AI lead identifier: Bitrix24 + Binotel
Objective: Develop an intelligent CRM marketing automation system for identifying anonymous leads in Bitrix24. Requirement: ensure automatic customer recognition through the analysis of incoming calls from Binotel telephony, minimize costs for AI analysis through batch deduplication, and clean the database of "junk" contacts.
My Contribution / Solution: Designed a multi-level architecture on self-hosted n8n, integrating Bitrix24 API with models (GPT-4o). Implemented logic for maintaining data context during complex workflow branches.
1. Intelligent Media Engine (Analysis and Identification):
Multi-Step AI Transcription & Analysis: Implemented a system for extracting audio recordings from Bitrix Activity entities. Used neural networks for transcription and semantic analysis of dialogues to identify customer names, company names, and types of requests.
High-Precision Filtering: Implemented strict filtering of the incoming stream: ignoring outgoing calls (DIRECTION: 1), cutting off conversations shorter than 40 seconds, and spam detection. This allowed focusing AI resources only on targeted incoming leads.
2. Batch Processing and Data Integrity (Optimization):
Batch Deduplication Standard: Developed a mechanism for comparing incoming data with the existing database based on the principle [Input] - [DB] = [New]. This eliminated the reprocessing of archived calls (2024–2026) and reduced costs for neural network API.
Source of Truth Recovery (V16): Resolved the issue of context loss (Activity ID, Phone) during successful API requests to Bitrix24. Created an architecture where the final node Normalize Data refers to the initial state of the iterator (Process Calls3), ensuring 100% field completion in final logging.
3. Reliability and Infrastructure Management:
Optimized the operation of the self-hosted n8n instance for bulk processing of large archives. Implemented a data cleaning strategy, disabled logging of successful runs to save disk space, and implemented automatic database compression.
Archival & Real-time Hybrid: The system is configured for hybrid mode: deep processing of historical archives (depth up to 2 years) and daily monitoring of new contacts "on hot trails."
Result: Created an autonomous backend product for automatic enrichment of CRM data:
Data Enrichment: Automated the identification of over 80% of anonymous incoming calls, transforming "Phone call from..." into named contacts with a request history.
#n8n #Bitrix24 #Binotel #AIAutomation #GPT4 #Backend #CRMIntegration #NoCode #DataEngineering
My Contribution / Solution: Designed a multi-level architecture on self-hosted n8n, integrating Bitrix24 API with models (GPT-4o). Implemented logic for maintaining data context during complex workflow branches.
1. Intelligent Media Engine (Analysis and Identification):
Multi-Step AI Transcription & Analysis: Implemented a system for extracting audio recordings from Bitrix Activity entities. Used neural networks for transcription and semantic analysis of dialogues to identify customer names, company names, and types of requests.
High-Precision Filtering: Implemented strict filtering of the incoming stream: ignoring outgoing calls (DIRECTION: 1), cutting off conversations shorter than 40 seconds, and spam detection. This allowed focusing AI resources only on targeted incoming leads.
2. Batch Processing and Data Integrity (Optimization):
Batch Deduplication Standard: Developed a mechanism for comparing incoming data with the existing database based on the principle [Input] - [DB] = [New]. This eliminated the reprocessing of archived calls (2024–2026) and reduced costs for neural network API.
Source of Truth Recovery (V16): Resolved the issue of context loss (Activity ID, Phone) during successful API requests to Bitrix24. Created an architecture where the final node Normalize Data refers to the initial state of the iterator (Process Calls3), ensuring 100% field completion in final logging.
3. Reliability and Infrastructure Management:
Optimized the operation of the self-hosted n8n instance for bulk processing of large archives. Implemented a data cleaning strategy, disabled logging of successful runs to save disk space, and implemented automatic database compression.
Archival & Real-time Hybrid: The system is configured for hybrid mode: deep processing of historical archives (depth up to 2 years) and daily monitoring of new contacts "on hot trails."
Result: Created an autonomous backend product for automatic enrichment of CRM data:
Data Enrichment: Automated the identification of over 80% of anonymous incoming calls, transforming "Phone call from..." into named contacts with a request history.
#n8n #Bitrix24 #Binotel #AIAutomation #GPT4 #Backend #CRMIntegration #NoCode #DataEngineering
Kyiv 
Available for hire
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