enterprise-crm-integration-platform@activeprime:~/case-study
Enterprise Software / iPaaS 10 months 2019

Enterprise CRM Integration Platform

@ ActivePrime — Python Developer

Enabling seamless data flow between enterprise CRM systems with intelligent conflict resolution and real-time sync

99.9% Sync Reliability
50+ CRM Connectors
1B+ Records Synced

$ 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 Manual data exports/imports were error-prone and always out of date.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:

1
Standardized Connector Framework

Reusable connector architecture for Salesforce, HubSpot, Microsoft Dynamics, and custom CRMs. New connectors built in days, not weeks.

2
Smart Field Mapping

AI-assisted field mapping suggests connections between different CRM schemas. 'Company' in one system maps to 'Account' in another.

3
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.

4
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

Redis Elasticsearch React

☁️ Infrastructure

AWS (EC2, RDS, SQS) Docker / ECS Terraform

$ cat ARCHITECTURE.md

The platform uses a hub-and-spoke model:

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CRM A ←→ Connector A ←→ Integration Hub ←→ Connector B ←→ CRM B
                      Field Mapping
                      Conflict Resolution
                      Data Quality
                      Audit Log

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:

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class CRMConnector(ABC):
    
    @abstractmethod
    def authenticate(self, credentials: Dict) -> bool:
        """Establish connection to CRM"""
        pass
    
    @abstractmethod
    def get_schema(self) -> List[ObjectSchema]:
        """Retrieve available objects and fields"""
        pass
    
    @abstractmethod
    def read_records(self, object_type: str, 
                    since: datetime = None) -> Iterator[Record]:
        """Read records with optional delta sync"""
        pass
    
    @abstractmethod
    def write_records(self, object_type: str, 
                     records: List[Record]) -> WriteResult:
        """Create or update records"""
        pass

Connector Implementation (Salesforce):

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class SalesforceConnector(CRMConnector):
    
    def read_records(self, object_type: str, since: datetime = None):
        query = f"SELECT {self.fields[object_type]} FROM {object_type}"
        
        if since:
            query += f" WHERE LastModifiedDate > {since.isoformat()}"
        
        for batch in self.sf.query_all_iter(query):
            for record in batch['records']:
                yield self.normalize_record(record)

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:

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class ConflictResolver:
    
    def resolve(self, records: List[ConflictingRecord], 
               rules: ConflictRules) -> Record:
        resolved = {}
        
        for field in self.get_all_fields(records):
            values = [(r.source, r.data.get(field), r.modified_at) 
                     for r in records if field in r.data]
            
            if len(set(v[1] for v in values)) == 1:
                # No conflict — all values same
                resolved[field] = values[0][1]
            else:
                # Apply resolution rule
                rule = rules.get_rule(field)
                resolved[field] = self.apply_rule(rule, values)
        
        return Record(data=resolved)
    
    def apply_rule(self, rule: str, values: List) -> Any:
        if rule == 'last_write_wins':
            return max(values, key=lambda v: v[2])[1]
        elif rule == 'source_priority':
            priority = self.source_priorities
            return min(values, key=lambda v: priority[v[0]])[1]

$ echo $RESULTS

99.9% Sync Reliability Across 50+ CRM Connectors

99.9% Sync Reliability Enterprise-grade uptime
50+ CRM Connectors Major platforms plus custom CRMs
1B+ Records Synced Across all customer deployments
90% Faster Integration Setup Compared to custom development

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

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