Painful, inaccurate or non-private transcriptions — AI-first private, cost-transparent solution

Painful, inaccurate or non-private transcriptions — AI-first private, cost-transparent solution targets a $12.0B = 200M knowledge workers x $60 ARPU/year (global speech-to-text & transcription demand across enterprise & creators) total addressable market with medium saturation and a year-over-year growth rate of 18-25% CAGR driven by AI model improvements and enterprise adoption.

Key trends driving demand: Model accuracy improvements -- LLMs and specialized speech models have reduced WER (word error rate) significantly, making automated transcription usable for downstream workflows.; Verticalization -- Demand is shifting from generic models to domain-adapted models (legal, medical, broadcast) that reduce post-edit time.; Privacy-first deployment -- On-device inference and hybrid-cloud options create demand for vendors that can guarantee data residency and non-retention.; Cost-per-minute economics -- Efficient model inference and competition are driving per-minute pricing down, making high-volume use cases economical..