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Telecom & Communications

Contact Centre Speech Analytics API

Quality teams listen to a small sample of calls and generalise from it. The other ninety-eight per cent contain most of what you would want to know.

Contact centre platforms
BPO operators
Banks and insurers
Utilities and telecoms customer operations
Quality and compliance teams

Discuss This Build See What It Returns

What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

A mid-sized contact centre generates tens of thousands of calls a month. Quality assurance typically reviews a handful per agent per month, scored against a form, which means the quality picture is built from a sample too small to be representative and too slow to be actionable. Problems that would be obvious across the full population — a product the whole team explains incorrectly, a process step everyone skips — surface months late or not at all.

Compliance adds a sharper edge. Regulated conversations carry required disclosures, prohibited statements and consent capture obligations, and a missed disclosure on a sales call is a finding rather than a coaching note. Sampling a fraction of calls gives you a defensible process on paper and very little actual assurance.

Analysing every call is technically achievable now, and that is exactly where the legal questions start. Recording a call, processing its content, and in particular deriving anything from voice characteristics are all separately regulated, and the rules differ by jurisdiction, by party and by whether the processing counts as biometric. The build has to start there rather than treat it as a footnote.

Who buys this

Contact centre platforms, BPO operators, Banks and insurers, Utilities and telecoms customer operations, Quality and compliance teams

Category: Telecom & Communications

What the API returns

What comes back

Transcription with speaker separation between agent and customer
Topic and reason-for-contact detection with per-call confidence
Quality scoring against your own scorecard criteria, not a generic template
Compliance flags for required disclosures, prohibited statements and consent capture
Silence, talk-over, hold and escalation timing measures
Sentiment and effort indicators reported at call level with explicit uncertainty
Agent and team aggregates with coaching-relevant detail
Redaction of payment details and sensitive identifiers before storage

Endpoints

Method	Path	Returns
POST	/v1/calls	Submit a recorded call for analysis
WS	/v1/live	Live transcription and flagging during an active call
GET	/v1/calls/{id}/analysis	Full analysis for one call
GET	/v1/calls/{id}/compliance	Compliance flags with timecoded evidence
POST	/v1/scorecards	Define or update quality scoring criteria
GET	/v1/agents/{id}/summary	Aggregated quality and topic profile for an agent

Request and response

illustrative

Request

{
  "call_id": "call_8829140",
  "audio_uri": "s3://your-recordings/call_8829140.wav",
  "channels": { "0": "agent", "1": "customer" },
  "language_hint": "en-GB",
  "scorecard_id": "sc_sales_v4",
  "redact": ["card_number", "national_id", "dob"],
  "consent": { "basis": "recorded_announcement_both_parties",
               "jurisdiction": "GB" }
}

Response

{
  "call_id": "call_8829140",
  "duration_s": 486,
  "language_detected": "en-GB",
  "transcript": { "words": 1142, "mean_confidence": 0.91,
                  "low_confidence_regions_s": [[212.0, 238.0]] },
  "topics": [
    { "topic": "billing_dispute", "confidence": 0.88, "first_mention_s": 41.2 },
    { "topic": "retention_offer",  "confidence": 0.63, "first_mention_s": 302.6 }
  ],
  "quality": {
    "scorecard_id": "sc_sales_v4",
    "score": 78,
    "criteria": [
      { "criterion": "identity_verification", "met": true,  "at_s": 22.1 },
      { "criterion": "needs_established",     "met": true,  "at_s": 96.4 },
      { "criterion": "summary_before_close",  "met": false, "note": "No recap detected before close" }
    ]
  },
  "compliance": [
    { "flag": "required_disclosure_missing",
      "detail": "Cancellation rights statement not detected",
      "severity": "high", "expected_before_s": 420 }
  ],
  "interaction": { "silence_share": 0.09, "talk_over_events": 4,
                   "hold_total_s": 61 },
  "redaction": { "segments_redacted": 2, "types": ["card_number"] },
  "uncertainty_note": "Sentiment not reported: audio quality below threshold for 26s of customer channel"
}

