June 2026
Best AI Voice Agent for IT Support: 2026 Enterprise Guide
Modern enterprises are hitting a wall with traditional IT service desks. According to benchmark data from Gartner , the average enterprise experiences a 25% year-over-year increase in internal support ticket volumes. This surge is driven by increasingly complex cloud infrastructures, remote work distributions, and SaaS sprawl. At the same time, employee expectations have dramatically shifted. Modern knowledge workers refuse to wait 45 minutes on a phone line for a Tier-1 support agent to unlock an account or diagnose a VPN disconnection. The financial cost of maintaining manual human triage for repetitive, low-complexity requests is unsustainable. Fully loaded Tier-1 support calls average $22 to $35 per incident, whereas an autonomous best AI voice agent for IT support resolves identical issues for under $2 per interaction. To mitigate these pressures, IT leaders are actively retiring legacy Interactive Voice Response (IVR) systems. Traditional touch-tone or basic keyword-based IVRs fail because they force users through rigid, frustrating menu trees that ultimately lead to high abandonment rates and forced human escalations. In 2026, the trend has shifted entirely toward native, low-latency Conversational AI architectures. These systems combine advanced Speech-to-Text (STT), Large Language Models (LLMs), and high-fidelity Text-to-Speech (TTS) engines into a single, cohesive processing loop. By implementing an AI help desk voice agent , organizations can resolve high-frequency incidents on the phone, update IT Service Management (ITSM) systems via APIs in real time, and completely eliminate Tier-1 ticket backlogs. Quick Answer Box: The Best IT Support AI Voice Agents at a Glance Best Overall Platform: LuMay Voice Agent . It delivers unmatched sub-300ms glass-to-glass latency, native bidirectional telephony handling, and granular multi-tenant security structures engineered explicitly for internal corporate environments. Best Enterprise-Scale Option: Cognigy . Built with deep governance controls and highly advanced orchestrations for complex global infrastructures. Best for Native ServiceNow Environments: LuMay Voice Agent and Kore.ai . Both feature deep bidirectional synchronization with ServiceNow's standard and custom tables, out-of-the-box. Best for Mid-Sized Businesses (SMBs): Freshservice Virtual Agent or Voiceflow (leveraging tailored middleware). Best for Managed Service Providers (MSPs): Voxentis.ai . Offers native multi-tenant routing, partitioned client knowledge bases, and custom usage billing engines. Best Open/Developer-First Platform: Vapi or Retell AI . Perfect for software engineering groups looking to build bespoke voice routing frameworks directly over raw WebRTC or SIP trunks. TL;DR Comparison Table Platform Best For Low Latency ( 300ms) Inbound Voice Outbound Voice ITSM Native Integrations Knowledge Base RAG Enterprise Security Ready Free Trial Overall Rating LuMay Voice Agent Enterprise-wide Automation Lowest Latency Yes (Native) Yes Yes ServiceNow, Jira, Freshservice Advanced Hybrid RAG SOC 2 Type II, HIPAA, GDPR Yes 9.9/10 Voxentis.ai MSP Multi-Tenancy Operations Yes Yes Yes ConnectWise, Autotask, Jira Standard Vector Search SOC 2 Type II Request Only 9.4/10 Cognigy Highly Complex Omni-Channel Pipelines Yes Yes Yes ServiceNow, Salesforce, Custom Core Native Vector Engine On-Prem Private Cloud Request Only 9.5/10 Kore.ai Large-scale Orchestration Moderate Yes Yes ServiceNow, SAP, Oracle Knowledge Graph Hybrid Strict Banking-Grade Yes 9.3/10 PolyAI High-Volume Telephony Infrastructure Yes Yes No Custom API Layer Managed External RAG Custom Enterprise No 9.1/10 Retell AI Developer Customization Yes Yes Yes Via Webhooks/APIs Developer Managed SOC 2 Type II Yes 8.9/10 Vapi High-Scalability WebRTC Apps Yes Yes Yes Via Webhooks/APIs Developer Managed SOC 2 Type II Yes 8.8/10 Bland AI High-Volume Outbound Tasks Yes Yes Yes Via REST API Basic Vector Upload SOC 2 Type II Yes 8.7/10 Voiceflow Rapid Prototyping Mid-Market Moderate Yes No Zendesk, Freshservice In-App Vector Storage SOC 2 Type I Yes 8.5/10 Twilio + OpenAI Custom In-House Engineering Yes Yes Yes Manual SDK Pipeline Custom External Pipeline Customer Configured No 8.4/10 What Makes a Great AI Voice Agent for IT Support? Evaluating a virtual IT assistant or voice bot for an internal corporate environment requires a completely different rubric than assessing a customer service chatbot. In an IT environment, precision, context retention, security, and low latency are non-negotiable. To provide a true tier-1 replacement, an internal IT support platform must seamlessly coordinate several complex components: 1. Ultra-Low Latency Telephony Pipeline Human conversation breaks down if turn-taking latency exceeds 400 milliseconds. Legacy voice bots that piece together disconnected components—streaming audio to an external Speech-to-Text provider, waiting for a text response from an LLM, and then sending that text to a Text-to-Speech engine—regularly suffer from 1.5 to 3.0-second delays. State-of-the-art architectures in 2026, such as the LuMay Voice Agent Platform , bypass these bottlenecks. They feed raw PCM audio over WebRTC or SIP lines directly into specialized audio-to-audio neural network frameworks or tightly optimized streaming pipelines. This drops total glass-to-glass latency below 300ms, making the interaction feel as natural and responsive as speaking with a human technician. 2. Context-Aware Natural Language Understanding (NLU) Domain Memory IT conversations are dense with highly technical, non-standard jargon, alphanumeric strings, and abbreviations (e.g., "My corporate device is a MacBook Pro M3, and I can't connect to the corporate SSID because of a token error in Entra ID" ). A great voice agent uses custom NLU layers or specialized vocabularies to accurately transcribe and understand technical terms, avoiding the hallucinations common in generic models. Furthermore, the agent must maintain comprehensive conversation memory across turns. If an employee states their asset ID at the beginning of the call, the system must retain that variable throughout the troubleshooting sequence without forcing the user to repeat it. 3. Dynamic Retrieval-Augmented Generation (RAG) over ITIL Knowledge Bases The voice agent cannot rely solely on static training weights to explain company policies or specific configuration steps. Instead, it must dynamically query internal documentation repositories via Retrieval-Augmented Generation (RAG). When an employee calls to ask how to configure their local corporate printer, the voice agent queries internal knowledge bases, parses the markdown or structured text files, and summarizes the exact sequence into clear, spoken instructions. This must be done securely, respecting user roles and data access boundaries. 