Crypto Casino
Torrent Gear
AI analysis of player chat and voice communications
Business value
Speed and quality of support: autosammarization of dialogs, suggestions for answers, prioritization of VIP/hot cases.
RG and safety: early risk detection, soft interventions, routing to a specialist.
Antifraud: identifying coordination, "scripting" patterns, social attacks on support.
Product insights: top reasons for requests, friction points in CCP/payments, UX defects.
Operational efficiency: AHT reduction, above FCR, fewer escalations.
Pipeline: from signal to action
1. Data Capture and Protection
Chat: web/application/instant messengers (Telegram/WebApp, etc.).
Voice: IVR, calls, voice chat in live games.
Immediately: encryption, pseudonymization (user_id instead of PII), DLP filters.
2. ASR (for audio)
He-device/edge, jargon/multi-accents, diarization (who says), timestamps.
Sensitive models for sensitive markets.
3. NLU/NLP
Intents (payment, KYC, bonus, technical combat, complaint).
Tone/emotion (neutral/irritation/stress).
RG markers (impulsivity, despair, "dogon").
Anti-fraud patterns (social engineering, general scripts, "multi-acc").
4. Markup and explainability
Trigger reasons (key phrases, tempo of speech, repetition of routine).
Confidence assessments, escalation rules.
5. Orchestration of actions
Auto-tips for support, ready-made response templates.
RG interventions: "pause/limit/help."
Antifraud: freezing transactions with a case and a clear SLA.
Creating a ticket with sammari and next steps.
6. Logging and auditing
Immutable logs, model/rule version, timestamps, outcome.
Signals and features (text/voice)
Linguistics: "urgent," "all money," "cancel the limit," "now depna," "you must"; slang for KUS/payments.
Paralinguistics (voice): tempo, pause frequency, volume, spikes in peak energy.
Behavioral contexts: a series of calls "in a row," changing the channel (chat→golos), repeating the request to increase the limit.
Fraud markers: the same scripts for different accounts, "transferring the conversation" to alternative channels, requests to bypass procedures.
AI roles in support channel
Operator Assistant: draft response, policy references computed by ETA, "what to say without escalation."
Quality co-pilot: signals an incorrect tone of the agent, prompts de-escalation.
Topic aggregator: clusters of causes, rating of bugs/UX problems, trends in payments/bridges.
RG observer: "soft" chat prompts, quick limit buttons, routing to a specialist.
Anti-fraud filter: if the patterns match, an automatic "yellow flag" and verification.
Privacy and ethics (default)
Minimization: store only text/embeddings without PII; audio raw materials are removed after ASR unless law/permission is required.
It-device/edge-inference: where possible; outward only metrics/labels.
Consent and transparency: pop-up note "dialogue analyzed by AI for quality/RG."
Prohibition of discrimination: without protected features; regular bias audits.
Right to appeal: "why was I refused/paused?" - clear explanation + manual check.
Integration
CRM/Helpdesk: Zendesk/Freshdesk/in-house - tags, statuses, sammari.
KYC/Payments: status of applications/payments, limits, hold/ETA.
Risk/AML: sanclisting, address graph, velocity rules.
RG module: cross-platform limits, self-exclusion, intervention logs.
Telephony/IVR and instant messengers: queue, recording, web hooks of events.
Quality and Success Metrics (KPIs)
Support: FCR, AHT, p95 response time, CSAT/NPS,% escalations.
Classification: accuracy of intents/keys, F1 by RG triggers and fraud.
RG: proportion of "soft" interventions, limits/pauses taken, decline in "marathon" sessions.
Antifraud: TP/FP, average time to lock, amounts prevented.
Product: top reasons for hits, time to fix bugs, impact on churn/ARPU.
Roadmap 2025-2030
2025–2026:- Pilot: text chat + basic ASR; intents, tonality, RG markers; answer assistant.
- Sammari ticket and "next steps"; privacy by design, AI note.
- Paralinguistics, multi-accent ASR, on-device models for sensitive markets.
- Anti-fraud clusters by chat/voice, auto-prioritization of VIP/critical topics.
- Risk escalation forecast by dialogues; adaptive tone of communication; real-time co-pilot quality.
- End-to-end integration with payments/CCM for smart ETA and explanations.
- Multimodal signals (chats + voice + product behavior); public reports on RG algorithms.
- Partial zk-proofs of compliance with data policies for partner/regulator trust.
- Industry standards for AI transparency in support; certification of RG/anti-fraud models; explainability by default.
Risks and how to reduce them
False positives: threshold zones, manual verification of "red" cases, operator feedback.
Prompt injections/social engineering: context guards, stop phrase lists, staff training.
Data drift: regular retraining, canary releases, quality monitoring.
PII leaks: DLP, tokenization, RBAC, encryption, short TTL raw materials.
Negative perception: transparent disclaimers, neutral tone, understandable reasons for decisions.
Pilot checklist (30-60 days)
1. Connect chat and basic ASR to a single pipeline; enable aliasing and DLP.
2. Train/configure intent, key and RG-marker models; define thresholds and explainability.
3. Enable the Answer Assistant and Ticket AutoSIM.
4. Set up integrations with CRM/KYC/Payments/Risk; keep audit trails.
5. Agree on an ethical guide and disclaimers; train the team.
6. Run KPI dashboards (FCR, AHT, CSAT, F1 by RG/fraud) and weekly calibrations.
7. Perform bias/privacy audit and data drift test.
AI analysis of chats and voice communications turns support into a proactive service: it solves issues faster, reduces risk, warns fraud and helps people maintain control. Success comes where technology is paired with ethics: a minimum of data, a maximum of explainability and respect - and the rigorous processes that anchor it.