Only if you’re ready to become a software company.
Many organisations exploring AI call monitoring face a strategic decision: build internally or adopt a specialised platform.
Both approaches are technically possible, but they carry very different operational, compliance and cost implications.
A proof of concept can often be built in weeks. A production AI call monitoring platform must survive scale, audits, regulator scrutiny and the messy realities of day to day operations.
Organisations choosing to build internally often discover that operating the platform becomes an ongoing software programme.
That difference is where many internal AI initiatives run into trouble.
You could, in theory, build an AI driven call monitoring platform, but what is the real cost?
Yes, it is technically possible to build an AI call monitoring system using Large Language Models (LLMs).
Early demos often look impressive – especially where they are pre-prepared, pre-prompted and curated to impress.
But building in-house means committing to something much bigger than an experiment.
Organisations choosing to build internally should be prepared to:
- Spend 18 to 24 months building and maintaining software
- Hire and retain a multidisciplinary engineering and data team
- Carry compliance, security and regulatory accountability internally
- Operate in a rapidly evolving technical and regulatory environment
Most organisations don’t realise this when they see their first proof of concept. That’s because Minimum Viable Products (MVPs) lie. Proof of concept systems demonstrate a narrow capability while excluding the operational infrastructure required for production deployment.
They answer one simple question: Can an AI analyse a call?
What they hide is everything required to make that analysis reliable, scalable and defensible in production.
The problem is not the AI. It’s everything around it.
Large Language Models are powerful tools. They aren’t a magic box.
An LLM can read a transcript, classify sentiment, score a conversation and summarise issues. That’s the easy part.
Call monitoring at scale is a systems problem.
Production systems must handle reliable ingestion of large volumes of calls, pre-processing and segmentation of transcripts, model evaluation and validation, workflow orchestration, audit trails and compliance reporting.
As one engineer involved in building these systems put it:
“We didn’t underestimate the model. We underestimated everything around it.”
Industry experience consistently shows that most complexity in AI deployments comes from the surrounding infrastructure rather than the model itself.
The AI call monitoring stack:
To understand the gap between demos and real platforms, it helps to look at the full system.
A production AI call monitoring stack typically looks like this:
Most internal proof of concepts stop around the LLM analysis stage.
The stages that follow, evaluation, workflow management and compliance reporting, are where the majority of operational complexity lives.
The point is that proof-of-concept systems often demonstrate a narrow capability while excluding the operational infrastructure required for production deployment.
The hidden cost layers of running AI systems:
Running AI call monitoring involves more than just model usage.
Production systems incur multiple cost layers, including:
Transcription infrastructure
LLM inference costs
Data storage and retention
Evaluation pipelines
Monitoring and alerting systems
Human quality assurance loops
Security and compliance controls
Costs can scale quickly.
One finance manager involved in early experimentation of LLM build in the UK noted that running an LLM on one day of calls for a single client, without optimisation, produced a bill of around £300.
This illustrates a common pattern: cost often scales before value does.
Accuracy, consistency and trust:
Even strong models can fail in ways that are difficult to detect.
Outputs may sound confident while being incomplete or incorrect. Preventing this requires continuous testing, calibration and monitoring.
In one early experiment, an AI assistant analysing calls was asked whether any agents had used inappropriate language during customer conversations. It confidently responded that none had.
The underlying data showed otherwise.
The system was not malicious or broken. It was attempting to be helpful rather than precise.
Obvious errors like this are correctable. The more subtle risk comes from plausible but incomplete answers that quietly undermine trust over time.
At scale, those errors matter.
Workflow: where insight becomes action.
Many internal builds focus on transcription and scoring.
That’s the starting point.
The real operational value begins when insight turns into action.
Production call monitoring systems must answer practical questions such as:
Which conversations need escalation?
Who should review them?
What action is required
By when?
How is follow up tracked and evidenced?
In regulated environments, evidence matters as much as action.
Supervisory questions often focus on exactly this point: What data and management information is the firm using to monitor outcomes, and what action is being taken as a result?
Without workflows and audit trails, answering that question becomes difficult.
Compliance and data protection:
Exporting call transcripts into third-party AI tools can immediately introduce data-protection and transfer obligations.
Under UK GDPR, organisations must have appropriate processor contracts and safeguards for international data transfers when personal data is shared with external systems.
One managing director of a finance brokerage told Voyc:
“Before Voyc, we uploaded call transcripts into ChatGPT to generate coaching feedback for each call. At the time, I didn’t realise that doing this without a signed DPA with ChatGPT, and the additional clauses required for transferring data to the US, could constitute a GDPR breach. According to ICO guidance, fines can reach up to £8.7 million for missing processor contracts and £17.5 million for illegal international data transfers, which was a surprise to learn. We now use Ask Joyc (Voyc’s internal LLM) to generate the same per-call coaching feedback directly within the platform as the data resides in Ireland under EU GDPR. It also saves time because there is no longer any need to export transcripts and upload them elsewhere.” – Managing Director (client).
The regulatory implications of these workflows often only become clear once experimentation moves into production.
In regulated environments, evidence matters as much as action. Supervisors increasingly ask questions such as:
“What data and management information is the firm using to monitor outcomes, and what action is being taken as a result?” – FCA-style supervisory question.
Workflow systems that assign tasks, track actions and record outcomes create the audit trail needed to answer that question.
Security considerations:
Security introduces another layer of complexity.
Prompt injection, where untrusted inputs manipulate model behaviour, is now widely recognised as a risk in LLM systems
In call monitoring, customer speech itself becomes untrusted input, which means models must be surrounded by guardrails, validation layers and defence-in-depth controls.
This is one reason production AI systems involve far more than a model. They typically require data pipelines, workflow infrastructure, monitoring and governance oversight.
A simple exercise that illustrates the complexity of internal builds and whether a self-build is right for you.
Ask your preferred AI tool the following question:
“If we spend £X per month on AI call monitoring software, can we replicate the same capability internally using Large Language Models? Please estimate time to production, team size, operating costs and key failure points at scale.”
The response is often revealing.
Where specialised platforms fit?
Many organisations exploring AI call monitoring face a strategic decision: build internally or adopt a specialised platform.
Both approaches are technically possible, but they carry very different operational, compliance, and cost implications.
For many organisations, the decision ultimately comes down to focus.
Building internally provides maximum cont.ol, but it also creates an ongoing software programme involving engineering, compliance and operational infrastructure.
Adopting a specialised platform shifts much of that operational burden to a vendor while allowing internal teams to focus on coaching, quality assurance and customer outcomes.
Platforms such as Voyc provide the full monitoring stack from ingestion and analysis to workflow, audit trails and reporting.
The real question?
In the end, the question is rarely whether something can be built.
The real question is this:
Do we want to run a software company, or the business we originally set out to build?