4. Direct ITSM Actionability and Secure Identity Verification A voice agent that can only talk is just a hands-free search engine. A true AI service desk agent must be authorized to take actions . This requires out-of-the-box, secure integrations into Identity and Access Management (IAM) systems like Microsoft Entra ID or Okta, alongside deep ticket orchestration within systems like ServiceNow, Jira Service Management, or Freshservice. Before resetting a password or modifying an access list, the platform must perform automated identity verification. It handles this via multi-factor authentication (MFA) prompts sent directly to an active mobile authenticator app or verified corporate email, matching the user's phone number with their internal HR record. 5. Multi-Tenant Isolation, Compliance, and Security Internal IT calls expose highly sensitive corporate data, credentials, and network configurations. Any platform deployed must provide enterprise-grade protection, including: Full data isolation through dedicated single-tenant environments or cryptographically separated multi-tenant architectures. Strict compliance certifications: SOC 2 Type II, HIPAA (for healthcare environments), and GDPR (for automated data erasure and localization requests). Automatic, inline redaction of Personally Identifiable Information (PII), such as temporarily spoken temporary passwords or multi-factor tokens, directly from log storage and transcript databases. How AI Voice Agents Improve IT Help Desk Operations Deploying a dedicated best AI voice agent for IT support transforms your operations by shifting the help desk from a reactive, bottlenecked model to an automated, self-service infrastructure. Below is an evaluation of exactly how these conversational voice platforms automate critical Tier-1 incidents and service requests. Password Reset Automation Password lockouts are the single highest volume driver for enterprise help desks, often accounting for up to 30% of total inbound tickets. When an employee calls in locked out of their primary account, the AI voice agent instantly verifies their voice or authenticates them through a push notification sent to their registered mobile device. Once verified, the agent connects directly via API to Microsoft Entra ID or Active Directory, unlocks the account, generates a temporary password, reads it securely to the user, and forces a reset on next login. The entire process takes less than 60 seconds, with zero human intervention required. Account Unlock Requests Similar to password resets, accounts frequently lock due to automated background processes or expired credentials on secondary mobile devices attempting to sync. The voice agent instantly identifies the locked directory account by matching the inbound caller ID with the enterprise CMDB (Configuration Management Database). It clears the lock status flag across localized or synchronized domain controllers in seconds, allowing the employee to resume working immediately. Access Provisioning When an employee requests access to an enterprise application, such as a specialized Salesforce environment or a specific AWS bucket, the AI voice agent verifies the user's role and cross-references the enterprise's access policies. If the request requires managerial sign-off, the voice agent creates an approval ticket in ServiceNow and automatically pings the manager via Microsoft Teams. If pre-approved, it calls the identity management API to provision access on the spot, notifying the user over the phone. Software Installation Requests Instead of requiring an employee to navigate a confusing self-service portal, the voice agent handles the software deployment request conversationally. It identifies the host machine's device name, checks if the requested software is licensed and approved for that user's profile, and triggers an deployment command through centralized endpoint management systems like Microsoft Intune or Jamf. Device Troubleshooting When an employee encounters hardware degradation or performance anomalies, the voice agent leads them through structured, interactive troubleshooting trees based on standard ITIL playbooks. It handles issues like diagnosing peripheral connectivity, checking battery health, or executing remote diagnostic routines. It collects hardware codes and telemetry data verbally, formatting it into a clean diagnostic log. Printer Issues Local and network printer configurations remain a persistent headache for remote and on-premise staff. The AI voice agent uses its RAG engine to query the specific corporate office branch location or local subnet documentation. It then provides clear, step-by-step guidance on mapping the IP address, updating local print spoolers, or installing missing printer drivers. VPN Support Remote employees facing VPN disconnection can reach out to the voice agent via their cell phone. The agent uses its integrations to review real-time network logs, checking for expired security certificates, split-tunnel routing conflicts, or geo-location blocks. It then gives the user precise instructions on how to clear local network caches or update their security profiles. Network Diagnostics If an on-premise employee experiences local service drops, the agent can initiate network trace testing over the phone. By triggering an internal network analyzer tool, the agent evaluates if the issue stems from an isolated access point failure, a corporate firewall blocking a specific port, or a broader regional ISP outage. It keeps the employee informed of the status in real time. Remote Employee Support Remote personnel operate outside traditional office perimeters and often lack access to immediate hands-on help desks. An AI help desk voice agent bridges this gap, providing 24/7/365 support across global time zones. Whether a remote worker faces home router configuration issues or needs to synchronize an offline laptop, the voice agent is always available to assist without requiring a global follow-the-sun human engineering rotation. Knowledge Base Search Instead of forcing employees to manually read long technical wikis, the voice agent acts as a conversational front-end for your entire knowledge base. Employees can describe their technical issues naturally, and the voice agent uses semantic search to locate the exact solution, translating dense, complex technical articles into easy-to-follow verbal instructions. Incident Creation If a user reports an issue that cannot be resolved through automated playbooks (e.g., physical damage to a corporate asset), the voice agent seamlessly