Deploy a fractional Audio AI team that builds production-grade speech recognition, voice synthesis, and sound analysis systems — without the cost of assembling an in-house audio ML lab.
Audio AI is a niche discipline. Hiring an in-house team means competing for a small pool of ML engineers — and paying for their ramp-up time. We've already solved the hard problems.
Audio AI isn't just about running Whisper on your files. It's about handling real-world noise, domain-specific vocabulary, multiple speakers, latency constraints, and privacy requirements — all at the same time.
We bring production-tested pipelines and the accumulated learnings from dozens of audio deployments, so you don't discover the edge cases the hard way.
We adapt base models to your specific jargon — medical terms, financial tickers, product names — dramatically reducing word error rates.
We design for HIPAA, GDPR, and enterprise data governance — with on-premise deployment options and no third-party data exposure.
We optimize inference stacks for your latency SLA — whether that's real-time streaming or high-throughput batch processing.
Specialized expertise deployed directly into your engineering pipeline.
High-accuracy transcription pipelines for calls, meetings, and media — fine-tuned for your domain vocabulary, accents, and noise conditions.
Natural-sounding voice synthesis for IVR systems, content production, and accessibility — including custom voice personas trained on your brand.
Models that detect sentiment, emotion, speaker identity, and acoustic events from raw audio — turning sound into structured, actionable signals.
We don't just write code and leave. We integrate seamlessly with your goals.
We evaluate your existing audio data, quality, and use case to determine the optimal model architecture and fine-tuning strategy.
We curate, clean, and label audio datasets — including domain-specific vocabulary and speaker profiles.
We fine-tune base models against your data and run evaluation benchmarks against real-world accuracy targets.
We deploy the model as a low-latency API, integrate it with your product, and hand over the full pipeline with documentation.
Every engagement ends with working software, documented systems, and a team that knows how to extend them. You own the intellectual property.
A domain-adapted speech model calibrated to your vocabulary, speakers, and acoustic environment.
Real-time or batch processing pipeline that ingests audio, runs inference, and outputs structured data at production scale.
WER, CER, and domain-specific accuracy benchmarks documented so you know exactly where the model performs and where it needs improvement.
Model cards, integration guides, and runbooks so your engineering team can extend and maintain the system independently.
Real scenarios, real numbers. The specifics change — the pattern is consistent.
A BPO was spending significant budget on manual call QA. We built an ASR + sentiment pipeline that auto-transcribes and scores 100% of calls against compliance checklists.
A podcast network needed subtitles and searchable transcripts across a 5,000-episode back catalogue. We deployed a fine-tuned pipeline that processed the archive in 72 hours.
A telehealth platform needed ambient clinical documentation from patient-physician conversations. We built a HIPAA-compliant ASR system with medical vocabulary tuning.
Real engagements from this practice area — the challenge, the build, and the outcome.
Achieved 32% revenue growth, 28% faster ESG reporting, and 40% client retention in 6 months by solving data fragmentation and compliance challenges for textile sustainability reporting.
A leading US-based materials testing lab improved customer retention by 35% and captured 42% more enterprise leads within six months by deploying a domain-trained AI chatbot.
An India-based public cloud provider piloted an Agentic AI-driven competitive intelligence system for the ME region, delivering 45% faster insights, 35% better targeting, and driving 38% revenue growth.
The questions most teams ask us before they decide to move forward.
Ask us anythingIt depends on the domain and quality of the base model. For most business use cases — contact center transcription, medical dictation, media — a few hundred hours of labeled audio is enough to dramatically improve accuracy. We can also use synthetic augmentation to stretch smaller datasets.
Yes. We work with multilingual base models and can fine-tune for regional accents, Indian English, non-native speakers, and code-switched language (e.g. Hinglish). We have specific experience with South and Southeast Asian language audio.
For streaming use cases we typically achieve sub-500ms word-level latency using chunked audio processing. For near-real-time call transcription the end-to-end lag is usually under 2 seconds. We profile and optimize against your specific latency target during the engagement.
Yes. We operate under NDAs and can work within your infrastructure — processing audio in your cloud environment rather than ours. We never retain or use your data for purposes beyond the engagement.
Book a 30-minute strategy session. We'll map your specific opportunity, identify the highest-leverage starting point, and tell you exactly what an engagement looks like.
