# Call Quality AI > A QA workspace for Indonesian contact centres that redacts personal data, scores every recorded call on a rubric calibrated against human QA and links each flag to its moment in the audio. Check every call, and hear the evidence behind each flag. - URL: https://weaveward.com/work/call-quality-assurance-ai - Year: 2026 - Industry: Financial services - Capabilities: AI solutions, Business systems, Data & dashboards - Technologies: Python, faster-whisper, CTranslate2, FastAPI, Pydantic, SQLite, React, TypeScript, Vite, YAML rubrics ## The challenge A contact centre records every call, but its QA team can listen to only a small sample. Required disclosures, misleading promises, greetings, empathy and script adherence go unchecked on most calls, and a transcript that does exist can leak a customer's personal data. Call Quality AI is built for the contact centres of banks, insurers, multifinance companies and telcos in Indonesia, and for the outsourced centres that serve them. It transcribes and redacts each recording, scores it against the team's own rubric and takes a reviewer straight to the moment behind every flag. ## What we built Call Quality AI joins a Python speech pipeline to a reviewer workspace. - **Speakers from the recording.** Stereo WAV and MP3 recordings are split by channel into agent and customer, the way contact-centre recorders keep the two legs, so no diarisation model is needed. Mono files are segmented by an energy-based voice activity detector and marked as lower confidence - **Indonesian transcription.** An adapter around faster-whisper, which runs on CTranslate2, transcribes the speech in Indonesian with segment timestamps - **Redaction before storage.** NIK, card numbers checked with the Luhn algorithm, phone and account numbers and OTP codes spoken in Indonesian are masked in the transcript, and the matching audio spans are replaced with a tone before anything is stored - **A rubric engine in YAML.** Required phrases with Indonesian normalisation and synonyms, forbidden phrases, and order and timing rules, such as a greeting within the first 15 seconds and a disclosure before the closing - **Conversation measures.** Dead air, talk ratio, interruptions found in overlapping segments, and a lexicon-based Indonesian sentiment trajectory across the call - **Evidence-quoting judgement items.** Optional items, such as empathy or whether the issue was resolved, run through an LLM adapter and must quote their evidence with timestamps - **Calibration against human QA.** Automatic scores are compared with human QA scores on a labelled sample of calls, and agreement is reported as Cohen's kappa - **A reviewer workspace.** A call list with scores, a call view with speaker turns and timestamped flags, an audio player that jumps to the evidence, and reviewer overrides kept in an audit trail ## How it works ### Split and transcribe The ingest stage reads each recording, splits stereo files into the agent and customer channels and segments mono files with the energy-based detector. The speech recognition adapter transcribes the speech with faster-whisper and keeps the segment timestamps. ### Redact before storing The redactor finds NIK, card, phone, account and OTP numbers in the transcript, masks them, and replaces the matching time spans in the audio with a tone. It runs before anything is written, so the stored transcript and audio are the redacted versions. ### Score against the rubric The rubric engine loads the team's YAML rubric and checks phrases, order and timing, dead air, talk ratio, interruptions and the sentiment trajectory, plus any LLM-assisted items. Every flag keeps the timestamp of its evidence. ### Review and calibrate A FastAPI service backed by SQLite serves the React and TypeScript workspace, where reviewers play the audio from the evidence and override a score with the change recorded in an audit trail. Calibration compares automatic and human scores and reports their agreement as Cohen's kappa. ## Engineering insights The pipeline is built around evidence: every score points to a moment in the call, and personal data is redacted before anything is kept. - **Speakers from channels, not a model.** Contact-centre recorders usually keep the agent and customer legs on separate channels, so the speaker of every line comes from its channel rather than from a diarisation model. Talk ratio comes from each channel's speech time and interruptions from segments that overlap across the two channels, while mono files are marked as lower confidence - **Redaction in text and in audio.** A number read out on a call exists twice, in the transcript and in the recording, so the redactor masks the text and overwrites the matching audio span with a tone. Both happen before storage, so a detected NIK, card, phone, account or OTP number never reaches the database or the stored audio - **Rubrics that read Indonesian.** Required phrases match after Indonesian normalisation and declared synonyms, so informal and variant spellings still count, while forbidden phrases catch promises an agent must not make. Order and timing rules run on the call's timeline, such as a greeting within the first 15 seconds or a disclosure before the closing - **Machine judgement with quoted evidence.** LLM-assisted items must quote the transcript and give the timestamp of their evidence, so a reviewer can check every machine verdict against the call itself. The adapter keeps the model provider replaceable - **Agreement measured as kappa.** Calibration compares automatic scores with human QA scores on the same labelled calls and reports Cohen's kappa rather than raw percentage agreement, because kappa discounts the agreement two raters would reach by chance. The team sees how closely the engine agrees with its own reviewers before relying on its scores A fake speech recogniser fed by fixture transcripts and a fake LLM adapter run the whole test suite offline, without a GPU, a model download or an API key. A script downloads a small faster-whisper model for real transcription. ## Design system QA studio is a calm, readable interface for reviewers who read transcripts all day, in which each colour keeps one meaning. - **Colour.** Slate-white surfaces and near-black ink keep long transcripts easy on the eyes, teal is kept for controls, agent blue and customer magenta mark who spoke, and green, red and amber mean only pass, flag and attention - **Typography.** Inter throughout, with transcript lines held to about 70 characters and timestamps set in tabular numerals, so columns align and digits do not jump during playback - **Speakers.** Every turn carries a speaker label as well as its colour, so telling the agent from the customer never depends on hue alone - **Evidence.** Each flag links to its timestamp and the audio player jumps to the evidence, so a reviewer hears the moment before accepting or overriding a score ## Where this fits Contact centres in banking, insurance, multifinance and telecoms can apply the same pipeline to their own recordings and rubrics. Explore our [AI solutions](/services/ai-solutions), [business systems](/services/business-systems) and [data dashboards](/services/data-analytics-dashboards), see our [insurance work](/industries/insurance), or [talk to us](/contact) about reviewing every call.