Enterprise CRM Integration Platform
@ ActivePrime — Python Developer
Enabling seamless data flow between enterprise CRM systems with intelligent conflict resolution and real-time sync
$ cat PROBLEM.md
Enterprises Drowning in Disconnected CRM Data
Large enterprises often use multiple CRM systems across divisions — Salesforce in sales, HubSpot in marketing, custom CRMs in specific business units. Data silos led to inconsistent customer views, duplicate records, and missed opportunities. Manual data exports/imports were error-prone and always out of date.
Key Challenges:
- Same customer record in 3 different CRMs with conflicting data
- Marketing campaigns targeting customers already closed by sales
- IT spending weeks building one-off integration scripts
- Data quality degrading over time with no validation
$ cat SOLUTION.md
Configurable Integration Platform with Smart Conflict Resolution
We built a platform that connects any CRM system through standardized connectors, with intelligent field mapping, conflict resolution, and data quality enforcement.
Technical Approach:
Standardized Connector Framework
Reusable connector architecture for Salesforce, HubSpot, Microsoft Dynamics, and custom CRMs. New connectors built in days, not weeks.
Smart Field Mapping
AI-assisted field mapping suggests connections between different CRM schemas. 'Company' in one system maps to 'Account' in another.
Conflict Resolution Engine
When the same record exists in multiple systems with different values, rules determine the 'source of truth' based on data freshness, system priority, or field-level policies.
Data Quality Layer
Validation rules, deduplication, and enrichment applied during sync to improve data quality across all connected systems.
$ cat tech-stack.json
🚀 Core Technologies
Python
Integration engine and connectors
Why: Excellent API libraries, rapid connector development, great for data transformation
Apache Airflow
Workflow orchestration
Why: Reliable job scheduling, dependency management, and monitoring
PostgreSQL
Integration metadata and mapping storage
Why: Robust JSONB support for flexible schema mapping
🔧 Supporting Technologies
☁️ Infrastructure
$ cat ARCHITECTURE.md
The platform uses a hub-and-spoke model:
| |
System Components:
Connector Framework
Standardized interface for CRM read/write operations
Integration Hub
Core engine handling mapping, transformation, and routing
Conflict Resolver
Rule-based engine determining source of truth
Configuration UI
Non-technical users can configure integrations
$ man implementation-details
Building the Connector Framework
The connector framework standardizes CRM interaction:
Connector Interface:
| |
Connector Implementation (Salesforce):
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Conflict Resolution Engine
When the same record exists in multiple systems with different values:
Resolution Strategies:
- Last Write Wins — Most recently modified value wins
- Source Priority — Designated “master” system wins
- Field-Level Rules — Different rules per field
- Custom Logic — User-defined resolution functions
Implementation:
| |
$ echo $RESULTS
99.9% Sync Reliability Across 50+ CRM Connectors
Additional Outcomes:
- Customers reported unified customer view for first time
- IT teams freed from integration maintenance
- Data quality improved across all connected systems
$ cat LESSONS_LEARNED.md
API Rate Limits Require Creative Solutions
Every CRM has different rate limits. We built adaptive throttling that maximizes throughput while respecting limits, with priority queuing for high-value syncs.
Schema Evolution is Inevitable
CRM schemas change. We built schema versioning and migration tools so existing integrations don't break when customers customize their CRMs.
Non-Technical Configuration is Key
Initial version required developers for configuration. Adding a visual mapping UI increased customer adoption 5x.
$ cat README.md
Related
Experience: Python Developer at ActivePrime
Technologies: Python, REST APIs, PostgreSQL, Celery
Related Case Studies: Salesforce ERP Integration | AI Recruitment Platform
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