LLM-Syndicated Search for Verified Enterprise Onboarding
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Solution Overview
Problem
Existing enterprise software onboarding processes are time-consuming and inefficient, often requiring manual data entry and lacking automated data checking, leading to inconsistencies, duplicate entries, and difficulty in synchronizing data across platforms, which complicates data analysis and reporting.
Innovation Solution
A system utilizing a large language model (LLM) to generate search queries, obtain verified search results, web-scrape relevant data, and merge it with web presence schemas to create accurate digital profiles of entities, leveraging AI agents for automated data extraction and validation from authoritative sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used for onboarding, then data can be entered into the system, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs self-service by automatically searching for, extracting, and validating entity data from multiple online sources without requiring manual data entry. The automated profile generation system independently completes the onboarding data collection process
Solution Approach 2:
The manual mechanical process of data entry is replaced with an automated electronic system that uses web scraping, search queries, and AI-based validation to extract and verify entity information from online sources, eliminating the need for manual typing and form filling
2Reliability
If manual digital profile generation is performed, then digital profiles can be created, but the process lacks automated data checking capabilities leading to inconsistencies and duplicate entries
Solution Approach 1:
The system implements feedback mechanisms by cross-validating data across multiple sources, checking for consistency and duplicates, and iteratively refining the extracted information. The validation process provides feedback on data quality and triggers corrections when inconsistencies are detected
Solution Approach 2:
The system performs preliminary actions by pre-validating and cross-checking data from multiple sources before finalizing the digital profile. This includes pre-screening for duplicates, pre-verifying data formats, and pre-confirming entity identity before completing the profile generation
3Adaptability or versatility
If data is retrieved from diverse information sources in different formats, then comprehensive entity information can be gathered, but conversion to acceptable format results in inconsistencies that complicate data analysis
Solution Approach 1:
The system achieves universality by designing a multi-functional data processing pipeline that can handle multiple data formats, sources, and entity types through a single integrated workflow. The standardized schema and validation rules enable the system to process diverse inputs consistently
Solution Approach 2:
The system applies parameter changes by transforming various data formats into a standardized schema through automated format detection, conversion, and validation. This includes normalizing data types, standardizing field names, and adjusting data structures to match the target profile format while preserving data integrity
4Adaptability or versatility
If manual onboarding processes are used, then entity information can be collected, but integration with other systems is limited making it difficult to synchronize data across different platforms
Solution Approach 1:
The system applies segmentation by breaking down the integration process into modular components: data extraction modules, validation modules, transformation modules, and synchronization modules. This modular architecture enables flexible integration with multiple external systems while maintaining ease of operation through standardized interfaces
Data Source
AI summary
A method includes generating, by a large language model (LLM), a multitude of search queries from a user input term obtained from a user application. The method further includes, executing, by the LLM, the multitude of search queries to obtain a multitude of verified search results regarding a multitude of candidates. A multitude of web presence schemas corresponding to the multitude of candidates from the multitude of verified search results is generated. The method further includes web-scraping a multitude of websites of a subset of candidates selected from the multitude of candidates to obtain a set of corresponding web-scraping payloads. The method further includes merging the corresponding web-scraping payloads with corresponding web presence schemas of the subset of candidates to obtain a multitude of updated web presence schemas. The method further includes presenting the multitude of updated web presence schemas of the subset of candidates in the user application.