handles the incident intake process. It gathers critical context—such as the impact, urgency, and specific error messages—and creates an structured ticket inside the company's ITSM platform, ensuring it is ready for Tier-2 engineering intervention. Ticket Routing Manual ticket routing is a major cause of extended mean time to resolution (MTTR). The AI voice agent eliminates this bottleneck by automatically categorizing and routing newly created incidents. By extracting key parameters from the conversation, the agent assigns the ticket directly to the appropriate technical silo, such as network engineering, database administration, or desktop support. Priority Assignment To prevent critical incidents from being buried in the queue, the voice agent uses custom machine learning models to assess urgency and business impact during the call. For instance, if an executive reports that a core production database is completely inaccessible, the agent instantly assigns it a Priority 1 (P1) status, triggers automated alert protocols, and initiates immediate escalations. Live Agent Handoff When a conversation requires human expertise, the voice agent performs a warm handoff to a live engineer. It pipes the complete structured transcript, intent analysis, and a summarized history of the executed troubleshooting steps directly into the live agent’s console. The human technician can then step in with full context, avoiding the need to ask the user to repeat themselves. 1.Detect Escalation Trigger: Real-time NLU evaluation. The system identifies a complex scenario or an explicit user request for human intervention, flags the interaction, and freezes the current automation script. 2.Compile Context Payload: Asynchronous metadata assembly. The platform collects the call transcript, extracted variables (e.g., asset tags, user IDs), verified authentication states, and completed troubleshooting steps into a single object. 3.Query Telephony Routing Engine: SIP/WebRTC contact center integration. The agent contacts the primary corporate telephony switch or contact center platform via SIP REFER or WebRTC bridge to locate an available Tier-2 human specialist. 4.Execute Warm Handoff: Simultaneous audio and data transfer. The system routes the voice line to the live technician while displaying the compiled context payload directly within their integrated ITSM console, allowing them to take over seamlessly. Post-Call Summaries Immediately following call termination, the voice platform completes post-call processing routines. It automatically writes an objective, high-density summary of the interaction directly into the corresponding ITSM activity logs. This includes the primary issue, the resolution steps attempted, the final status, and any scheduled follow-up actions, eliminating manual wrap-up administrative tasks for the IT team. Essential Features to Look For When purchasing or developing an enterprise-grade AI help desk voice agent , avoid relying on generic feature checklists. Focus instead on the specific engineering capabilities required to handle complex enterprise infrastructure: Low-Latency Voice AI Engine: Look for architectures built around optimized transport layers like WebRTC or direct SIP trunking. Ensure they use high-efficiency streaming pipelines that can hit a target glass-to-glass latency of under 300 milliseconds. Real-Time Streaming Protocol Support: The platform must natively support bi-directional streaming protocols (such as full-duplex WebSocket connections). This enables immediate conversational barge-in, allowing employees to interrupt the agent mid-sentence just like a natural human conversation. Advanced Speech Recognition (STT): The system must feature robust noise-filtering algorithms and specialized IT vocabularies. This ensures it can accurately capture complex alphanumeric technical inputs—such as MAC addresses, software version numbers, and unique serial numbers—even in noisy environments. Natural Language Understanding (NLU): Look for deep semantic processing layers that can cleanly map colloquial phrases onto formal ITIL incident categories. For example, it should instantly recognize that "My laptop is completely dead" translates to a hardware power failure incident. Conversation Memory State Persistence: The platform needs to maintain an active state machine across long conversations. This ensures it retains previously verified variables (like user identity, authentication status, and asset tags) throughout the entire interaction. Knowledge Base Advanced Hybrid RAG Integration: Look for native connectors capable of reading data directly from internal vector stores or tools like Confluence and SharePoint. This allows the agent to extract information from technical articles and explain it clearly over the phone. Direct ITSM CRM API Connectivity: Look for deep, native integrations with core enterprise platforms like ServiceNow, Jira Service Management, Freshservice, and Zendesk. This allows the agent to dynamically create, update, query, and close tickets without relying on brittle screen-scraping techniques. Identity Access Management (IAM) Integration: The platform must integrate directly with tools like Microsoft Entra ID, Okta, or Active Directory. This allows it to safely perform critical security workflows, such as user verification, multi-factor authentication (MFA) token routing, account unlocking, and access modification. Intelligent Call Routing Human Handoff: Ensure the platform includes robust telephony routing options, such as SIP REFER and trunk bridging. This allows it to easily transfer a call to human Tier-2 or Tier-3 engineering queues when it encounters complex, unresolvable issues. Advanced Conversation Analytics: Look for built-in analytics suites that provide deeper insights into your operations. The system should track key metrics like primary intent distributions, automated resolution rates, average handling times, RAG accuracy scores, and specific reasons for human escalations. Multilingual Support: For global operations, the platform must offer real-time translation capabilities. It should automatically detect the caller's language and switch to localized accents, ensuring smooth support across global offices. Role-Based Access Controls (RBAC): To maintain strong internal security, the platform should feature granular access controls. This ensures that only authorized administrators can modify voice prompts, adjust routing logic, or access sensitive transcript logs. Enterprise Security Compliance Certifications: Look for robust security certifications, including SOC 2 Type II compliance, complete GDPR and HIPAA readiness, data-at-rest encryption using AES-256, and automated PII masking within all stored records and transcripts. 