PolyAI: Enterprise Voice AI Delivery and the AI Core Behind It

Role and Scope

As a Forward Deployed AI Engineer at PolyAI, I turn conversational AI designs into production voice experiences. The role sits where AI systems engineering, dialogue behaviour, customer systems, and live operations meet: understand the workflow, design how the assistant retrieves and reasons over real data, build the functions and integrations it calls, validate the complete call path, and remain accountable through launch and hypercare.

This public case study describes my engineering approach without exposing customer-confidential prompts, data, credentials, or internal architecture.

The AI Core

Most of the value of a voice agent is decided in the layer between the language model and the customer’s systems, and that layer is where I spend most of my time:

• Retrieval-augmented context (RAG). Grounding each turn in the customer’s knowledge and live system state so the assistant answers from facts rather than pattern-matching, with retrieval scoped to what the current caller is allowed to know.
• Tool-calling GenAI functions. Python functions the model invokes to look up records, check availability, execute business actions, and return validated, structured results it can speak from safely.
• Dialogue behaviour and guardrails. Turning a workflow into bounded conversational behaviour: what the assistant may assert, when it must confirm, when it escalates, and how it degrades when a dependency is slow or unavailable, so it never fabricates an outcome.
• Evaluation. Structured test suites over realistic conversations, happy and negative paths, run before every promotion so a prompt or function change cannot silently regress a live program.

What I Build

• Production voice agents for healthcare, hospitality, real estate, accessibility, and other service workflows
• Python-heavy backend services and GenAI functions that retrieve context, execute business actions, and return safe conversational responses inside a live-call latency budget
• Retrieval over enterprise knowledge bases: embedding-based semantic search, chunking strategy, and scoping tuned until the assistant answers from the right passage
• Migrations from legacy intent-based IVR and chatbot flows to generative agents, without losing the call handling the business already depends on
• Integration layers for EHR, CRM, scheduling, telephony, video, hospitality platforms, and customer-owned APIs
• Lightweight front-end surfaces for configuration, review, and operational visibility
• Explicit escalation and fallback paths for authentication failures, unavailable dependencies, ambiguous requests, and human handoff
• Release validation across sandbox, pre-production, and production environments

Integration Engineering

Each live conversation can cross several systems with different authentication methods, data shapes, latency profiles, and failure semantics. I keep those concerns behind clear adapters so the AI logic stays readable and customer-specific behaviour remains testable.

Typical responsibilities include mapping API contracts, normalizing responses, managing OAuth flows, handling retries and timeouts, validating permissions, and making sure an integration failure produces a useful caller outcome instead of silence or fabricated information.

The Real-Time Speech Stack

A voice agent is judged on how it sounds and how fast it answers, not on how good its transcript looks. I tune the speech path end to end: recognition accuracy for the accents, names, and vocabulary a programme actually hears, synthesis and custom brand voices so the assistant sounds like the business, and the turn-taking behaviour underneath both, voice-activity detection and barge-in, so a caller can interrupt and be heard.

Latency is treated as a feature with its own budget. Caching, prompt and retrieval shaping, and runtime optimisation all get spent against the same target: the pause before the assistant speaks should feel like a person thinking, not like software loading.

Voice and Telephony Reliability

Voice software has a human-scale latency budget. I configure and validate SIP paths, routing behaviour, transfers, fallback destinations, and environment-specific call flows while watching the full turn: caller audio, recognition, retrieval, dialogue decision, tool execution, and synthesized response. Onboarding a new customer usually means meeting their telephony stack where it is, diagnosing why a call leg fails, and fixing it before anyone hears about it from a caller.

Production readiness includes happy-path testing, negative-path testing, observability, rollback planning, and a clear ownership model for issues found during go-live.

Delivery Lifecycle

1. Translate customer workflows into bounded conversational, retrieval, and integration requirements.
2. Build and test the GenAI functions, retrieval, and API adapters the assistant depends on.
3. Validate behaviour with dialogue, product, and customer stakeholders against evaluation suites.
4. Promote configuration and code through controlled environments.
5. Monitor production, investigate failures, and support hypercare after launch.

Evaluation and Production Insight

Two things keep a live programme honest. Before release, structured suites and LLM-as-judge scoring run realistic conversations, happy and negative paths, over real and synthetic call data, so a prompt, retrieval, or function change cannot quietly regress behaviour that already worked. After release, the call volume itself becomes the feedback loop: analysing live conversations at scale surfaces the questions the knowledge base cannot answer yet and the journeys that end in an unnecessary transfer, which is where the next iteration comes from.

Monitoring covers the same path as the conversation, from telephony and recognition through retrieval, tool calls, and response, so an incident points at a component rather than at a feeling.

Engineering Outcome

The result is not a voice demo. It is an operable AI workflow grounded in real data, with real integrations, known failure behaviour, production monitoring, and a team that can understand what happened when a conversation does not go as planned. It is the same discipline I applied at PeerLogic, now at enterprise scale.