Enterprise Software / iPaaS 2019 10 months

Enterprise CRM Integration Platform

ActivePrime โ€” Python Developer

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

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

๐ŸŽฏ The Challenge

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 Manual data exports/imports were error-prone and always out of date.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 Pain Points

  • 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

๐Ÿ’ก The Solution

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.

๐Ÿ› ๏ธ Technology Stack

๐Ÿš€ Core Technologies

Python

Integration engine and connectors

Excellent API libraries, rapid connector development, great for data transformation

Apache Airflow

Workflow orchestration

Reliable job scheduling, dependency management, and monitoring

PostgreSQL

Integration metadata and mapping storage

reliable JSONB support for flexible schema mapping

๐Ÿ”ง Supporting Technologies

Redis Elasticsearch React

โ˜๏ธ Infrastructure

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

๐Ÿ—๏ธ Architecture

The platform uses a hub-and-spoke model:

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CRM A โ†โ†’ Connector A โ†โ†’ Integration Hub โ†โ†’ Connector B โ†โ†’ CRM B
                           โ†“
                      Field Mapping
                      Conflict Resolution
                      Data Quality
                           โ†“
                      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

โš™๏ธ 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]

๐Ÿ“Š Results & Impact

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

๐Ÿ“š Key Takeaways

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.

๐Ÿ“ Additional Details

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