Reduce ASR errors by managing glossaries and tiny bias adapters

Reduce ASR errors by managing glossaries and tiny bias adapters targets a $4.8B = 160k businesses × $30K ACV total addressable market with medium saturation and a year-over-year growth rate of 20% YoY (voice AI and speech-to-text market CAGR estimated ~20% by industry reports such as MarketsandMarkets and Gartner).

Key trends driving demand: On-prem and edge inference — privacy and latency requirements are shifting some customers away from cloud-only ASR, creating demand for local tooling.; Adapter and parameter-efficient fine-tuning — new tiny-adapter methods allow model behavior to be adjusted cheaply, enabling per-customer biasing without full fine-tuning.; Standardization of developer tooling — teams expect packaged artifacts, CLI/SDK integrations, and reproducible evaluation pipelines, which creates an opening for a focused product.; Vocabulary-sensitive metrics matter — organizations are increasingly measuring rare-word accuracy and brand-term correctness, which a specialized tool can improve..