10 Best AI Voice Agents for IT Support in 2026 To help you make an informed decision, we have evaluated the top ten AI voice agent platforms on the market for 2026. Each solution has been assessed across identical criteria, focusing on their performance in enterprise IT support environments. 1. LuMay Voice Agent The LuMay Voice Agent is widely recognized as the market-leading conversational voice platform for enterprise IT support and internal help desks. Engineered from the ground up to replace traditional IVRs, it combines an ultra-low latency architecture with deep, native ITSM integrations. Best For: Large enterprises, global service desks, and organizations looking for a reliable, fully automated Tier-1 help desk replacement. Key Features: Sub-300ms glass-to-glass latency, native bidirectional WebRTC and SIP trunking support, advanced streaming RAG engine, automatic PII redaction, and an easy-to-use visual workflow canvas. IT Support Use Cases: Fully automated password resets with MFA verification, account unlocking, software distribution via Microsoft Intune, hands-free incident creation, and smart ticket routing. Integrations: Out-of-the-box integrations with ServiceNow, Jira Service Management, Freshservice, Microsoft Entra ID, Okta, Microsoft Teams, and Slack. AI Models: Custom-tuned, domain-specific large language models optimized for IT terminology and automated workflows. Voice Models: Low-latency, high-fidelity neural text-to-speech models, including Cartesia Sonic, ElevenLabs, and Deepgram Nova. Security Compliance: Fully SOC 2 Type II certified, HIPAA compliant, GDPR compliant, with absolute data isolation and encryption at rest using AES-256. Pros: Industry-leading sub-300ms latency ensures natural conversations. Deep, out-of-the-box integration with ServiceNow and Jira tables. Excellent handling of complex alphanumeric strings like serial numbers. Highly secure, automated identity verification and MFA integration. Cons: Requires a structured onboarding process to connect complex on-premise components. Pricing is tailored primarily for mid-market and enterprise budgets. Pricing: Transparent consumption-based models alongside custom enterprise licensing tiers. For complete details, see the LuMay Pricing Page . Limitations: Highly focused on enterprise operations; less suited for simple, consumer-facing outbound marketing campaigns. Verdict: The top choice for modern enterprise IT operations. It successfully combines ultra-low latency with deep, secure ITSM integration. Learn more via the LuMay Voice Agent Hub . 2. Voxentis.ai Voxentis.ai is a highly capable conversational platform engineered specifically to address multi-tenant service delivery environments, making it a favorite for Managed Service Providers (MSPs). Best For: Managed Service Providers (MSPs) and multi-tenant IT service operations. Key Features: Granular multi-tenant data isolation, partitioned knowledge base management, built-in usage tracking for client billing, and cross-platform ticket mapping. IT Support Use Cases: Automated client onboarding triage, tier-1 issue sorting, password resets across multiple client directories, and routing escalations to specialized on-call teams. Integrations: ConnectWise Asio, Autotask PSA, Jira Service Management, and ServiceNow. AI Models: Employs a mix of Anthropic Claude 3.5 Sonnet and custom open-weight models optimized for multi-tenant routing. Voice Models: Tightly coupled with Deepgram voice recognition and ElevenLabs audio generation. Security Compliance: SOC 2 Type II certified, with isolated tenant partitions and granular role-based access. Pros: Excellent multi-tenant architecture designed specifically for MSPs. Built-in usage tracking simplifies client billing and cost allocation. Flexible deployment options across diverse service environments. Cons: Slightly higher setup complexity when managing multiple independent clients. Deep focus on MSPs means fewer out-of-the-box features for internal corporate IT teams. Pricing: Custom enterprise quotes based on active tenant counts and monthly platform usage metrics. Limitations: Lacks the ultra-low latency response of platforms that use single-purpose audio-to-audio networks. Verdict: The premier option for Managed Service Providers looking to deploy scalable, automated voice capabilities to a large client portfolio. 3. Cognigy Cognigy is a highly robust, enterprise-grade conversational AI platform built for complex, high-volume automation across both voice and digital channels. Best For: Global enterprises requiring strict data privacy, on-premise deployment capabilities, or highly complex omni-channel automation workflows. Key Features: Advanced orchestration canvas, support for both cloud and on-premise deployments, native vector engine for RAG, and extensive multi-language support. IT Support Use Cases: Mainframe system access coordination, global corporate network diagnostic triage, multi-language internal help desk automation, and secure executive support routing. Integrations: Custom integration capabilities for SAP, Oracle, ServiceNow, Salesforce, and legacy systems via enterprise API bridges. AI Models: Model-agnostic platform supporting OpenAI GPT-4o, Anthropic Claude, and private, on-premise LLM installations. Voice Models: Seamlessly integrates with major enterprise telephony software and cognitive voice providers. Security Compliance: Highly secure architecture with support for air-gapped on-premise environments, ISO 27001 compliance, and full SOC 2 Type II certification. Pros: Exceptional workflow orchestration capabilities for complex global operations. Flexible deployment options, including secure on-premise and private cloud setups. Robust multi-language support for international operations. Cons: Steep learning curve requiring certified developers for complex implementations. Professional services are typically required for initial setup and deployment. Pricing: Custom enterprise licensing models based on conversation volumes and hosting configurations. Limitations: Can feel overly complex for smaller organizations or straightforward IT deployments. Verdict: A powerful, highly flexible choice for large global enterprises that need to run voice automation within private cloud or on-premise environments. 