Avaus Factory Componets
With our pre-built components, you can quickly implement ready-to-use solutions that seamlessly connect with various data sources. This accelerates your data-driven journey, helping you achieve faster profits and improved efficiency.
See our Technical Components Frequently Asked QuestionsWhat is Avaus Factory?
Factory is Avaus’ approach to expand the use of data and automation across channels. Our solution, Avaus Factory, embodies the power of Composable Data Architecture. It is a strategic move for any organization looking to harness the full potential of their customer data.
Why It’s Essential for Your Infrastructure
In today’s data-driven world, businesses of all sizes rely on data to make informed decisions, gain a competitive edge, and deliver superior customer experiences. With the increasing volume, velocity, and variety of data, it’s crucial to adopt modern data architectures that can keep pace with these demands. One such architecture gaining traction is Composable Data Architecture. Here, we will explore the key reasons why you should consider integrating composable data architecture into your infrastructure.
Technical Architecture Overview
The backbone of Avaus Factory is built on a sophisticated cloud-native architecture that leverages the full potential of modern cloud computing. This foundation supports deployment across all major cloud providers, including AWS, Google Cloud Platform, and Microsoft Azure, ensuring maximum flexibility for organizations with varying cloud strategies.
1. Consent Manager
Purpose & Core Functionality
The Consent Manager serves as a centralized system for handling all aspects of customer consent across digital channels. It ensures GDPR compliance while enabling efficient data utilization through real-time consent tracking and management.
Technical Architecture
- Real-time Processing Engine
- Event-driven architecture handling consent updates in <100ms
- Distributed caching layer using Redis for instant consent lookups
- Eventual consistency model with immediate read availability
- Automatic synchronization across geographical region
Key Features
- Centralized Consent Repository
- Unified storage of all consent types and preferences
- Granular consent tracking at individual permission level
- Historical consent trail with full audit capabilities
- Automated consent expiration management
- Integration Capabilities
- REST APIs for real-time consent verification
- Webhook support for consent change notifications
- Native integration with major CMP providers
- Batch processing interfaces for bulk updates
Implementation Benefits
- Reduces manual consent management work by up to 90%
- Enables real-time consent enforcement across channels
- Minimizes legal and reputational risks through automated compliance
- Typically operational within 3-4 weeks
2. Feature Store
Purpose & Core Functionality
The Feature Store serves as a centralized repository for machine learning features, enabling efficient feature reuse and ensuring consistency across different models and applications.
Technical Architecture
Dual Storage System
- Online store for real-time feature serving (Redis/Aerospike)
- Offline store for batch processing (BigQuery/Snowflake)
- Automated synchronization between stores
- Version control system for feature definitions
Key Features
- Feature Management
- Centralized feature registry and metadata management
- Automated feature validation and testing
- Feature versioning and dependency tracking
- Computation scheduling and caching
- Development Tools
- Feature engineering pipelines
- Jupyter notebook integration
- Automated documentation generation
- Testing and validation frameworks
Implementation Benefits
- Reduces feature computation costs by up to 60%
- Eliminates redundant feature calculations
- Improves model development velocity
- Ensures consistent feature definitions across teams
3. ID Resolution Engine
Purpose & Core Functionality
The ID Resolution Engine consolidates multiple customer identifiers into unified profiles while maintaining privacy through sophisticated pseudonymization techniques.
Technical Architecture
- Matching Systems
- Deterministic matching algorithms
- Real-time identity graph updates
- Distributed processing framework
- Privacy-preserving record linkage
Key Features
- Identity Managment
- Cross-channel identity resolution
- Real-time profile merging
- Conflict resolution algorithms
- Identity graph maintenance
- Privacy Controls
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- Advanced pseudonymization service
- Encryption key management
- Access control frameworks
- Audit logging system
Implementation Benefits
- Achieves 99.9% accuracy in identity matching
- Processes millions of identity records per second
- Enables compliant cross-channel tracking
- Operational within 3-4 weeks
4. Interaction History Log
Purpose & Core Functionality
Records and manages all customer interactions across channels, providing a unified view of customer engagement history while enabling real-time data access and analysis.
Technical Architecture
- Storage System
- Time-series optimized database
- Event streaming pipeline
- Real-time indexing engine
- Automated data partitioning
Key Features
- Data Management
- Schema-based event storage
- Custom attribute support
- Automated data lifecycle management
- Real-time event processing
- Access Layer
- REST and GraphQL APIs
- SQL query interface
- Real-time event subscriptions
- Batch export capabilities
Implementation Benefits
- Reduces data access latency to <50ms
- Supports billions of interaction records
- Enables real-time customer journey analysis
- Simplifies compliance reporting
5. Machine Learning Framework
Purpose & Core Functionality
Provides a standardized environment for developing, deploying, and maintaining machine learning models while ensuring scalability and reproducibility.
