AI Partner Matching Using Usage Progression and Capability Data
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Solution Overview
Problem
Existing AI-based systems for matching IT company partners with customers rely on limited transactional data, failing to provide a holistic approach that considers all available contextual data throughout the customer engagement lifecycle.
Innovation Solution
A customer-partner matching pipeline that utilizes machine learning and generative models to analyze comprehensive contextual data, including customer software usage characteristics, partner capabilities, and existing relationships, to match partners with customers at different stages of the engagement lifecycle, recommending tailored actions and partners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI-based systems use transactional data for partner recommendations, then the system is simpler to implement, but the recommendation accuracy and comprehensiveness deteriorate
Solution Approach 1:
The system segments the data processing into distinct modules: a first AI model processes customer software usage data from telemetry and cloud data, a second AI model processes partner capability data from documentation and execution metrics, and a match engine combines these results. This segmentation allows complex holistic analysis to be broken down into manageable processing stages while maintaining high recommendation accuracy.
Solution Approach 2:
The patent transitions from two-dimensional transactional data analysis to three-dimensional contextual data analysis by adding the dimension of usage progression modeling. The per industry usage progression model introduces temporal and contextual dimensions to customer behavior patterns, enabling recommendations based on holistic contextual data rather than limited transactional records.
2Adaptability or versatility
If the system analyzes all available contextual data throughout the customer engagement lifecycle, then the comprehensiveness of partner matching improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing customer software usage data, partner capability data, and usage progression models before the actual matching occurs. The per industry usage progression models are pre-computed and stored, allowing the match engine to quickly retrieve and apply relevant models during real-time matching operations without reanalyzing all historical data from scratch.
Solution Approach 2:
The system dynamically selects and applies the most relevant usage progression models based on the specific customer and partner context rather than processing all available data uniformly. The match engine adapts its data retrieval and processing based on the current matching task requirements, optimizing the balance between comprehensive analysis and processing efficiency.
3Measurement precision
If the system uses multiple AI models to process different data types, then the accuracy of customer usage identification and partner capability assessment improves, but the system complexity increases
Solution Approach 1:
The patent divides the AI processing into specialized models: a first AI model dedicated to processing customer software usage data from telemetry and cloud data, and a second AI model dedicated to processing partner capability data from documentation and execution metrics. This segmentation allows each model to be optimized for its specific data type and function, improving overall accuracy while maintaining manageable complexity through clear functional separation.
Data Source
AI summary
A data processing system implements receiving a call requesting a generative model to generate a partner recommendation for a customer of an entity; constructing a prompt, the prompt including partner documentation and historical execution metrics associated for determining partner capability data; providing the documentation and the historical execution metrics to the model and receiving the partner capability data; determining customer software usage data using an AI model based on telemetry data and cloud data; processing the customer software usage data and contextual data associated with the customer using a usage progression model to determine an optimal action/path for the customer; matching the customer with partner(s) based on the customer software usage data, the optimal action/path, and the partner capability data; and providing for display the matched partner(s) to a client device associated with the entity/customer/partner(s).