Sales and support teams with high call volumes
Businesses with heavy order, technical support and complaint traffic. Being able to analyse what is said in these teams produces a usable resource for both training and product decisions.
CUSTOM SOFTWARE · VOICE AI
Voice AI and Call Analytics: Turning Speech into Data
We develop solutions built on Turkish speech recognition and synthesis: call recordings transcribed and made searchable, topic and sentiment analysis, a voice assistant on the order line, and voice-driven data entry for field staff whose hands are full.
The most produced yet least used data in a business is speech. What did the customer complain about on the phone, which product did the dealer ask about, what did the field technician see on the machine? All of this is spoken, some of it is briefly noted afterwards, and most of it is never written down anywhere. Even where call recordings are kept, in practice nobody listens to them; to find a recording you need to know which one to look for, and that knowledge usually does not exist.
Speech recognition makes this data accessible. Once recordings are transcribed they become searchable: which calls mention a product, how often a particular complaint recurs, which topic rose in which period. Add topic classification and trend analysis on top, and customer service stops being a cost centre and becomes a source that feeds product and process decisions.
On the Turkish side this work has its own difficulties, and we see no point in hiding them. Industry terminology, brand and product names, regional speech and the audio quality of the phone line all measurably affect accuracy. Noise is a separate problem in recordings made on the factory floor. The answer is not a magic model but teaching the system a domain-specific vocabulary, getting the audio capture setup right and measuring accuracy on real recordings.
The second use case runs in the opposite direction: places where voice is the input. Someone whose hands are full or gloved can enter a quality record, a maintenance note or a stock count by speaking. Expectations must be set correctly here: voice entry works reliably when it is built as a flow that fills defined fields, not as a system that tries to understand free speech. Validation and approval steps are kept for critical fields.
Who is it for?
Who is Voice AI and Call Analytics a good fit for?
Companies with wide dealer and customer networks
Firms where the same questions come in again and again from different dealers. Measuring the recurring questions shows directly which information is missing from the portal or the documentation.
Field teams working with their hands full
Maintenance, quality control, stocktaking and assembly crews. In these jobs records are usually entered late and incomplete; voice entry creates the record at the moment the work is done.
Noisy production environments
Areas where keyboards and screens are hard to use. In these environments voice is not an easy answer and must be set up properly — but done right, it fills a gap no other method can.
What we build
What we deliver within Voice AI and Call Analytics
Turkish speech recognition and transcription
Call and field recordings are transcribed. Domain-specific terms and product and brand names are taught to the system; accuracy is measured on real recordings. Accuracy promises made without measurement deserve caution, because results vary with audio quality and domain.
A searchable call archive
Transcribed recordings become searchable and can be filtered by customer, product, date and topic. The history of a complaint, or when a promise was made, can be found in the record. This alone is the fastest-felt benefit in most businesses.
Topic classification and trend analysis
Calls are grouped under topic headings; which topic rose in which period is reported. The classification headings are defined around the way your business works — you are not forced into a ready-made list.
Sentiment and risk flagging
Calls carrying signs of dissatisfaction or churn risk are flagged from the tone and content of the conversation. This is a prioritisation tool, not a precise measurement; flagged calls are surfaced for a person to review.
Voice-driven data entry
A flow is built in which defined fields are filled by speaking in the field: which machine, which fault, which operation. A structured flow is preferred over trying to interpret free speech; approval steps are kept for critical fields.
Voice response and routing
A voice response layer can be set up on order and information lines: the customer states the request, the system classifies it, pulls information from your systems or routes it to the right person. The boundary of scope is drawn from the start; a setup that tries to handle every request is what wears users out the most.
Technologies
The technologies we work with
- Turkish speech recognition models
- Domain vocabulary and term training
- Speaker diarisation
- Text classification and summarisation
- Speech synthesis
- Phone system integration
- Noise reduction and audio pre-processing
- On-premise-capable model options
- Accuracy measurement setup
Process
How we move from discovery to go-live
- 01
1. Choosing the use case
Call analytics, voice data entry or voice response — together we determine which one solves a real problem. Trying to build all three at once is a common mistake and ends with none of them settling in.
- 02
2. Accuracy measurement on real recordings
A sample is taken from your own recordings and accuracy is measured. The result is shared in writing. If it falls short, the causes are examined: the audio capture setup, the term vocabulary or ambient noise. We do not proceed without this measurement.
- 03
3. Term vocabulary and classification headings
Product names, industry terms and frequent phrases are taught to the system; topic headings are defined around your structure. This step is the single intervention that lifts accuracy the most.
- 04
4. Integration with your systems
The connection to the phone system, field app or relevant system is made; where the output is written and who sees it is decided. Storage of and access to voice recordings is configured with personal data rules in mind.
- 05
5. Go-live and the improvement loop
We start with a limited scope; misrecognitions are collected and the vocabulary is updated. Accuracy is measured regularly. The system is handed over to your team, with written guidance on how to keep the term vocabulary current.
Frequently asked questions
Common questions about Voice AI and Call Analytics
How accurate is it in Turkish?
Giving a single figure would not be honest, because results vary markedly with audio quality, ambient noise and domain-specific terms. That is why we start by measuring accuracy on a sample of your own recordings and share the result in writing. We recommend treating accuracy promises made without measurement with caution.
Does it work over factory noise?
Ambient noise is the toughest factor. The solution usually lies not in the model but in the audio capture setup: a close-talking microphone, a noise-cancelling headset and choosing a quiet spot to speak. During feasibility we test in the real environment and tell you plainly whether it is possible.
Is processing call recordings a problem under personal data rules?
A voice recording is personal data, and we design with that taken seriously. How long recordings are retained, who can access them, how the duty to inform is met and, where needed, the masking of personal details inside transcripts are all defined up front. The legal assessment is your lawyer's domain; we make your decision workable in the system.
Will our voice data leave the company?
That is an architectural decision. It can be built with speech recognition models running entirely in-house; with that option there is a trade-off between accuracy and hardware cost, and we discuss it openly. If an external service is used, what data goes out and how long it is retained is put in writing from the start.
Won't a voice assistant annoy our customers?
It will if the scope is drawn wrong. That is why we build voice response not as a setup that tries to handle every request, but as a layer that resolves a specific, narrow set of topics quickly; anything outside the scope is passed to a person without delay. What wears users out most is a system insisting on a topic it cannot resolve.
What do we end up with?
A measured accuracy report; a transcribed, searchable archive of recordings; topic classification and trend reports; flagged priority calls; the voice entry flow or voice response layer where built; and documentation for maintaining the term vocabulary. Everything produced, source code and term vocabulary included, belongs to you.
Contact
Let us talk about your Voice AI and Call Analytics project
In a 30-minute discovery call we listen to what you need and tell you honestly whether custom development or an off-the-shelf product is the better answer.
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