Resume Processing. A bot capable of analyzing messages in chats, recognizing candidate questionnaires, extracting information, and structuring it in a table.
• Chat Summarization. An agent that daily collected messages from work chats, filtered out spam and irrelevant content, and generated structured summaries according to a pre-approved template.
• Employee Auto-Replies. A bot for answering common questions, utilizing a knowledge base and directing to relevant documents.
• Recruitment Automation. A system for processing applications from job sites, extracting candidate data, filtering, and highlighting relevant candidates.
• Revenue Reconciliation. An agent for automatic reconciliation of data from fiscal data operators and bank receipts, identifying discrepancies, and generating reports.
• First-Line Technical Support. A bot that receives inquiries, searches for relevant answers in a knowledge base, and, if necessary, routes the request.
• Legal Change Monitoring. An agent for weekly monitoring of legislative changes, filtering materials by industry criteria, and generating a legally significant digest.

A crucial aspect was the creation of a scalable architecture, allowing for easy addition of new agents and expansion of existing functionality without the need for "rewriting from scratch."

Implementation: Phased Rollout and Integration

The implementation was phased, which allowed for gradual employee adaptation and minimized risks. It began with the most painful and high-volume points where the benefits of automation would be immediately visible.

The first to be launched was the AI document workflow automation agent, which took over the primary processing of accounting and legal files. Other agents were then successively implemented: a bot for processing resumes from chats, a work chat summarizer, an employee auto-reply bot, a recruitment automation system, a revenue reconciliation agent, a tech support agent, and finally, a legal change monitoring agent.

Key to this was that the agents were integrated into existing familiar work tools, such as Telegram and Google Sheets, which greatly simplified team adaptation. Employees quickly saw how AI agents freed them from routine, and began to actively use the new capabilities.

Results of AI Agent Implementation

The implementation of the AI agent ecosystem allowed «Pizza Surgut» to comprehensively solve the problem of manual chaos. Time spent on routine operations was significantly reduced, manager workload decreased, and the number of errors and "gaps" between departments fell. Processes became more transparent and predictable, and employees were able to focus on more important, strategic tasks.

How to Implement This in Your Business

The «Pizza Surgut» case demonstrates that a comprehensive approach to routine automation using AI agents can yield significant results. If your company faces similar challenges, here's where you can start:

• Analyze your most labor-intensive routine processes. Begin with operations that consume the most time and involve clear, repetitive steps. This could include document workflow, inquiry processing, or initial screening.
• Identify information loss points. Where do tasks get "stuck"? Where do managers spend time rereading chats or answering repetitive questions? These are the areas where AI agents can provide the greatest benefit.
• Start small, but think big. Implement agents in stages, beginning with the least risky but most impactful tasks. At the same time, keep the overall architecture in mind so new agents can be easily integrated into the system.
• Utilize ready-made platforms. Modern low-code platforms allow for rapid creation and deployment of AI agents without extensive development, significantly speeding up the process and reducing costs.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager