Press Space to continue
Press Space to continue
micro1 CEO @aliansarinik explains why blacking out PII destroys the relationships AI needs to learn, and how FlowTransformer preserves them in a synthetic digital twin: "Every ingredient within the environment, we aim to make more realistic because it matches the distribution that models need to learn on. That's the distribution they're gonna act in whenever they're deployed into enterprises." "We've been partnering with hundreds of companies, licensing their data, anonymizing it, and then…
aliansarinik@aliansarinik1d ago
Today we’re launching micro1’s PII transformation model, flow-transform 1.0, delivering frontier-level performance across detection, identity synthesis, and transformation of personally identifiable information. On PrivacyBench, our model reaches 96.0% F1, outperforming every detection baseline we tested, including Tonic Textual, Claude Opus 4.8, Sonnet 4.6, Microsoft Presidio, Haiku 4.5 and GLiNER2. Some of the most valuable training data for frontier AI models lives inside fully functioning companies. It captures years of real work across decisions, communications, tools, handoffs, exceptions and the relationships connecting them. The problem is that this data is also full of PII. Traditional redaction makes the data safe, but it also destroys the very workflows and relationships frontier models need to learn from. flow-transform 1.0 solves this by turning enterprise operational data into high-fidelity training data for frontier models by replacing real-world identities without flattening the reality the data captures.
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