floatingpoint

Floatingpoint discovered that enterprise AI models consistently failed at document-centric workflows—the unglamorous but critical tasks like extracting data from invoices, processing contracts, or routing forms. Companies building document AI products faced a brutal choice: spend months collecting and labeling real examples, or deploy models that hallucinated, missed fields, or misinterpreted context. The pain was acute for mid-market software vendors who lacked the resources of tech giants to build proprietary training data at scale. The problem was measurable—production error rates on document tasks ran 5-10x higher than on benchmark datasets. Alternatives existed but were expensive: hiring annotation teams, licensing enterprise datasets, or building custom models from scratch. Early validation came when initial customers immediately integrated Floatingpoint's datasets into their training pipelines and saw error rates drop by 40-60% within weeks. This rapid, quantifiable improvement signaled that the core insight was sound: real-world document work required specialized post-training data that generic datasets simply couldn't provide.

Floatingpoint launched with a narrow MVP focused on a single document task where they'd observed consistent model failures. Rather than building a platform, they shipped curated datasets for that one use case within weeks, deliberately excluding dataset customization, multi-task bundling, and their own training infrastructure. This constraint forced early validation: customers either needed exactly what they built or didn't. The tight scope meant they could hand-craft datasets from real sources and validate them through rapid training cycles, catching quality issues before scaling. Early signals validated the approach immediately. The first cohort of customers integrated the datasets and saw measurable model improvements on their specific workflows. This tight feedback loop revealed which document tasks actually mattered in production versus theoretical problems. By staying narrow and shipping fast, Floatingpoint avoided building features nobody needed while establishing proof that their core insight—that targeted post-training data solved real gaps—actually worked. The constraint became their competitive advantage.