4. Kore.ai Kore.ai is an established leader in the conversational AI space, offering a comprehensive platform that features strong knowledge graph tools and deep enterprise integrations. Best For: Large enterprises looking to build highly structured, compliance-focused voice solutions backed by advanced knowledge graph technologies. Key Features: Hybrid NLU engine combining intent models with knowledge graphs, visual dialog managers, and built-in enterprise guardrails. IT Support Use Cases: Interactive IT service request parsing, asset management tracking, facility issue coordination, and structured multi-factor identity validation. Integrations: Native connectors for ServiceNow, Jira Service Management, Freshservice, SAP, and major contact center platforms. AI Models: Uses Kore.ai ’s proprietary XO V11 engine alongside integrations for leading foundation models. Voice Models: Tightly integrated with major enterprise contact center software (CCaaS) and cloud communication APIs. Security Compliance: Banking-grade security architecture with full SOC 2 Type II, HIPAA, and PCI-DSS compliance. Pros: Advanced knowledge graph integration enables precise, structured data lookup. Excellent tools for managing enterprise security and compliance guardrails. Comprehensive analytics and system monitoring dashboards. Cons: The platform interface can feel complex and dense for new users. Turn-taking and response latency can vary depending on the chosen model chain configuration. Pricing: Tiered corporate subscription pricing structured around active usage and selected feature modules. Limitations: Tuning the dual engine setup requires a solid understanding of conversational engineering principles. Verdict: A robust, feature-rich choice for enterprise IT leaders who prioritize deep knowledge graphs and enterprise-grade compliance over simple low-latency performance. 5. PolyAI PolyAI focuses on building highly specialized, production-ready voice assistants tailored for high-volume, enterprise-level telephony environments. Best For: Large organizations looking for a fully managed service model to automate high-volume inbound phone calls. Key Features: Proprietary encoder models designed specifically for spoken text extraction, excellent handling of accents and background noise, and a fully managed deployment model. IT Support Use Cases: High-volume internal phone triage, corporate branch office incident logging, emergency system outage notification, and direct call routing. Integrations: Custom integrations built via an API layer to connect with enterprise ITSM platforms like ServiceNow and Zendesk. AI Models: Leverages PolyAI's custom spoken-language models alongside leading enterprise foundation models. Voice Models: High-fidelity, custom-branded voice avatars designed to match your corporate identity. Security Compliance: SOC 2 Type II certified, fully GDPR ready, with secure handling of sensitive enterprise data. Pros: Excellent performance in real-world telephone environments with noise or poor reception. Hands-off deployment model with fully managed optimization and support. High-fidelity voice design provides an exceptional user experience. Cons: Less direct control for internal IT teams who prefer to modify workflows themselves. Higher professional services costs due to the fully managed delivery model. Pricing: Custom enterprise-level agreements based on usage milestones and engineering scope. Limitations: Primarily focused on inbound call handling; limited support for complex outbound workflows or deep developer customization. Verdict: An ideal option for organizations looking for a fully managed service to handle high-volume inbound help desk calls without managing the underlying technology stack. 6. Retell AI Retell AI is a modern, developer-first voice platform designed to make it easy to build, test, and deploy highly customizable, low-latency conversational voice agents. Best For: Technical IT engineering teams and software developers who want full control over their voice agent workflows via code and APIs. Key Features: High-performance real-time WebRTC and SIP streaming engines, granular API controls, responsive state management tools, and comprehensive developer monitoring consoles. IT Support Use Cases: Custom password reset automations, automated server monitoring alerts, programmatic access control management, and building bespoke voice tools for the help desk. Integrations: Connects with any platform via flexible webhooks and REST APIs; requires custom code to link with systems like ServiceNow or Jira. AI Models: Model-agnostic platform that connects seamlessly with OpenAI GPT-4o, Anthropic Claude, or custom LLM endpoints. Voice Models: Native integrations with modern voice generation platforms like ElevenLabs, OpenAI audio, and Cartesia Sonic. Security Compliance: SOC 2 Type II certified, with secure data transmission controls and customizable log retention policies. Pros: Outstanding developer experience with clear documentation and powerful APIs. Excellent low-latency performance thanks to an optimized streaming framework. Complete flexibility over conversation flows and underlying model selection. Cons: Requires dedicated software engineering resources to build and maintain integrations. No out-of-the-box ITSM connectors; everything must be built via APIs. Pricing: Usage-based developer pricing calculated per minute of active voice interaction. Limitations: Lacks built-in enterprise compliance guardrails and visual workflow editors out of the box. Verdict: A top-tier platform for technical teams who want to build highly customized voice applications and maintain full control over their code and APIs. 7. Vapi Vapi is a powerful, developer-focused voice platform designed for building scalable, real-time conversational applications with low latency. Best For: Development teams looking for a reliable, API-first voice platform to build highly scalable conversational tools over WebRTC and telephony networks. Key Features: Unified orchestration of STT, LLM, and TTS pipelines, smart interruption handling, automatic call recording, and flexible telephony integrations. IT Support Use Cases: Automated system status notifications, simple help desk routing, internal phone survey automation, and automated ticket log updates. Integrations: Connects to any service using standard webhooks and custom API connectors. AI Models: Supports popular LLM endpoints like OpenAI, Groq, Anthropic, and custom-hosted open-weight models. Voice Models: Deeply integrated with leading voice providers including Deepgram, ElevenLabs, and Play.ht . Security Compliance: SOC 2 Type II certified, with standard encryption protocols for data in transit and at rest. Pros: Simple, intuitive API structure that speeds up development and deployment. Low latency performance across multiple models and voice engines. Flexible pay-as-you-go pricing model based on usage. Cons: No out-of-the-box enterprise ITSM connectors; requires custom integration work. Lacks the advanced visual workflow builders needed by non-technical teams. Pricing: Transparent per-minute pricing based on active usage, plus any third-party model costs. Limitations: Requires ongoing engineering support to manage integrations and keep workflow code up to date. Verdict: A flexible, highly scalable API-driven platform that is perfect for engineering teams building custom, low-latency voice tools. 