Contact Centre AI and Call Analytics
Analysis of every conversation rather than the two per agent per month someone had time to score, with live assistance while the call is happening and wrap-up written automatically when it ends.
Most contact centres score a fraction of a percent of their conversations, chosen unrepresentatively, weeks after they happened, by people who could be doing something else. Everything that matters, why customers call, which agents need help with what, and which promises were made, sits in the recordings nobody has time to listen to.
Contact centre AI is the application of speech and language technology across every conversation in a contact centre: transcribing and analysing calls and chats, scoring quality automatically against your own scorecard, assisting agents live during the interaction, generating wrap-up notes, checking compliance obligations and reporting the reasons behind contact volume.
What becomes possible when every conversation is analysed
| Capability | What it replaces | The measurable effect |
|---|---|---|
| Automated QA | Sampling a handful of calls per agent per month | Complete coverage, consistent scoring, targeted coaching |
| Agent assist | Searching a knowledge base mid-call | Shorter handle time, fewer escalations, faster ramp for new agents |
| Automatic wrap-up | Two minutes of typing after every call | Recovered agent time and notes that are actually usable |
| Compliance checking | Spot checks and hope | Every regulated disclosure verified, with evidence |
| Contact driver analysis | Disposition codes agents pick under time pressure | The real reasons for volume, ranked and trended |
| Sentiment and escalation signals | Retrospective complaint review | Supervisor alerting while the call is still happening |
The last row is the one operations teams value most and the one that requires the most care, because an alert that fires often and means little will be ignored within a week.
How we build it
Transcription quality first
Everything here rests on the transcript, so recognition is tuned and measured on your own audio before any analysis is built on top of it. That work is described under speech-to-text and text-to-speech, and skipping it produces analytics that are confidently wrong.
Score against your scorecard, calibrated to your QA team
We implement your existing quality criteria rather than a generic one, then calibrate the automated scoring against your own assessors until agreement is high enough to be trusted, and report that agreement rate. An uncalibrated score is worse than no score because people will act on it.
Agent assist that helps rather than distracts
Retrieved answers and next-step suggestions surfaced during the call, with the discipline that the agent's attention is a scarce resource. Fewer, better-timed suggestions beat a panel of constant prompts, and we measure whether assist is used rather than assuming it helps.
Wrap-up written for the next reader
A summary, the outcome, the actions promised and the follow-up required, written to the format your CRM needs and reviewable before saving. This is usually the fastest measurable win in the programme and the one agents themselves ask for first.
Compliance as a check, not a claim
Required disclosures, consent capture and prohibited statements verified on every relevant conversation, with exceptions surfaced for review and the evidence retained. This turns an assurance into a report your compliance function can use.
Contact drivers from the conversation, not the code
Reasons derived from what was actually said rather than from a disposition code chosen under time pressure. The results routinely differ from the official taxonomy, and that difference is usually where the improvement opportunity has been hiding.
Agents must be told what is measured and why
Analytics across every conversation changes the working environment, and deployments that arrive as a surveillance surprise get resisted regardless of their merit. We recommend involving agents and their representatives early, being explicit about what is scored and what is not, and using the output for coaching before it is used for anything else.
Privacy and employment considerations
- Consent and disclosure for recording and analysis, per jurisdiction, for both customers and agents.
- Redaction of payment and health information from transcripts before storage, in stream wherever possible.
- Retention limits applied automatically to audio, transcripts and derived scores.
- A stated policy on how automated scores may and may not be used in performance management, agreed with the relevant representatives.
- The right to challenge an automated assessment, with a human review path.
These are not optional extras. In several jurisdictions they are the difference between a programme that launches and one that is stopped, and they are far cheaper to design in than to retrofit.
How the engagement runs
Transcription is proven first, scoring is calibrated against your own assessors, and agents are involved before launch.
Audio, scorecard and baseline
Transcription accuracy measured on your calls, existing scorecard and disposition data reviewed, current QA coverage and handle time captured.
Analytics and QA scoring
Automated scoring implemented against your criteria and calibrated to your assessors, with the agreement rate reported.
Agent assist and wrap-up
Live retrieval and suggestions, automatic wrap-up in your CRM format, both piloted with a small agent group.
Compliance and driver analysis
Disclosure and prohibited-statement checks, contact driver taxonomy derived from conversations and compared with dispositions.
Rollout and handover
Graduated rollout with dashboards, agent communication completed, and handover of calibration and maintenance guidance.
What you receive
Complete coverage of your conversations, calibrated to your own standards.
Transcription pipeline
Tuned recognition with diarisation and in-stream redaction, measured on your audio.
Automated QA scoring
Your scorecard implemented and calibrated against your assessors, with the agreement rate published.
Agent assist
Live retrieval and next-step suggestions, with usage measured rather than assumed.
Automatic wrap-up
Summary, outcome and actions in your CRM format, reviewable before saving.
Compliance checking
Disclosure and prohibited-statement verification with exception reporting and evidence retention.
Contact driver analytics
Reasons derived from conversations, ranked and trended, compared against disposition codes.
Is this the right engagement?
Worth being direct. Contact Centre AI and Call Analytics is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- You score a small, unrepresentative sample of conversations today.
- Agents spend meaningful time on after-call work.
- Compliance disclosures are verified by spot check rather than systematically.
- Disposition codes are unreliable and nobody trusts the contact reason data.
- You can involve agents and their representatives before launch.
Choose something else if
- Recordings are not retained or cannot be accessed for analysis.
- Transcription accuracy on your audio is too poor to build on, and tuning is out of scope.
- There is no appetite to involve agents, which makes the deployment a conflict rather than a tool.
- The volume is small enough that human QA already achieves full coverage.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What is contact centre AI?
The application of speech and language technology across every conversation: transcription, automated quality scoring, live agent assistance, automatic wrap-up notes, compliance verification and analysis of why customers are contacting you. The defining change is coverage, from a sample to everything.
How accurate is automated QA scoring?
As accurate as its calibration, which is why we measure agreement between the automated score and your own assessors and publish that rate. Objective criteria such as whether a disclosure was made score very reliably; subjective criteria such as empathy need human review in the loop and we say so.
Will agents accept this?
More readily than teams expect, if it is introduced properly and used for coaching before anything else, and particularly if automatic wrap-up gives them time back. Deployments that arrive as a surveillance surprise get resisted regardless of merit, so we recommend involving agents and their representatives early.
Can it help agents during the call rather than after it?
Yes, with retrieved answers and next-step suggestions surfaced live. The design constraint is attention: fewer, better-timed suggestions outperform a constant panel, and we measure whether assist is actually used rather than assuming it helps.
What about payment details and health information in recordings?
Redacted in the stream before storage wherever possible, with retention limits applied automatically to audio, transcripts and derived scores. We also document how automated assessments may be used in performance management, because in several jurisdictions that is a precondition for launching at all.
Often paired with this
Most clients combine two or three engagements from the Conversational AI pillar. These are the ones that most often run immediately before or after.
Speech-to-Text and Text-to-Speech
Recognition and synthesis tuned on your own audio, measured on your vocabulary rather than on clean benchmarks.
Read more →Voice Bot and IVR Modernisation
Menu trees replaced with natural speech, intent-based routing and a fast path to a person.
Read more →Conversational Design and CX Strategy
The design work that decides whether an assistant is used twice: scope, failure recovery, escalation and tone.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.