This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
RakshIQ
Self-hosted PII detection & redaction for AI pipelines
Rakshiq detects & redacts PII before it reaches your model self-hosted, so data never leaves your infrastructure. What's different: • 100% offline: no phone-home, no data sent to us, ever • Presidio-compatible API a drop-in for existing setups • Built for India: Aadhaar, PAN, GSTIN, IFSC, UPI & more, with checksum validation • Multilingual name/place detection (Hindi + English) • Checksums + libphonenumber, not just regex fewer false positives
Hey Product Hunt 👋
I built Rakshiq after watching teams pipe customer data straight into
LLMs and third-party APIs with no real idea how much PII was going along
for the ride. The usual answer Microsoft Presidio is solid, but it's
a Python library you have to host, tune, and babysit, and its coverage of
Indian identifiers (Aadhaar, PAN, GSTIN, UPI…) is thin.
So Rakshiq started as a simple idea: Presidio-compatible, but self-hosted
and with India built in. It keeps a compatible API so existing setups can
switch with almost no code change, but everything runs inside your own
infrastructure no phone-home, no data sent to us, ever. You can pull the
network cable and it still works.
The hardest part was false positives. Naive regex flags every 12-digit
number as an Aadhaar and every long number as a phone. So detection is
backed by real validation Verhoeff and Luhn checksums, libphonenumber,
PAN holder-type checks which got India recall to ~97% while keeping
precision high. It also handles Hindi and English names/places out of the box.
It ships as a Docker image and the license is a signed key you verify
offline. Would love your feedback especially which other identifiers or
languages you'd want covered next.
Honestly, the multilingual name detection sounds really useful. One thing I’d love to see added is a simple audit log that shows exactly which fields were redacted and when, just for compliance reviews. That kind of transparency would make it much easier to trust the system in production.
Finally someone handling Aadhaar and PAN properly with checksum validation, so many tools miss that and throw false positives everywhere. The Presidio-compatible API was a nice touch, made swapping it into our existing pipeline painless.
About RakshIQ on Product Hunt
“Self-hosted PII detection & redaction for AI pipelines”
RakshIQ was submitted on Product Hunt and earned 0 upvotes and 5 comments, placing #97 on the daily leaderboard. Rakshiq detects & redacts PII before it reaches your model self-hosted, so data never leaves your infrastructure. What's different: • 100% offline: no phone-home, no data sent to us, ever • Presidio-compatible API a drop-in for existing setups • Built for India: Aadhaar, PAN, GSTIN, IFSC, UPI & more, with checksum validation • Multilingual name/place detection (Hindi + English) • Checksums + libphonenumber, not just regex fewer false positives
RakshIQ was featured in Privacy (11.2k followers), Developer Tools (515.9k followers), Artificial Intelligence (473.7k followers) and Vercel Day (26 followers) on Product Hunt. Together, these topics include over 192.7k products, making this a competitive space to launch in.
Who hunted RakshIQ?
RakshIQ was hunted by Vikash kumar Upadhyay. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how RakshIQ stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Vikash kumar Upadhyay