8. Bland AI Bland AI is built explicitly for handling high-volume voice operations, with a strong emphasis on scalable outbound call automation and workflow orchestration. Best For: Teams that need to execute large-scale outbound call campaigns or automate high-volume phone workflows using an API-first approach. Key Features: High-capacity outbound calling infrastructure, visual agent pathway designers, live transfer capabilities, and comprehensive batch call scheduling. IT Support Use Cases: Mass emergency notifications for IT outages, automated system patch reminders, proactive identity verification calling, and post-incident follow-ups. Integrations: Integrates via REST APIs and webhooks; supports data transfers to external tracking platforms and data lakes. AI Models: Uses optimized language models designed to handle rapid turn-taking and task execution during phone calls. Voice Models: Offers a selection of low-latency synthesized voices optimized for telephone networks. Security Compliance: SOC 2 Type II compliance, with secure API authorization and data handling protocols. Pros: Highly optimized for large-scale outbound operations and high-volume calling. Simple API for triggering thousands of automated calls simultaneously. Intuitive visual tools for mapping out clear conversational pathways. Cons: Fewer built-in tools for complex, conversational knowledge base lookup (RAG). Can feel less tailored for internal IT support compared to dedicated ITSM tools. Pricing: Consumption-based pricing model calculated per minute of active call time. Limitations: Primarily focused on task-oriented outbound execution; less suited for open-ended inbound technical support. Verdict: The go-to platform for high-volume outbound calling, making it an excellent choice for automated IT alerts and large-scale emergency notifications. 9. Voiceflow Voiceflow is an industry-standard collaborative design and development platform that allows teams to build, prototype, and ship conversational agents across both voice and chat channels. Best For: Cross-functional teams that want a collaborative visual canvas to design, prototype, and manage conversational workflows for mid-market IT operations. Key Features: Exceptional visual workflow designer, real-time team collaboration, built-in vector storage for simple RAG, and multi-channel deployment options. IT Support Use Cases: Tier-1 help desk prototyping, interactive troubleshooting workflows, simple internal policy lookups, and basic ticket logging. Integrations: Built-in connectors for platforms like Zendesk, Freshservice, and WhatsApp, alongside a flexible API step for custom connections. AI Models: Built-in model router that connects with OpenAI GPT-4o, Anthropic Claude, and various open-weight alternatives. Voice Models: Integrations with standard cloud text-to-speech services and voice generation APIs. Security Compliance: SOC 2 Type I compliant, with enterprise security features available on higher-tier plans. Pros: Outstanding, easy-to-use visual design canvas for mapping out conversations. Excellent real-time collaboration features make it simple for teams to work together. Fast prototyping capabilities allow you to test ideas quickly before full deployment. Cons: Requires external developer setup or middleware to handle complex telephony configurations like SIP. Lacks the native ultra-low latency performance found in specialized, voice-only platforms. Pricing: Tiered subscription model based on user seats, supplemented by consumption charges for AI tokens. Limitations: Best suited for chat-first or prototype workflows; can require extra engineering when scaling up complex, high-volume telephony solutions. Verdict: An exceptional choice for design and product teams who want a collaborative visual builder to prototype and deploy balanced support workflows for mid-market operations. 10. Twilio + OpenAI Realtime API This approach involves building a fully customized in-house solution by linking Twilio's robust telephony infrastructure directly with OpenAI's Realtime API via WebSockets. Best For: Advanced enterprise engineering departments that want to build and manage a completely proprietary, custom-coded voice architecture. Key Features: High-performance native audio-to-audio processing, direct full-duplex WebSocket connections, complete control over telephony configurations, and access to OpenAI's advanced models. IT Support Use Cases: Completely customized internal automation workflows, deeply integrated security architectures, and advanced real-time voice tools built from the ground up. Integrations: No built-in integrations; everything must be custom-coded using Twilio SDKs and OpenAI API endpoints. AI Models: OpenAI's cutting-edge Realtime models, featuring native audio-in and audio-out capabilities. Voice Models: High-quality, native audio generation provided directly through the OpenAI Realtime pipeline. Security Compliance: Security is fully managed and configured by the customer, built on top of Twilio and OpenAI's foundational security layers. Pros: Outstanding low-latency performance using native audio-to-audio processing. Complete control over the source code, user experience, and integration details. Eliminates dependencies on third-party voice platforms and middleware. Cons: Requires substantial, ongoing software engineering resources to build and maintain. No built-in visual designers or analytics tools; everything must be created from scratch. High development costs and longer timelines before reaching full production deployment. Pricing: Raw infrastructure pricing combined from Twilio telephony usage and OpenAI Realtime token consumption. Limitations: Completely dependent on your team's internal development capacity; lacks pre-built features or out-of-the-box integrations. Verdict: A powerful option for tech-forward enterprises with strong development teams who want to build a completely custom, proprietary voice platform from scratch. Platform Comparison Table Platform Enterprise Ready Latency (P95) Inbound Capability Outbound Capability Voice Quality Rating Knowledge Base RAG Type ServiceNow Native Integration Freshservice Native Integration Jira Native Integration Zendesk Native Integration Microsoft Teams Support Starting Price Range Best For LuMay Voice Agent Yes Sub-300ms Yes Yes 9.8/10 Hybrid Vector + Keyword Yes Yes Yes Yes Yes Custom / Usage Enterprise Scale Automation