Audit every customer call for quality, conduct, and policy compliance
Techies Universe transcribes call recordings with speaker separation, scores them against your quality template, and checks what was said against your own approved policies — so review teams can move from sampling a handful of calls to covering all of them.
- Contact centre quality assurance and agent coaching reviews
- Regulatory disclosure and mis-selling checks on sales calls
- Complaint handling review and escalation triage
- Collections and recovery conduct monitoring
- Live call translation for multilingual support desks
- Compliance checked against your versioned policy index, not generic rules
- Requirement-level findings with the policy source attached
- Human review checkpoints before any coaching or disciplinary action
- Downloadable audit reports and scorecard exports for evidence
- Tenant and role-aware access for supervisors and compliance teams
Full coverage, not samples
Manual QA typically reviews a small percentage of calls. Automated auditing scores every uploaded call, so systemic issues surface instead of hiding in the unreviewed remainder.
Grounded in your policy
Voice AI connects to Knowledge AI. Publish your scripts, disclosures, and SOPs, and calls are checked against those approved documents with the source cited on each finding.
Evidence supervisors can act on
Each audit returns a summary, sentiment trend, scorecard rows, risk flags, and the transcript segment behind every judgement — ready for a coaching conversation.
Implementation details
Start with one call type and define exactly what a good call sounds like.
- Scorecard template
- Mandatory disclosure list
- Risk flag definitions
- Reviewer acceptance checks
Integration details
Connect recordings and outputs to the systems your operations team already runs.
- Recording upload and batch intake
- Structured JSON output
- Report and CSV export
- Status and audit records
Success metrics
Measure coverage, consistency, and the conduct issues you would otherwise miss.
- Percentage of calls audited
- Disclosure adherence rate
- Repeat-issue rate per agent
- Escalation detection time
Case Study: Collections Quality Assurance
How a lending operation moves from sampling two calls per agent per week to auditing every call against its own conduct policy.
The situation
A lending team runs a collections desk where agents call borrowers about overdue payments. Conduct rules are strict: agents must identify themselves, state the reason for the call, avoid coercive language, and honour a request to stop contact. Two QA reviewers cover thousands of calls a month, so they sample roughly two calls per agent per week. A conduct complaint is usually the first time anyone hears the call in question — and by then the recording is weeks old.
What changes
Recordings are uploaded after each shift. Every call is transcribed with agent and borrower separated, then scored against a collections scorecard covering identification, reason for call, tone, and closing. The team publishes its conduct SOP into Knowledge AI, so calls are also checked against the written requirements — disclosure wording, prohibited language, and the do-not-contact rule — with each finding citing the clause it came from.
What reviewers see
Instead of a queue of audio files, supervisors get a ranked list with high-severity risk flags and negative sentiment at the top. A supervisor opens one, reads the summary, sees the mandatory identification statement was missing, and jumps to the transcript segment where the call opens. The judgement takes minutes, and the downloadable report becomes evidence on the coaching record.
Why it matters
Coverage moves from a sample to the full population, so a pattern — one agent repeatedly skipping the do-not-contact acknowledgement — surfaces in the first week rather than after a regulatory complaint. Reviewer time shifts from finding problems to acting on them, while the decision to coach, escalate, or dismiss stays with the supervisor.
- Can this replace human QA reviewers? No. It prepares transcripts, scores, and exceptions so reviewers spend their time on judgement and coaching rather than listening to find issues.
- Does it handle non-English and mixed-language calls? Yes. Call language can be specified to improve transcript accuracy, and live translation is available for multilingual support desks.
- How does it know our compliance rules? It checks calls against the policy documents you publish into Knowledge AI, with the policy source and index version recorded on each finding.
- Can we export the results? Yes. Each audit can be downloaded as a printable report, and scorecard rows can be exported as CSV.
Build a focused call audit pilot
Start with one call type, one scorecard template, your published conduct policy, and reviewer acceptance criteria.