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micro1 CEO @aliansarinik on where synthetic data belongs in an expert data pipeline: everywhere the expert’s craft isn’t. "It's a bit counterintuitive because we're talking about realism, but then we also wanna use data generation models." "A lawyer's expertise that gets distilled into model training is the golden response of a legal memo. But the training signal comes from rubrics. The lawyer would also create 30 to 40 rubric items that define what a perfect legal memo looks like." "The…
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.