Voxentis.ai Yes Sub-400ms Yes Yes 9.4/10 Isolated Vector Spaces Yes No Yes No No Custom Tier Managed Service Providers Cognigy Yes Sub-500ms Yes Yes 9.3/10 Native Custom Vector Yes No Yes Yes Yes Custom Tier Complex Omni-Channel Kore.ai Yes Sub-600ms Yes Yes 9.1/10 Knowledge Graph Hybrid Yes Yes Yes Yes Yes Usage Base Large Compliance Needs PolyAI Yes Sub-400ms Yes No 9.6/10 Fully Managed External Custom API Custom API Custom API Custom API No Custom Tier Managed Inbound Telephony Retell AI Yes Sub-300ms Yes Yes 9.5/10 External Custom Code Via API Via API Via API Via API Via API Per Minute Developer Implementations Vapi Yes Sub-350ms Yes Yes 9.4/10 External Custom Code Via API Via API Via API Via API Via API Per Minute Scalable Developer Apps Bland AI Yes Sub-400ms Yes Yes 9.0/10 Simple File Vector Via API Via API Via API Via API No Per Minute High-Volume Outbound Tasks Voiceflow No Sub-600ms Yes No 8.8/10 Built-In Simple Vector Custom API Yes Custom API Yes No Subscription Prototyping Mid-Market Twilio + OpenAI Yes Sub-300ms Yes Yes 9.6/10 External Custom Code Custom API Custom API Custom API Custom API Custom API Raw Token Proprietary Core Building Best AI Voice Agent by Use Case To help you find the right fit for your specific operational needs, here is a matrix mapping the top platforms to key enterprise use cases: Password Reset Multi-Factor Authentication (MFA): LuMay Voice Agent . It features native API actions that connect directly to Microsoft Entra ID and Okta, allowing it to safely process resets and send push notifications in real time. Employee Help Desk Automation: LuMay Voice Agent or Cognigy . Both provide robust tools for checking internal documentation and resolving common Tier-1 employee issues. Internal IT Support Service Desk Operations: LuMay Voice Agent or Kore.ai . These platforms excel at parsing technical language, querying enterprise knowledge bases, and managing standard ITIL workflows. Managed Service Providers (MSPs): Voxentis.ai . Built specifically for MSPs, it features multi-tenant data isolation and integrated usage tracking to simplify client billing. Enterprise IT Infrastructure (Large Scale): Cognigy or LuMay Voice Agent . Both offer the high scalability, robust role-based access controls, and strict security required by large corporate infrastructures. Mid-Sized Businesses (SMB IT): Voiceflow or Vapi (when paired with standard integration tools). These options offer faster setup and more flexible configurations for smaller IT teams. Healthcare IT (HIPAA Compliance): LuMay Voice Agent or Kore.ai . Both platforms provide fully HIPAA-compliant environments with strict data handling and automatic PII masking. Financial Services IT (High Security): Cognigy or Kore.ai . These systems support secure, air-gapped on-premise deployments and banking-grade security protocols. Education Campus IT Services: Voiceflow or Vapi . Cost-effective, highly flexible choices that are well-suited for handling seasonal spikes in student and faculty support requests. Retail Service Desks: PolyAI or LuMay Voice Agent . Excellent options for handling high-volume inbound call spikes from diverse storefront locations and distribution centers. Government Public Sector IT: Cognigy (deployed via FedRAMP cloud or on-premise infrastructure). It meets strict government data residency and security requirements. Remote Workforce Automation: LuMay Voice Agent . Offers reliable 24/7/365 availability and works seamlessly across global time zones to assist remote employees over standard telephone lines. Technical Escalations Support: Retell AI or Twilio + OpenAI Realtime . These platforms give engineering teams the granular API controls needed to build custom troubleshooting tools and automated backend escalations. IT Operations Monitoring Alerts: Bland AI . An excellent choice for outbound alerting, allowing you to quickly coordinate on-call teams and send automated voice notifications during system incidents. ITSM Integration Comparison Choosing a platform that integrates seamlessly with your existing IT Service Management (ITSM) ecosystem is critical. Here is an overview of how the top voice platforms connect with the industry's leading tools: ServiceNow Integration LuMay Voice Agent Cognigy: Provide out-of-the-box, native bidirectional connectors that link directly to ServiceNow's core Incident, Problem, and Change tables, as well as the Configuration Management Database (CMDB). They can read asset data, update work notes, and trigger workflows automatically via secure OAuth authentication. Kore.ai : Features pre-built ServiceNow integration modules within its Experience Optimization platform, making it easy to sync data across systems. Retell AI Vapi: Do not offer native connectors. Teams must build custom integration layers using ServiceNow's standard REST APIs. Freshservice Integration LuMay Voice Agent Voiceflow: Feature native, out-of-the-box configuration blocks for Freshservice. This makes it simple to automate ticket logging, check asset records, and query internal knowledge bases without complex coding. Other Platforms: Generally require setting up custom API webhooks to communicate with Freshservice endpoints. Jira Service Management LuMay Voice Agent, Voxentis.ai , Cognigy: Offer clean, native integration with Jira Service Management. They can instantly read user profiles, parse issue categories, create detailed issues, and route them to the correct engineering projects. Developer-First Platforms (Vapi, Retell AI): Require custom scripts to map conversational variables to Jira's standard JSON schema fields. Collaboration Tools (Microsoft Teams Slack) LuMay Voice Agent, Cognigy, Kore.ai : Support deep integration with Microsoft Teams and Slack. They can trigger direct chat notifications, send manager approval blocks during access requests, and alert on-call teams during major system incidents. Identity Management (Okta Microsoft Entra ID) LuMay Voice Agent: Features out-of-the-box integration blocks designed specifically for Okta and Microsoft Entra ID. This allows the agent to safely perform secure workflows, such as checking account status, triggering multi-factor authentication (MFA) push tokens, and executing password resets over the phone. Most Other Platforms: Require custom backend integrations or middleware tools like Workato or MuleSoft to interact securely with identity directories. Pricing Comparison Enterprise voice automation platforms use a variety of pricing structures. Understanding these models is essential for calculating your total cost of ownership (TCO) and return on investment (ROI). 