About Us
Our Mission
To provide affordable and quality software that can analyze the legitimacy of online reviews
We want to act as a beacon for businesses with real reviews by providing reassurances that they are a high-quality business worthy of their customer's hard-earned money.
Our team
We work hard to bring a new element of trust back to the online review marketplace.
Curtis Boyd
Founder
Curtis Boyd is the founder of The Transparency Company. His expertise in Online Reputation Management gave The Transparency Company the direction for the technology.
Roman Grabar
Sr. DevOps Engineer
Roman is the right hand man behind the scenes, making the API's work and driving functionality to a very large data pipeline. He's more than a full-stack developer, he's family. 
Rashid Ali
Front-End Developer
Rashid is one of the hardest working individuals, who refuses to say, " I can't do that." His commitment to his projects has been invaluable to our team.
Our Process
What is our workflow?
1
Data collection
Hybrid Data Mining/API’s
We collect all the information we can from: 
1. Review Content 
2. Reviewer Profiles
 3. Business Profiles
2
Data Analysis
Our Process
The aggregated data is analyzed by our server and evaluated based on pre-trained AI Models.
3
Data Synthesis
We build profiles
Based on the unique scores of each reviewer, we profile them based on known fraudulent profiles.
Our metrics
Profile Metrics
Profile data includes the behavioural data of a reviewer. The businesses they have reviewed, the types of businesses, the types of reviews they leave.
Distance Matrix Analysis
We look at each reviewer individually and look at all the businesses they have reviewed, then calculate the distances between all of those businesses.This helps us to understand a group of reviewers of a particular business, particularly about where they live and write reviews.
Reviewer Pod Analysis
The review pod analysis measures if there are large groups of reviewers who have reviewed the same businesses. It looks a lot like a private reviewer circle. This is often a sign of someone being paid or incentivized to publish reviews.
Reviewer Image Analysis
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Content Metrics
Content Metrics are created from the content of the review itself. Not all reviews are created equal, and we look at them closely.
Natural Language Processing
As human beings we all have our own unique authorship style, the way we write sentences, ect. We use machine learning to identify unique authorship styles. This is helpful when looking for reviews written by the same author.
Sentiment Analysis
Fake reviews are generally higher in sentiment than real reviews, which is why we look at sentiment to make a prediction whether a review is real or fake. We also look at use of exclamation marks and other indications that show over the top positive sentiment.
Keyword Stuffing
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See our technology in action
Consumers are 15x more likely to have a negative experience from a company with fake positive reviews.
Try it now