We’ve got your message and it’s already in our inbox. Expect a real reply from a human — usually Jeff, the person who built Philter — within one business day.
What happens next
- You’ll hear back from us directly — no ticket routing, no SDR triage gauntlet. The person who replies is the person who’ll be on the call.
- We’ll suggest a 30-minute call to walk through your stack and the privacy problem you’re trying to solve. Bring your architecture; we’ll bring honest answers about where Philter fits and where it doesn’t.
- You’ll leave with a concrete plan — a deployment path, a policy starting point, and a rough cost picture — whether or not Philter ends up being the right tool. No sales pitch; you can take the plan and run it yourself.
Want to skip ahead?
If you’d rather book a time directly instead of waiting for our reply, here’s our calendar — pick whatever works.
While you wait
The three posts our buyers most often tell us shaped their thinking:
- Why API-based redaction is a security antipattern The architectural argument for keeping sensitive data inside your perimeter — and why "we’ll just call a managed redaction API" is a deeper mistake than it looks.
- The TCO of "free" cloud PII redaction A worked-example pricing comparison: AWS Comprehend, Google DLP, and self-hosted Philter at realistic enterprise volumes. The break-even sits closer than most teams expect.
- From Phileas to Philter Origin story: how an open source library grew into the redaction engine that hospitals, banks, and law firms run in production today.
Other places to look
- Comparisons — how Philter stacks up against AWS Comprehend, Google Cloud DLP, and Microsoft Presidio
- Use cases — deployment patterns for healthcare, finance, legal, and AI workloads
- github.com/philterd — every line of source, Apache 2.0, audit-ready
- All posts — deeper writing on PII redaction, AI privacy, and self-hosted compliance