Speech Recognition & Audio AI Development Services
Turn raw audio into meaningful data.
Speech-to-text, transcription, speaker diarization, voice analytics and multilingual audio processing, engineered for accuracy in real-world, noisy conditions.
Most of your organisation's spoken knowledge - calls, meetings, interviews, support conversations - never becomes usable data. We change that.
We build audio intelligence pipelines using Whisper and best-in-class ASR models, tuned for your accents, domain vocabulary and recording quality. The output isn't just a transcript - it's clean, speaker-attributed, timestamped, analysable text.
From there we layer analytics: sentiment, topics, entities, summaries and compliance flags. Everything runs where your data needs to live, whether that's a managed cloud API or a fully self-hosted deployment for sensitive audio.
What we build
From waveform to insight
End-to-end audio pipelines, not just a transcription endpoint.
Speech-to-text (ASR)
High-accuracy transcription of calls, meetings and media, with punctuation, formatting and domain-specific vocabulary.
Speaker diarization
Automatically separate and label who spoke when, even in overlapping, multi-party conversations.
Voice analytics
Sentiment, intent, topics, keywords and talk-time analysis surfaced from every conversation.
Multilingual processing
Transcribe and translate across dozens of languages, including mixed-language and code-switched speech.
Real-time streaming
Live captions and low-latency transcription for voice agents, meetings and contact-centre use.
Redaction & compliance
Automatic PII/PHI detection and redaction to keep sensitive audio inside your compliance boundary.
How we deliver
A path from idea to production
A pragmatic engagement model that de-risks adoption and gets a working system in front of your users fast.
- 01
Audio audit
We benchmark candidate models on your actual audio - accents, noise, jargon - not clean demo files.
- 02
Pipeline design
We design ingestion, chunking, diarization and post-processing for your throughput and latency targets.
- 03
Build & tune
We integrate the pipeline, add custom vocabulary and analytics, and validate accuracy against ground truth.
- 04
Deploy & monitor
We ship to your cloud or on-prem, with monitoring for accuracy drift and cost per hour of audio.
Tools & stack
Technologies we work with
We stay model- and vendor-flexible, choosing the stack that fits your data, budget, and compliance needs.
- OpenAI Whisper
- WhisperX
- faster-whisper
- AssemblyAI
- Deepgram
- ElevenLabs Scribe
- pyannote
- NVIDIA Parakeet/NeMo
- Amazon Transcribe
- Azure Speech
- Python
- FastAPI
Where it fits
Use cases & industries
Anywhere spoken conversation carries value your systems can't currently see.
Contact centres
Transcribe and analyse every call for QA, coaching and compliance.
Meetings & interviews
Searchable, summarised records with action items and decisions.
Media & podcasts
Captions, subtitles, chapters and multilingual versions at scale.
Healthcare & legal
Diarized, redacted transcripts within strict data-residency rules.
Voice agents
Streaming transcription that feeds conversational AI in real time.
Market research
Analyse hours of qualitative interviews for themes and sentiment.
Common questions
Yes. Whisper and several ASR models can be self-hosted on your GPUs, so audio never leaves your environment - ideal for regulated data.
Accuracy depends on your audio. We benchmark on your real recordings and improve results with custom vocabulary, domain tuning and LLM post-correction.
Yes, for streaming use cases like live captions and voice agents we use models and architectures built for low-latency streaming.
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