Implementation of On-Premise LLM and RAG Systems to Automate CRM Operations and Management Control

Business Task

Our client had company with more than 20 managers. The management team (CEO and Commercial Director) faced a manageability crisis: scaling the sales department was expensive, and existing management resources were exhausted.

Key Problems Identified by Decision Makers:

• “Blind Spot” in Control: The Sales Manager spent up to 40% of their time on manual “blamestorming”—discipline inspection, call monitoring, and CRM completion control. With a staff of 20, effective monitoring of everyone’s work became impossible.
• Leaking Funnel: Due to human error, deals stalled without next steps. Managers forgot about “warm” leads, missed SLAs on callbacks, and duplicates created chaos.
• Expensive and Time-Consuming Hiring: Candidate screening relied on intuition, making it difficult to quickly assess whether newcomers possessed the right soft skills and product knowledge. This led to hiring unsuitable candidates and wasted time on training.
• CRM Mess: Instead of serving as an assistant, the CRM became a repository of scattered data, making it impossible to quickly reconstruct context (preparing for a call took 15–30 minutes).

Objective: To create an “All-in-One Sales Manager” system—an AI assistant that not only helps salespeople sell but also takes over routine management tasks (Auto-Control), operating in a secure closed loop (On-Premise).

Solution: Autopilot Architecture

We developed an AI Sales Copilot that functions in two ways: assisting line employees (Copilot) and providing transparency for managers (Manager’s Cockpit)

Functional Modules:

1. Lead-Guardian (Lead Advocate)
   • The system automatically monitors the “health” of deals. If an SLA deadline is missed for a lead or there is no scheduled next step, the AI sends an alert to the manager and prioritizes the lead for the day.
   • Result: The issue of “forgotten” clients is eliminated.
2. Smart Prioritization and Next Best Action
   • Instead of a chaotic list of tasks, the AI generates a daily plan: who to call, who to email. The algorithm analyzes the probability and value of each deal, suggesting the “next best action.”
   • The system generates draft follow-up emails, saving hours of routine work.
3. Noise Control (Auto-Summarization)
   • The algorithm cleans the transaction history of spam and logs, generating a summary of key facts.
   • Before: 20 minutes to read the full history. After: 2 minutes to read the summary.
4. On-Premise RAG (Knowledge Base)
   • A chatbot integrated into the CRM is trained on company regulations and scripts. It serves both an instant prompt and a valuable tool.