Industry: Voice Agents & Call Centers

Voice agents that don't make things up.

Knowledge graph backed voice agents that deliver sub 800ms grounded responses, deflect 40 to 70% of tier 1 volume without hallucination risk, and give every AI answer a citation back to the customer record or policy that supports it. Built on Twilio, Vapi, LiveKit, Deepgram, ElevenLabs, and open weight LLMs when regulation requires it.

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Why voice AI needs a graph: even more than chat does

Voice Raises the Stakes on Hallucination

A chat user can catch a hallucinated answer and hit undo. A voice user hears the wrong thing spoken confidently, acts on it, and calls back angry. Voice raises the stakes on grounding: and lowers the latency budget for delivering it.

Vector Only RAG Fails on Account Context

Vector only retrieval in a voice loop struggles twice over: it can't answer "what did I order last week" because that's account context, not similar text; and its second best answer sounds just as fluent as its best one, so the customer has no signal when the model is guessing.

Graph Backed Voice Agents Refuse to Fabricate

A knowledge graph backed voice agent retrieves the actual account facts, walks the actual chain of events, and refuses to answer what the graph does not know. That's the difference between a bot that customers trust and one they escalate away from on the first turn.

Six proven voice use cases

Real time agent copilot

Live transcription plus intent detection plus a graph query over the caller's account, orders, and history: surfacing exactly the answer the agent needs, with citation, in under a second.

•Average handle time reduced by removing swivel chair lookups
•Cite checked answers: the copilot points at the source system field it used
•Compliance script adherence tracked automatically from the transcript

Fully autonomous voice agents (grounded)

For high volume, narrow workflows (order status, appointment scheduling, tier 1 troubleshooting), a graph backed voice agent handles the call end to end: with the guardrails and provenance a pure LLM agent cannot provide.

•Deflection rates on tier 1 workflows without the hallucination risk of vector only RAG
•Escalation with full context: the human agent sees exactly what the bot said and why
•Latency optimized retrieval so responses feel natural in conversation

Post call analytics and QA at scale

Every call transcript becomes graph entities: caller intent, resolutions, sentiment, mentioned products, compliance events. Trend detection stops being a sample based sport.

•100% of calls scored for compliance and QA, not just the sampled 2%
•Emerging issue detection days earlier than manual review cycles
•Product and content team feedback loops from real customer language

IVR replacement with grounded understanding

Legacy DTMF trees replaced with a graph backed conversational front door. The IVR understands intent, disambiguates when needed, and routes with full account context: never asking for the account number twice.

•Higher containment rates than menu based IVR
•Zero repeat questioning: every downstream skill inherits the resolved context
•Voice first analytics on intent distribution, not menu hit counts

Caller identity and entity resolution

The same caller reaches you from different phone numbers, aliases, and account states. Resolve to a canonical customer at pickup, so every interaction (voice, chat, or web) shares the same context.

•Faster identity confirmation, reducing friction and fraud risk
•Cross channel continuity: the agent knows the caller opened a web chat this morning
•Fraud ring detection across accounts and phone numbers using graph community detection

Knowledge base grounding for CX teams

Product manuals, policy documents, and internal know how ingested into the graph. Every agent (human or AI) answers from the same current, versioned source of truth: with citations back to the policy that supports the answer.

•Consistent answers across agents and shifts
•Zero legacy policy drift: old versions are archived, not silently overwritten
•Content owners see which articles are surfaced most, where they fail, and where gaps exist

Frequently asked questions

Which voice and telephony stacks do you integrate with?+

Telephony: Twilio, Amazon Connect, Genesys, Five9, LiveKit. Voice agent orchestration: Vapi, Retell, LiveKit Agents, Bland, and custom pipelines. ASR: Deepgram, AssemblyAI, Whisper, Azure Speech. TTS: ElevenLabs, Cartesia, PlayHT, Azure. LLM: OpenAI, Anthropic, Google, or open weight (Llama, Mistral) hosted in your VPC when needed.

How do you handle latency? Real time voice needs sub second responses.+

Latency budget is the constraint everything designs around. We use streaming ASR, streaming TTS, speculative retrieval that starts before the user finishes talking, precomputed graph views for hot paths, and small local models for intent classification when the LLM would be too slow. Typical end to end response latency lands under 800ms.

How is this better than a plain vanilla RAG voice agent?+

Plain RAG voice agents fail on questions that need real time account context ('what did I order last week?') and on multi hop questions ('why hasn't my refund arrived?'). A graph gives the agent that account context in one query and lets it walk the actual chain of events: with the source system fields visible for compliance.

What about PII and PCI?+

Voice channels are heavily regulated. We design for HIPAA, PCI DSS, and TCPA from day one: BAA covered ASR/TTS options, PCI safe pauses (DTMF for card capture), token based PII redaction in transcripts before ingestion, encryption end to end, and full call audit logging.

What's a realistic first engagement?+

Two common starts: (1) a 6 to 8 week agent copilot pilot over one queue with an evaluation harness for accuracy and AHT, or (2) a 10 to 12 week fully autonomous agent build for one narrow, high volume workflow with a phased rollout to production traffic.

Service

GraphRAG implementation →

How grounded retrieval is built for latency critical workloads.

Deep dive

How to reduce LLM hallucinations →

Techniques that apply doubly to voice: where users can't skim to check.

Related capabilities

Services we bring to voice agents teams.

Generative AI Development

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Enterprise Knowledge Graph Consulting

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Custom AI Software Engineering

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Not sure which lane is yours?

Pick the voice agent lane that fits your team.

A 30 minute call and we'll tell you whether an agent copilot, fully autonomous agent, or IVR replacement is the right first bet: and what a working pilot would cost and take.