A whitepaper on architecting Voice AI systems enterprises can trust: accurate, auditable and safe by design. Why naïve RAG falls short, where production systems fail, and the framework for building beyond it.
The whitepaper, at a glance
Enterprises are deploying Voice AI at scale, but most architectures were not designed for the rigorous demands of regulated, high-stakes environments. RAG is a starting point, not a destination. This paper sets out the architecture and operational principles that move teams beyond it.
- The challenge. Why enterprise Voice AI is failing at scale.
- Why RAG isn't enough. Limitations of naïve retrieval architectures.
- Failure points in Voice AI. Telephony, ASR, retrieval and LLM breakdowns.
- Real-world data insights. What millions of voice interactions reveal.
- Governance & compliance. Explainability, auditability and regulatory pressure.
- Risks of poor architecture. Hallucinations, downtime and data exposure.
- The solution framework. Governed knowledge, hybrid retrieval, multi-agent design.
- The reliable Voice AI stack. From observability to scalable architecture.
Three numbers worth internalising
Organisations with structured AI governance frameworks are 3× more likely to realise measurable business value from GenAI initiatives.
Half of all Voice AI production incidents originate from telephony or audio layers, not the LLM itself.
Hybrid retrieval architectures combining vector, keyword and graph search achieve up to 96% factual correctness in financial Q&A tasks.
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- Full architecture framework
- Data insights from millions of voice interactions
- The reliable Voice AI stack, end to end