Technical Architecture
- Development Environment
- Containerized model development
- Automated training pipelines
- Model registry and versioning
- Distributed training support
Key Features
- MLOps Tools
- Model lifecycle management
- Automated A/B testing
- Performance monitoring
- Feature importance tracking
- Integration Capabilities
- Support for major ML frameworks
- API-based model serving
- Real-time prediction endpoints
- Batch prediction pipelines
- Batch export capabilities
Implementation Benefits
- Reduces model development time by 40%
- Ensures consistent model performance
- Enables rapid experimentation
- Simplifies model maintenance
6. Profile Manager
Purpose & Core Functionality
Creates and maintains standardized customer profiles by consolidating data from various sources while ensuring real-time updates and accessibility.
Technical Architecture
- Profile Database
- Distributed NoSQL storage
- Real-time update pipeline
- Profile merging engine
- Change data capture system
Key Features
- Profile Management
- Unified customer view
- Real-time profile updates
- Custom attribute support
- Profile validation rules
- Integration Layer
- REST APIs for profile access
- Webhook notifications
- Batch update interface
- Real-time sync capabilities
Implementation Benefits
- Creates single source of truth for customer data
- Enables real-time profile updates
- Improves data quality by 75%
- Reduces profile management overhead
7. Reporting Component
Purpose & Core Functionality
Automates data integration and reporting processes while providing flexible visualization options and integration with major BI tools.
Technical Architecture
- Data Pipeline
- ETL automation framework
- Real-time data processing
- Custom SQL engine
- Automated quality checks
Key Features
- Reporting Tools
- Pre-built report templates
- Custom dashboard builder
- Automated scheduling
- Export capabilities
- Integration Options
- BI tool connectors
- API-based data access
- Custom report builders
- Real-time data feeds
Implementation Benefits
- Reduces reporting time by 80%
- Ensures data consistency
- Enables self-service analytics
- Simplifies compliance reporting
8. Realtime Prediction Engine
Purpose & Core Functionality
Enables instantaneous AI-driven predictions for marketing, sales, and service operations while maintaining high performance and reliability.
Technical Architecture
- Prediction Service
- In-memory processing
- Load-balanced endpoints
- Model serving infrastructure
- Feature computation pipeline
Key Features
- Real-time Processing
- Sub-100ms response times
- Online learning capabilities
- A/B testing framework
- Performance monitoring
- Integration Options
- REST APIs
- Streaming interfaces
- Batch prediction support
- Custom model deployment
Implementation Benefits
- Enables real-time decisioning
- Improves prediction accuracy
- Supports millions of predictions per second
- Quick implementation (3-4 weeks)
9. [YourCompany] ChatGPT
Purpose & Core Functionality
Provides a customizable conversational AI system that leverages large language models while maintaining security and relevance to your business context.
Technical Architecture
- Language Model Integration
- Multi-model support
- Context management
- Response generation
- Source attribution
Key Features
- Conversation Management
- Context preservation
- Memory management
- Response filtering
- Custom training
- Integration Capabilities
- REST APIs
- WebSocket support
- Custom deployment options
- Analytics integration
Implementation Benefits
- Rapid deployment (3-4 weeks)
- Customized to business context
- Secure handling of sensitive data
- Scalable conversation handling
Frequently Asked Questions
We already have a cloud platform, a CDP and other solutions in place. Why do we need your components?
Our Avaus Factory components help in getting more from your existing platforms. Organizations generally have a range of technical capabilities, but many face hurdles in using these to generate the business results that they are looking for. Our Factory components help in structuring and making use of data in a way that maximizes your capabilities.
Our existing platforms already includes these capabilities, why do we need your components?
Our components help with specific functionalities that enhance your capabilities and bring in a new level of agility and scalability. Our experience is that the inability of different systems to share data and communicate with one-another can be a barrier for capturing their full potential. Our components are designed to bridge silos between systems and data, thus enabling you to use those capabilities to create the results you are looking for.
How would using your components work with our current infrastructure?
Our components are designed to seamlessly integrate and complement existing infrastructures / architectures.
Would we need to make large changes to our infrastructure?
No. Our components are relatively easy and fast to implement and are typically operational within 3-4 weeks.
Are your technical components dependent on Avaus’ continuous engagement?
Once our components are deployed in your infrastructure, we will maintain them. If you decide to not renew your subscription, you would still retain the current solution, but not have access to updates and improvements to the components.