We design and build custom speech-to-text systems with real-time transcription, custom-trained models, and secure integrations.
Generic APIs fail on accents, jargon, and noisy environments. We don't use generic APIs. We build yours.
Off-the-shelf transcription tools are trained on clean, neutral speech. Your audio isn't clean or neutral; it's medical terminology in a clinic, legal arguments in a deposition room, or customer calls in a contact center.
We build custom speech-to-text systems trained on your real data, tuned to your environment, and deployed where you need them: cloud, hybrid, or on-prem.
Every project is handled by senior engineers who have worked on real-time audio systems for over 20 years. No handoffs to junior staff, no templates. We own the outcome.
Generic models struggle with accents, domain vocabulary, and background noise. We fine-tune on your audio data, whether that's Whisper, Wav2Vec 2.0, Kaldi, or a purpose-built architecture. Accuracy of 95%+ is achievable in production environments where standard APIs fall apart.
We build STT pipelines that work in two modes: sub-second real-time transcription for live calls, meetings, and streams; and high-throughput batch processing for thousands of hours per day. The same system handles both without separate infrastructure.
Beyond text output, we add speaker diarization (who said what), noise reduction and audio enhancement, sentiment signals, and compliance controls for GDPR, HIPAA, and SOC 2. Your transcription data stays useful, auditable, and secure.
We don't start writing code until we understand your audio. Most projects fail at the data and requirements stage, not the engineering stage.
Here's how we avoid that.
We review your use case, audio samples, and accuracy targets to produce a structured SRS document (data requirements, model options, and latency targets).
We assess your training data: volume, quality, label coverage, domain coverage. If gaps exist, we design a data collection or augmentation strategy. This step determines your accuracy ceiling.
Depending on your data volume and latency requirements, we select the right base model (Whisper, Wav2Vec, Conformer, or custom) and establish a baseline WER (Word Error Rate) before fine-tuning begins.
We train on your domain data, evaluate against held-out test sets, and iterate. You see accuracy numbers at each checkpoint — not after 3 months.
The trained model ships as a clean REST or WebSocket API, containerized for your infrastructure. We handle CI/CD setup, monitoring hooks, and fallback logic.
Go live on your schedule. We support post-launch monitoring, retraining pipelines, and incremental improvements as your audio data grows.
By the time you're in production, you have a model tuned to your environment – not a black-box API you rent by the minute.
We design modular, observable pipelines built to scale from MVP to enterprise volume without a full rebuild.
Core components:
Start with an MVP and scale to enterprise-grade systems with millions of concurrent streams. Reach out for free roadmap and SRS →
Real-time audio intelligence is not a single use case; it's a layer that different products need in different ways.
Custom speech-to-text software development for every stage and situation. Secure, scalable, and built by engineers who have been doing this for 20 years.
![[background image] image of logistics control room (for a trucking company)](https://cdn.prod.website-files.com/64e8910adc5a63966a68acc1/68e7dfd17638aaf511162f7a_f841ed23dc31eb8a94e23195c64f4acb_develop.webp)
Have an idea? We’ll turn it into a fully working app – from design and backend to launch and support.
Got a product that needs more speed, stability, or features? We’ll make it stronger and ready to scale.
![[digital project] image of a showcased project (for a ai robotics and automation)](https://cdn.prod.website-files.com/64e8910adc5a63966a68acc1/68e7e04abb8f1a3770a8625e_fix.webp)
Struggling with unfinished or broken code? We’ll step in, clean it up, and get your project back on track.
Startup 💡
Core STT pipeline, base model fine-tuning, REST API, basic deployment
from
$8,000
from 1 month
Growth 🚀
Real-time streaming, speaker diarization, analytics dashboard, production deployment
from
$20,000
from 2.5 months
Enterprise 🏢
Multi-model ensemble, custom compliance controls, high-volume scaling, on-prem option
from
$32,000
from 4 months
Ready for a realistic timeline and cost breakdown tailored to your ARS & STT needs? We offer free SRS and a code audit for existing projects.
Perfecting complex real-time video & audio software since day one – reliable custom solutions that deliver real value.
Senior developers, QA, UI/UX designers, analytics – all in-house. We think like product owners, not just coders.
Over 600 completed projects, 100% Upwork Success rate, and 400+ honest clients' reviews. Results you can trust.
Straight answers from engineers who build these systems.
It’s building an ASR (Automatic Speech Recognition) system trained on your specific data, vocabulary, and audio conditions, rather than relying on general third-party APIs. The result is a model that understands your accents, jargon, and acoustic environment, deployed on infrastructure you control.
With enough domain-specific data, custom models routinely reach 95-98% word accuracy. Off-the-shelf models often drop to 70-80% on specialized audio; fine-tuned custom models regularly hit 93%+. Accuracy depends on your data quality and volume.
Projects range from ~$8,000 for a single-domain MVP to $32,000+ for a full enterprise system with multi-language support, diarization, compliance, and on-prem deployment. Costs vary by languages, accuracy requirements, deployment, and available training data. We provide a precise estimate after a free discovery call.
MVP systems launch in 4-6 weeks. Full enterprise systems take 4-6 months. Using our Agentic Engineering approach – senior engineers working alongside AI agents – we deliver 4-10× faster than conventional timelines.
More domain-specific audio improves results, but limited data can be used via transfer learning and augmentation. Rough guide: 10-50 hours for meaningful fine-tuning, 100-500 hours for production-grade accuracy. We’ll audit your data and identify gaps.
On-premise or private cloud deployment is standard, including air-gapped setups for HIPAA, GDPR, or government/defense compliance.
We provide ongoing support: model monitoring, retraining pipelines, and incremental feature updates, so you’re never left with just a container and a goodbye.
