unrealistsSpeech AI: STT, TTS & voice workflows
Voice shows up in products and back-office work alike—support calls, field notes, accessibility, training content, and voice-driven assistants. We help teams design speech AI that fits their constraints: batch transcription vs low-latency streaming, cloud APIs vs self-hosted models, and what happens after audio becomes text (search, summaries, workflows, LLM steps).
Speech-to-text (STT)
Turn recordings and live audio into searchable, actionable text: meeting and call transcription, multilingual intake, quality review queues, and pipelines that feed CRM, ticketing, or internal knowledge bases. We care about word error rate on your accents and domains—not leaderboard demos—and about retention, consent, and who may access transcripts.
Text-to-speech (TTS) & voice UX
Read-aloud for accessibility, localized voice for apps, and controlled TTS for IVR or guided flows where a consistent voice matters. We integrate TTS with product UX and content workflows (when text updates, audio stays in sync) rather than treating speech as a one-off export.
After audio becomes text
STT is rarely the finish line. Summarization, classification, extraction, and RAG over transcript archives connect speech to the rest of your AI stack—the same engineering discipline as document processing, with different ingestion. We design those handoffs explicitly so ops teams are not stuck copying from a transcript tab.
Self-hosted & regulated audio
When audio cannot leave your network, we architect on-prem or VPC-bound stacks—open speech models, queue-based batch jobs, and inference you operate. Trade-offs between GPU cost, latency, and model size are part of the conversation, similar to our custom and fine-tuned model work when domain vocabulary or languages need extra attention.
How we typically start
A bounded pilot on real samples: a few hours of representative audio, agreed metrics, and a clear integration target (API, data lake, or product feature). Successful pilots move to monitoring, fallbacks when confidence is low, and human review where mistakes are costly—often through our PoC process before a longer build.
LLM adoption · Document processing · AI proof of concept · Custom models