At Reveal, your privacy and the confidentiality of your research data are at the core of everything we build. We know that handling sensitive qualitative data requires trust—and we take that responsibility seriously.
Reveal was designed from the ground up with privacy-first principles:
Uploaded recordings and documents are encrypted and stored in secure cloud storage platforms, such as Amazon S3, which are trusted by enterprises worldwide for their reliability and security.
All user data files, transcripts, and analyses are stored on Reveal-managed servers in a secured cloud environment. The data is stored in the U.S region. If you need the data to be stored in EU region, please contact us.
Access to these systems is highly restricted, monitored, and granted only to essential engineering and support staff under strict confidentiality agreements.
To convert your audio and video into text, we use best-in-class transcription services like:
These services receive your recordings solely to provide transcription functionality. They do not use your data to train their models.
Once transcripts are generated, they are processed by OpenAI models to:
Again, these services process your data only for your specific tasks. No data is retained or reused to train models.
Reveal integrates with secure, industry-leading services that share our commitment to data privacy:
These providers do not use your data to train their models. They act solely as processors on behalf of Reveal, providing only the functionality required for your project.
e.g. medical procedures, conditions, blood type, medications, and injuries.
e.g. contact info, financial details, and demographic information.
Our intelligent redaction tools automatically identify and obscure personally identifiable information (PII) and Protected Health Information (PHI) when generating transcripts, helping your efforts to comply with privacy regulations like GDPR, HIPAA, and CCPA. When redacting Reveal leaves a hint of the kind of information that was redacted to help with your analysis
Sometimes the senstive information is too specific to an industry or too contextual to be recognized. In such cases, you can simply find-and-replace any such information