Window Production: How an AI Agent Saved 4.8 Million Rubles and Boosted Conversion by 24%

Previously, eight managers at a window manufacturing company spent up to an hour on each dealer application, manually sorting through PDFs, photos, and Excel tables. This led to errors and long waits for clients. After implementing an AI agent that processes all incoming formats, the application time was reduced to three minutes, and sales conversion increased by 24%.

Routine data processing is not just wasted time, but also direct losses: errors due to human factors, delays that deter clients, and an inflated staff engaged in mechanical work instead of business development. Modern AI agents are capable of taking on this burden, freeing up resources and significantly improving efficiency.

The Reality of the Problem: Costly Routine

At the window manufacturing company, the flow of dealer applications was constant but chaotic. Documents arrived in various formats: from handwritten sketches sent via photos to structured PDF files and Excel tables. Each such application required manual deciphering, conversion to a unified format, cost calculation, and commercial proposal generation from the manager.

Processing one application took 30 to 60 minutes. Moreover, human error inevitably led to mistakes, requiring additional checks and corrections. Eight managers were constantly bogged down by this mechanical work, and clients waited hours for a response, negatively impacting conversion and loyalty.

The Path to an AI Agent: Why Manual Work Was No Longer Sufficient

The company realized that manual application processing had become a bottleneck, hindering growth. Increasing the number of managers only scaled the problem, rather than solving it: payroll expenses grew, but processing speed and quality remained unchanged. Existing CRM systems and basic automation tools could not handle the variety of input formats and required constant manual intervention.

It became clear that a fundamentally new approach was needed, one capable of not just collecting data, but independently analyzing it, extracting necessary information from any source, and making decisions based on predefined rules. This led the company to the idea of implementing an AI agent.

How the AI Agent Was Designed

The AI agent was designed as an intelligent system capable of working with unstructured data. Its primary task was to fully automate the process of incoming dealer application processing. The agent consisted of several key modules:

• Format Recognition Module. Ability to analyze and extract data from any type of document: photos, PDFs, Excel, text messages.
• Parameter Extraction Module. Identification of key order parameters, such as window dimensions, profile type, number of chambers, hardware, etc., regardless of their representation in the source document.
• Cost Calculation Module. Integration with the internal pricing database for automatic cost calculation based on extracted parameters.
• Commercial Proposal Generation Module. Automatic generation of a ready-to-use commercial proposal in the company's standard format.

Key requirements included high accuracy and processing speed, as well as the ability to self-learn to adapt to new application formats.

Implementation and Adaptation

The implementation of the AI agent began with a pilot project where the system worked in parallel with managers, processing a portion of incoming applications. During this stage, the AI agent was trained on real data and fine-tuned. An important aspect was the involvement of managers: they were shown how the AI agent took over the most routine and disliked part of the work, freeing up their time for more complex tasks, such as working with key clients or resolving non-standard issues.

Gradually, as the agent's accuracy and reliability grew, more and more applications were shifted to its processing. Managers were trained on how to interact with the new system, mastering control and validation functions rather than manual processing. This phased approach helped minimize resistance and ensure a smooth transition.

Implementation Results

The implementation of the AI agent brought significant improvements to the company. The time to process one application was reduced by 10-20 times, allowing for prompt responses to dealer inquiries. This directly impacted sales conversion, which increased by 24%, as clients no longer waited for hours.

Due to increased efficiency, the number of application processing managers was reduced from eight to four people, with the remaining employees reallocated to more strategic tasks. The annual payroll savings amounted to an impressive 4.8 million rubles.

How to Replicate This in Your Business

If your company has processes where employees spend a lot of time on routine processing of unstructured data, an AI agent can be a powerful tool for optimization. Here's how to start:

• Identify bottlenecks. Find processes where manual data processing slows down work, leads to errors, or requires a large staff.
• Define data formats. Analyze the formats in which information needs to be processed (scans, photos, voice messages, Excel).
• Start with a pilot. Choose one, most critical or high-volume process for a test implementation of an AI agent. This will allow you to fine-tune the system and demonstrate its benefits to the team.
• Engage the team. Explain to employees how the AI agent will help them get rid of routine, not replace them. Train them on how to interact with the new system.

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