Technology under the hood
Data Detection and Collection
Our data models scrape hundreds of thousands of data sources, extracting relevant information to build a subject profile.
- Data models analyze client input, such as the subject’s name, date of birth, city of residence, or current employment, to establish a baseline for initial data collection.
- A search across various databases and live sources expand upon the basic profile.
- The system continuously scrapes, gathers, and processes additional data.
Finding the Perfect Match
Our automated matching system leverages AI to process information and match the relevant data.
- Distinct models leverage AI to aptly process data—both structured and unstructured— for any data source type. Models undergo updates and retraining to accommodate new data sources.
- Every matching model is based on specific features that compare different data points to relevant information in the profile. The system applies certain weights and scores according to various parameters to determine the results’ suitability.
- Matched data points are added to the profile as part of a continuous iterative process to enhance the curated profile and optimize performance.
Finding the Perfect Match
Our automated matching system leverages AI to process information and match the relevant data.
- Distinct models leverage AI to aptly process data—both structured and unstructured— for any data source type. Models undergo updates and retraining to accommodate new data sources.
- Every matching model is based on specific features that compare different data points to relevant information in the profile. The system applies certain weights and scores according to various parameters to determine the results’ suitability.
- Matched data points are added to the profile as part of a continuous iterative process to enhance the curated profile and optimize performance.
Raising Relevant Flags
Our analysis engine derives meaning from the data to determine if an event should be flagged.
- Machine learning models merge similar results scraped from different sources, such as adverse news articles discussing the same topic, to prevent duplicates.
- NLP technology and predefined rules detect adverse content and designate a yellow or red flag contingent on the level of risk severity.
5 ways
Clarity is better than traditional due diligence reports
TRADITIONAL
vs.
Leveraging automation and AI increases the scope of coverage to access and extract data from all publicly available data sources
Sorting and analyzing piles of information requires the utmost attention to detail, a challenge inevitably subject to human error
AI decreases inaccuracies, reduces biases, streamlines tasks, reaches higher degrees of precision, and draws the most efficient conclusion
It can take up to a few weeks to deliver a report
Does not offer additional tools or services to enhance due diligence processes
Provides a collaborative platform for streamlined due diligence. Teams can communicate and integrate their deal workflow through the platform
Data is displayed in long paragraphs of text, making it difficult to read, digest, and derive meaning from the results
“We were searching for a solution that could deliver results quickly, without compromising on accuracy. Clarity’s expansive coverage, dependable results, and fast turnaround time far surpass that which we’ve seen from other background check providers.”
Ben Marcus
Co-Founder and Managing Partner
Intelligo’s Responsible AI
We are committed to ensuring that our AI is developed and utilized responsibly.
With artificial intelligence climbing to the top of the world’s most cutting-edge technology, it’s providing invaluable benefits to executives and consumers alike. Due diligence is a prime example of a field where the application of AI can decrease inaccuracies and reach higher degrees of precision.
One of our key principles is to avoid bias. As a result, our machine learning and natural language processing models were developed to process data impartially. The algorithms analyze results according to the objective data, thus preventing culture, gender, or any other types of bias from influencing the matching process.
While AI is transforming industry norms, we acknowledge that it also presents challenges and raises questions. At Intelligo, we’re committed to developing our models responsibly to further our mission of empowering trust in the business world.
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