Data and Identity Services

Key Business Use Cases: Data and Identity Services

Overview of data and identity services

Deep feature integrations with data tools like Office365, Tableau, Power Apps, Snowflake, and more lets orgs use OneReach.ai as an orchestration layer. This enables the automation of communications, and processes as well as integrations between data and workflow tools. Native connectors allow AI systems to utilize transfer learning to adapt and evolve. Native connectors also allow for various systems to become increasingly important, as IDWs can readily access and utilize data from different sources, enhancing their learning and functionality.

Graph DBs

Overview of graph DBs
Our platform leverages graph-based ontologies to organize enterprise data into a semantic network, linking disparate entities—such as users, support tickets, and interaction history—into a unified context. This ontological approach facilitates advanced predictive modeling by providing machine learning algorithms with rich relational metadata. The result is a proactive intelligence layer capable of forecasting user behavior and optimizing automated workflows through pattern recognition across the entire data fabric.

Use a graph database to make connections
Create ontologies linking between different internal operations, Q&A pairs, customers and their preferences/history, open tickets, etc. These are incredibly powerful tools for tracking data and predicting trends or behavior across the customer journey, uncovering meaningful relationships within customer data that previously would have been impossible to discover.

Organizational Digital Twin
Using graph technology, AI systems can begin constructing digital twins of their parent organization. This digital twin encompasses all aspects of a business, including events, data, assets, locations, personnel, customers, and other entities. The digital twin might start at a low fidelity, offering a limited view of the organization. However, as more interactions and processes are conducted through the AI ecosystem, the digital twin becomes higher fidelity. A high fidelity digital twin is an incredibly powerful tool for tracking data and predicting trends or behavior across the customer journey, and graph DB allows you to uncover meaningful relationships within customer data that previously would have been impossible to discover.

Vector-based database
A vector-based database complements graph DB technology by enabling efficient storage and retrieval of complex data structures, which are essential for understanding and processing natural language inputs.

Biosketches

Overview of biosketches
Bridge the gap between unstructured dialogue and legacy systems with GSX’s automated data extraction. Our engine distills meaningful insights from free-form conversations—context typically lost in standard relational models—and archives them into a unified memory layer. This architecture empowers your digital workforce to leverage historical context for advanced decision-making and predictive personalization.

Storing of unstructured data
GSX leverages persistent biosketches to transform unstructured conversational data into actionable customer intelligence. By capturing lifestyle markers—such as parental status inferred from scheduling constraints—the platform enriches the customer profile in real time. This allows your digital workforce to pivot dynamically, such as prioritizing safety-rated features in future engagements to align with the user’s implicit values and household needs.

Channel and context tracking
Conversations can persist on any combination of multiple channels simultaneously, or be spread out over both time and channels. Cross-channel context tracking and security protocols are built into the OneReach.ai platform, so that developers and business owners alike can create rich, journey-specific experiences.

Tables and data logging

Leverage Redis, Dynamo, Graph DB, Elastic Search, SQL Server all from UIs and APIs native to the platform, without having to worry about the underlying infrastructure.

Extensive logging
Since OneReach.ai is based on an event model, we log vast amounts of data from every conversation.

This includes: Custom Tags, Time, Location, Context, Transcript history, Feedback, User information, Total Users, New Users, User Engagement, Sentiment, Session Length (including response times per each piece of the engagement), Completed Conversations, Uncompleted Conversations, Fallback Responses, Missed Intents, Repeat Engagement, Top Messages, Top paths taken by users, Fulfilled Conversations, Escalated Conversations, Unfulfilled Conversations, Time-based pattern recognition, User-based pattern recognition, Topic-based pattern recognition.

Standard metric collection and reporting
Flows built on GSX capture standard metrics for conversational reporting. Our focus is on conversation-experience specific analytics. See the Reporting and Analytics section for more detailed features.

Temporal data organization
Conversational data is inherently time based and our platform prioritizes the organization and analysis of temporal data. This ensures that the chronological context of conversations are preserved and understood. For actionable insights and to make timely decisions, data is mined and monitored in near real-time. This immediacy allows for swift responses to emerging trends, user behavior patterns, and potential system issues.

Compliance and Governance

Restrict access to information based on identity and/or role, preserve audit trails, and restrict access to third-party system credentials.

Encrypted data at rest and in transit
All data whether in transit or at rest is encrypted. During transit we use TLS 1.2 and at rest we use AES-256 bit encryption. Data retention policies can be customized at the PDE level, so customers have full control and ability to customize data storage as fits their use cases.

Least access rights
For privacy compliance we make sure that only certain people have access to certain environments based on their role. Roles-based privilege can be determined differently on a per-customer basis.

Other Considerations

Intelligent Communication Fabric
The promise of being able to “talk to your data” is dubious until you experience a single communication layer (an intelligent fabric) that connects to all of the data inside an organization. A graph-based approach to data management is key to providing seamless and efficient experiences in conversational interactions and Intelligent Communication Fabric. The benefits can remedy one of the major frustrations in conversational AI interactions: the system repeatedly asking users for the same information. Intelligent Communication Fabric ensures that once a piece of data is obtained, it is accessible across the entire conversational platform, eliminating the need for repetitive questioning.

Advanced networking strategies
Mitigate infrastructure complexity with Software-Defined Networking (SDN) capabilities designed for dynamic resource management. Through a combination of proactive load balancing and integrated security frameworks, GSX ensures continuous service delivery and optimal traffic distribution, preventing bottlenecks even during peak demand cycles.