Turn Voice and Chat into Reliable Work Interfaces
Connect ASR, TTS, and bots to your systems so conversations actually complete tasks.
Why Most Voice and Chat Projects Stall
Bots answer FAQs but cannot update CRM, ERP, or ticketing. Work falls back to humans.
ASR misreads names, numbers, and domains. Data quality and user trust suffer.
Each channel runs its own logic. No shared state between web, app, messaging, and voice.
LLMs generate fluent text without grounding or policy checks. Risk teams block rollout.
No end-to-end logging or evaluation. Failures are anecdotal, not measurable.
The Reality
Speech and conversation need system design, not just UX design.
Business Outcomes for Your Organisation
Speech and conversational interfaces are engineered to achieve specific metrics.
Lower Cost-to-Serve
By automating high-volume, low-complexity interactions.
Reduce Handling Time
With better recognition, routing, and agent assist.
Improve Data Quality
By capturing structured entities directly from conversations.
Stabilize Compliance
With controlled scripts, prompts, and grounded responses.
Core Technical Capabilities
Automatic Speech Recognition (ASR)
- Select streaming or batch ASR based on latency and use case.
- Adapt language models to domain vocabulary, product names, and entities.
- Apply diarization, punctuation, and segmentation for multi-party calls.
- Normalize entities (dates, amounts, IDs) for downstream systems.
Higher recognition accuracy, less manual correction, better downstream automation.
Text-to-Speech (TTS)
- Deploy neural TTS per language, region, and brand profile.
- Use SSML-like controls for emphasis, pauses, and pronunciation.
- Manage a pronunciation dictionary for domain terms and legal phrases.
- Cache frequent prompts and templates to control latency and cost.
Clear, compliant automated messages with predictable performance.
Conversational Orchestration
- Train intent and entity models on your real interaction logs.
- Use LLMs for flexible language handling where deterministic NLU is not enough.
- Maintain dialogue state across turns and channels.
- Implement tool / function calling with strict schemas and validation.
Task-oriented conversations that can read and write data, not just chat.
Integration Layer
- Build typed connectors to CRM, ERP, ticketing, order systems, HRIS, and data warehouse.
- Use message queues or event buses for long-running or asynchronous workflows.
- Write back structured interaction data to support BI, QA, and model improvement.
End-to-end workflows that actually complete inside your core systems.
Analytics, Monitoring, and SLAs
- Track ASR WER, entity F1, latency, and drop rates.
- Monitor containment, task completion, escalation, and cost per interaction.
- Trigger alerts on degradation in model performance or flow success rates.
- Expose dashboards for operations, engineering, and leadership.
A conversational layer that can be operated like any critical platform.
Reference Architecture (Conceptual)
A typical deployment sits on this structure. It becomes a reusable platform, not a single bot.
Channel Layer
Web chat, in-app messaging, WhatsApp, Teams/Slack, mobile, and optional telephony.
Gateway
Normalizes events, applies auth, and routes requests to conversational services.
Speech Layer
ASR and TTS services, streaming or batch, with domain tuning.
Conversational Core
NLU, dialogue manager, LLM orchestration, and policy engine.
System Connectors
APIs into CRM, ERP, ticketing, order, and internal tools.
Observability and Governance – Logging, metrics, evaluation harnesses, IAM, and masking.
Example Implementations
Customer and Partner Interface
Order status, simple changes, FAQs, entitlement queries.
Web / app chat + optional voice; NLU + RAG; connectors to order and billing systems.
Reduced ticket volume, shorter resolution time, lower cost per interaction.
Internal Service Desk Assistant
IT, HR, finance, and ops questions; simple requests and status checks.
Assistant in Teams/Slack-type tools; RAG on SOPs and policies; connectors to ITSM/HRIS.
Fewer repetitive tickets, reduced time lost by employees on basic queries.
Agent Assist Layer
Support and sales agents handling complex cases.
Real-time transcription, suggested replies, knowledge retrieval, and auto-summaries pushed to CRM.
Higher throughput per agent, better notes, and more consistent service quality.
Governance and Risk Controls
Speech and conversational systems are designed with controls by default. The objective is simple: automation that a risk officer can sign off on.
Role-based access to transcripts, logs, and analytics.
Masking or tokenisation for sensitive fields in storage and logs.
Versioning for flows, prompts, and models with rollback capability.
Evaluation suites for high-risk topics and regulated interactions.
Full trace from user input to model decisions and backend actions.
Treat Speech and Conversational Interfaces as Platforms You Operate, Not Features You Buy
Customers and employees already rely on voice and chat to get work done. A weak architecture increases manual effort, latency, and exposure. Rudder Analytics engineers speech and conversational systems you can scale, monitor, and defend.
