AI & Data
Speech analytics tuned to your phone lines
Call recordings are narrowband, accented, overlapping and full of hold music. We build speech pipelines that transcribe, separate speakers, strip out what must not be stored, and surface sentiment, intent and compliance signals - measured on your own recordings.
What We Build
Six things your audio can tell you
Every engagement starts from a decision the audio should inform, not from a speech engine we'd like to sell.
Audio Transcription & Intelligence
Speech-to-text tuned on your product names, jargon and accents, producing searchable transcripts with speaker labels and timestamps.
Sentiment & Emotion Analytics
Tone, pace, pitch and overtalk read alongside the words, to flag frustration, hesitation or escalation risk while there is still time to act.
Compliance & Risk Monitoring
Script adherence, required disclosures and risky phrases checked across every recording rather than a sampled handful, with alerts at thresholds you set.
Audio Classification & Tagging
Non-speech sound as well: alarms, machinery faults, breaking glass, dead air. Labelled into categories your business defines.
Real-Time Audio Processing
Streaming pipelines on live call, conference and device feeds, for agent assist and alerts while the conversation is still happening.
Multilingual Speech Analytics
Per-language models, including calls that switch language mid-sentence, with accuracy reported language by language.
OUR PROCESS
From raw recordings to production audio intelligence
Six phases, each named for what it hands over. The first one measures a generic model on your audio, so every later decision rests on a real number.
Audio audit and baseline
We audit recording systems, formats, channel setup, languages and storage, and map business goals to audio capabilities.
A generic model runs on a sample of your recordings first. The baseline is a measured number, not a guess.
DELIVERABLES
- Recording infrastructure audit
- Channel and format inventory
- Baseline word error rate on sample audio
- Consent and recording-notice review
- Capability-to-objective map
- Proposed stack and deployment design
How It Works
Inside an enterprise speech pipeline
Clean studio audio is easy. Narrowband call recordings with two people talking at once are not. Every stage below exists because of that gap.
Ingestion & Preprocessing
Format normalisation, noise reduction, voice-activity detection, and removal of hold music and IVR prompts. Stereo recordings keep agent and caller apart; mono recordings need diarisation to do it.
Domain-Tuned Recognition
Speech models adapted to your vocabulary, product codes and accents, with dedicated handling for spoken numbers, names and alphanumerics.
Redaction & Extraction
Card numbers and personal data removed from audio and transcript before storage. Sentiment, intent, entities and compliance signals are extracted from what remains.
Integration & Delivery
Structured output by API, webhook or direct connector into your CRM, BI or QA tooling, deployed in your cloud or on your own servers.
Accuracy, Measured Properly
Domain-Tuned vs. Generic Speech AI: Where the Gap Opens
General speech APIs are trained mostly on clean, wideband audio. Narrowband telephony, overlapping speakers, regional accents and product jargon are where their errors pile up.
We measure word error rate on your own held-out recordings, broken down by channel, accent and noise level, so you can see where any model - ours or a generic one - falls short.
- Tuned on your recordings
- Your vocabulary and product codes
- Adapted to your channel quality
- Accents measured, not assumed
- Trained on general audio
- No domain adaptation
- Errors cluster on telephony and jargon
Set per use case and measured end to end on your own telephony, not on clean sample audio
Audio Stack
Models, Processing and Delivery
Speech models, audio processing, streaming and deployment. The recording setup and the use case decide which pieces go into the build.
ASR & Speech Models
NLU & Sentiment
Audio Processing
Real-Time Streaming
Deployment & MLOps
Cloud & On-Prem
Model-agnostic. A managed speech API is the answer when it clears your baseline. Custom tuning starts where it doesn't.
Where It Breaks
Three things that sink speech analytics
None of them shows up in a vendor demo. All of them show up on real phone lines.
Mono Recordings, Two Voices
When agent and caller share one channel, crosstalk blurs who said what. Without good diarisation, sentiment and compliance scores land on the wrong speaker.
Card Numbers in the Archive
Callers read out payment and personal details. If redaction misses them in the audio or the transcript, the analytics store becomes a liability.
Scores Nobody Trusts
If machine QA scores drift away from what your reviewers would say, supervisors stop using them. Calibration against people is part of the build.
Why Nujtech
How we build speech analytics
An engineering team building a system around your recordings. There is no licence to resell.
01
Baseline Before Build
A generic model runs on your audio first. Custom work starts only where the measured gap justifies it.
02
Redaction in the Pipeline
Sensitive details are stripped from audio and transcripts inside the pipeline itself, before anything is stored or analysed.
03
On-Premise When Required
Patient calls, financial conversations and legal recordings can stay inside your perimeter, including air-gapped environments.
04
Calibrated to Your Reviewers
Machine scores are checked against your human QA team's scores, so supervisors can act on them rather than second-guess them.
Vertical Focus
Where a spoken record carries weight
Contact centres, clinics, trading desks and field operations, where what was said, and how it was said, has consequences.
Clinical and operational systems for healthcare
Clinical documentation, claims and connected patient data, with a clinician approving anything that affects care.
Banking, insurance and risk systems
Fraud, KYC, underwriting and claims models that risk and compliance teams can review, explain and sign off.
Engineering for automotive
Connected-vehicle data, telematics and dealer analytics, built around functional-safety and cybersecurity requirements.
Plant-floor systems for manufacturing
Predictive maintenance, vision inspection and forecasting that connect plant data to the business.
Data and AI for agriculture
Crop, sensor and imagery analytics designed for patchy connectivity and one growing season a year.
Retail operations and customer data
Demand forecasting, personalisation and unified customer data, tuned to the trading calendar.
FAQs
Questions about speech analytics
Those are general speech APIs, and on clean audio they do well. On narrowband calls, heavy accents and product jargon, errors climb. Custom work tunes recognition to your recordings and adds what a raw transcript lacks: diarisation, redaction, sentiment, intent and compliance signals. If a managed API clears your baseline, use it.
Yes. Training, inference and storage can all run on your own GPU servers or in an air-gapped environment, inside your security perimeter.
That depends on the task and on how far a generic model already gets. Discovery measures the gap on a sample of your recordings, and active learning keeps labelling to the clips that will actually move accuracy.
Common formats such as WAV, MP3, FLAC and M4A, telephony codecs, and live streams over WebRTC, SIP or gRPC. Contact-centre recording platforms, conferencing tools and IoT devices connect through their export APIs or streaming interfaces, and preprocessing normalises whatever they produce.
It turns on recording quality, the number of languages and how many capabilities you combine. Discovery ends with a measured baseline and a phased plan, so the plan rests on your audio rather than a template.
Usually, and the rules vary by country and sector. Recording notices, purposes of use and retention periods are reviewed with your compliance team at the start of discovery.
Languages are measured one at a time, and per-language models are used wherever a shared model underperforms. Calls that switch language mid-sentence get dedicated handling and their own test cases.
New agents, products and regulations all shift what the model hears. Monitoring tracks accuracy against labelled validation samples, and retraining runs when the numbers move, using fresh recordings from the period that changed.
Pick one decision.
Measure before you build.
Tell us which decision a model should support and what a wrong answer costs. The first conversation covers your data and how the result will be judged. If an off-the-shelf model already passes, that is the answer you get.
Have a project in mind?
Bring the problem. The first call is about specifics, not slides.