PolyAI: Enterprise Voice AI Delivery and Integrations

Role and Scope

As a Forward Deployed AI Engineer at PolyAI, I turn conversational AI designs into production voice experiences. The role sits at the intersection of software engineering, dialogue behavior, customer systems, and live operations: understand the workflow, design the integration boundary, build the required functions, 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.

What I Build

• Production voice agents for healthcare, real estate, accessibility, and other service workflows
• Python GenAI functions that retrieve context, execute business actions, and return safe conversational responses
• Integration layers for EHR, CRM, scheduling, telephony, video, and customer-owned APIs
• 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

The most important work happens beyond the model. 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 dialogue logic stays readable and customer-specific behavior 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.

Voice and Telephony Reliability

Voice software has a human-scale latency budget. I configure and validate SIP paths, routing behavior, transfers, fallback destinations, and environment-specific call flows while watching the full turn: caller audio, recognition, dialogue decision, tool execution, and synthesized response.

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 and integration requirements.
2. Build and test reusable Python functions and API adapters.
3. Validate behavior with dialogue, product, and customer stakeholders.
4. Promote configuration and code through controlled environments.
5. Monitor production, investigate failures, and support hypercare after launch.

Engineering Outcome

The result is not merely a voice demo. It is an operable customer workflow with real integrations, known failure behavior, production monitoring, and a team that can understand what happened when a conversation does not go as planned.