Where the data comes from

Your call recordings	The source audio, read from your recording platform — dual-channel recordings produce substantially better results than mixed mono
Your scorecard definitions	The criteria your QA team already scores against, which is what makes the output usable rather than generic
Regulatory requirements per jurisdiction	Required disclosures, prohibited statements and consent rules, which differ by market and by product
Existing QA scores	Human-scored calls, used both to calibrate the model and to measure where it disagrees with your assessors
CRM and case context	Reason codes and outcomes, so topic detection can be validated against what the call was actually about
Speech models	Self-hosted or licensed; terms differ on commercial use, on data retention by the provider, and on whether audio may leave your environment at all

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Transcription with domain vocabulary injected from your product and process terminology, which is what fixes the errors that matter most
Speaker separation from channel assignment where recordings are dual-channel, falling back to diarisation with explicitly lower confidence on mono
Compliance detection built on phrase-level and semantic matching together, because a required disclosure can be delivered in many valid wordings
Scorecard criteria modelled individually with per-criterion accuracy reported, rather than one opaque quality number
Redaction applied before storage, with payment details removed from both transcript and audio where required
Calibration against human QA scores, reporting agreement rates per criterion so your assessors can see where the model is weak
Deliberate restraint on voice-derived inference: emotion and stress estimation from voice characteristics is unreliable and in several jurisdictions a biometric processing activity, so it is withheld rather than guessed
Monitoring of transcription confidence by queue, language and audio path, since a codec change can degrade everything silently

What it changes for the business

Full-population quality

Every call scored rather than a sample, which changes what quality reporting can actually tell you.

Compliance assurance

Missed disclosures surface as findings with timecoded evidence rather than as an audit discovery.

Targeted coaching

Per-criterion detail shows the specific behaviour to coach instead of a general score.

Operational insight

Topic distribution across every call reveals process and product problems that sampling cannot see.

How it fits into your product

How it embeds

Asynchronous analysis after call completion, with a live WebSocket path where in-call prompting is the requirement
Results written into your existing QA and workforce tooling rather than a separate dashboard
Scorecards defined and versioned through the API so a historical score can be reproduced against the criteria of the time
Deployment inside your own infrastructure, which is the normal and often the only acceptable arrangement for call audio
Redaction configurable per jurisdiction and per queue, applied before anything is stored
Role-based access so agent-level detail reaches coaches and aggregates reach wider management

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 20 weeks

Discovery and feasibility

Weeks 1–3

This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.

Architecture and baseline model

Weeks 4–6

A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.

Production build

Weeks 7–16

Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.

Validation and integration

Weeks 17–20

Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

What are the legal requirements for recording and analysing these calls?

They vary materially and they need settling before the build, not after. Recording consent differs by jurisdiction — some require all parties to consent, others only one, and several require an announcement at the start of every call. Processing call content is personal data processing needing a lawful basis and a retention period. If you operate across markets you will likely need per-jurisdiction configuration. We build to what your legal team specifies, but we will not design a single global flow and pretend it covers everywhere.

Can you analyse voice characteristics for emotion or stress?

We would decline to build it as a decision input. Two reasons. First, deriving emotional or physiological state from voice is not reliable enough to score an agent or a customer on, and the published evidence does not support the confidence vendors attach to it. Second, processing voice characteristics to identify or infer attributes about a person is biometric processing in several jurisdictions — including under the EU and UK regimes — which carries heavier consent requirements, impact assessment obligations and in some contexts outright prohibition. Sentiment from transcript language is a different and more defensible thing, and even that we report with uncertainty.

How accurate is the compliance flagging?

Good for disclosures with consistent wording, weaker where agents paraphrase. We would measure it against your human-reviewed calls and report precision and recall per flag. Our recommendation is to treat flags as a review queue rather than a finding — a missed-disclosure flag should route to a human before it reaches an agent's record.

Will agent-level scores be used for discipline?

That is your decision, and it is worth deciding deliberately. Automated scoring across every call is a significant change in how agents are monitored, with works council or employee consultation requirements in several jurisdictions. We would build transparency into it — agents able to see their own scores and the evidence behind them — because systems that score people opaquely generate disputes and attrition.

Does the audio have to leave our environment?

No, and for most buyers it should not. The normal deployment runs inside your own infrastructure with self-hosted models. Where a licensed cloud speech service is preferred, we check its retention and training terms carefully, because several providers retain audio by default in ways a regulated contact centre cannot accept.

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