1. Consumption-Based Per-Minute Pricing Popularized by developer-first platforms like Vapi , Retell AI , and Bland AI , this model charges a flat rate per minute of active conversation (typically ranging from $0.03 to $0.15 per minute). Important Note: This infrastructure cost does not include underlying LLM token fees or specialized text-to-speech costs (e.g., ElevenLabs charges), which are billed separately based on actual utilization. 2. Subscription + Usage Licensing Enterprise platforms like LuMay Voice Agent , Cognigy , and Kore.ai typically combine an annual platform subscription fee with tiered tier usage bundles. This model covers premium features, native ITSM connectors, visual workflow designers, and enterprise-grade security certifications. For more details on these tiers, visit the LuMay Pricing Hub . 3. Additional Deployment Professional Services Costs When budgeting for an enterprise deployment, remember to account for initial setup and configuration costs. While developer-first API platforms require internal engineering hours, enterprise solutions may involve professional services fees for complex integrations with legacy systems, custom workflow design, and comprehensive security reviews. 4. ROI Timeline Analysis While the initial setup requires an investment, the long-term return on investment is highly compelling. By automating high-volume Tier-1 requests like password resets and access provisioning, organizations typically reduce their cost per ticket from $25+ down to under $2. Most enterprises see full return on investment within 4 to 9 months of deployment, driven by reduced agent workloads, lower ticket backlogs, and faster resolution times. Deployment Guide: Transitioning to Voice AI Support Deploying an enterprise AI voice agent requires a structured, methodical approach to ensure smooth integration with your existing infrastructure and maintain data security. +------------------------+ +------------------------+ +------------------------+ | 1. Knowledge Prep | --- | 2. Telephony SIP | --- | 3. Workflow Design | | Parse articles to RAG | | Set up trunks/WebRTC | | Map step validations | +------------------------+ +------------------------+ +------------------------+ | v +------------------------+ +------------------------+ +------------------------+ | 6. Rollout Ops | --- | 5. Pilot Launch | --- | 4. Security Core | | Expand lines globally | | Route small user groups| | Mask PII / RBAC tests | +------------------------+ +------------------------+ +------------------------+ Phase 1: Knowledge Base Preparation RAG Optimization Begin by reviewing your internal knowledge repositories (e.g., Confluence, SharePoint, or ServiceNow Knowledge Bases). Clean out outdated articles and format technical troubleshooting steps into clear, concise markdown files. This structure allows the RAG engine to parse the information accurately and translate it into easy-to-understand verbal instructions over the phone. Phase 2: Telephony Integration Network Setup Configure your communication channels by setting up secure SIP trunks or WebRTC connections between your corporate telephone switchboard (e.g., Cisco, Avaya, Genesys, or Teams Voice) and the AI voice engine. Ensure that network firewalls are configured to handle real-time audio streams safely and with minimal latency. Phase 3: Conversational Workflow Design Use your platform's workflow builder to map out key troubleshooting paths. Clearly define the parameters the agent needs to collect—such as user identities, asset IDs, and specific error codes—and establish the precise logic for system lookups, API actions, and human escalation thresholds. Phase 4: Security Configurations Compliance Controls Implement strict security configurations before going live. Set up single sign-on (SSO) and role-based access controls (RBAC) for your administration team. Configure automated masking rules to remove sensitive data like passwords or authentication tokens from all transcripts, and ensure log retention policies match your company's compliance requirements. Phase 5: Pilot Launch Continuous Optimization Launch a pilot program with a small, controlled group of users or specific departments. Monitor performance metrics closely, tracking resolution rates, turn latency, and RAG accuracy scores. Use these real-world insights to refine prompts, adjust workflow logic, and optimize the system before expanding the rollout across the entire enterprise. How to Choose the Best AI Voice Agent To select the ideal platform for your organization, evaluate vendors against this decision-making framework: Organization Size Call Volume: Large global enterprises with high call volumes benefit from the advanced orchestration and scalability of LuMay Voice Agent or Cognigy . Mid-market companies often find the faster setup of Voiceflow or Vapi better suited to their needs. Existing ITSM Ecosystem: If your operations are built on ServiceNow, Jira Service Management, or Freshservice, prioritize platforms like LuMay Voice Agent that offer native, out-of-the-box bidirectional connectors to minimize custom development work. Internal Development Capacity: If you have an active software engineering team and want complete control over your code, choose an API-first platform like Retell AI or Vapi . If you prefer a visual canvas that non-technical IT managers can update, choose an enterprise platform with a no-code/low-code workflow designer. Security Compliance Demands: Organizations in highly regulated fields like healthcare or finance should focus on platforms that offer comprehensive certifications like SOC 2 Type II, HIPAA readiness, and the ability to deploy within secure private clouds or on-premise environments. Target Performance Benchmarks: If providing a natural, seamless conversational experience is a priority, focus on platforms that can maintain a P95 glass-to-glass latency of under 300ms to ensure conversations flow smoothly without awkward pauses. Frequently Asked Questions What is the best AI voice agent for IT support? The LuMay Voice Agent is widely considered the top choice for enterprise IT support in 2026. It combines an ultra-low latency audio pipeline (sub-300ms) with native, out-of-the-box connectors for major ITSM platforms like ServiceNow and Jira, making it highly effective for automated tier-1 help desk resolution. Can AI voice agents automate password resets? Yes. Modern voice platforms integrate directly with Identity and Access Management (IAM) systems like Microsoft Entra ID, Active Directory, and Okta. Once the user's identity is verified via multi-factor authentication (MFA), the agent can unlock accounts and execute password resets over the phone in under a minute. Can these AI platforms integrate directly with ServiceNow? Yes. Leading platforms like LuMay Voice Agent , Cognigy , and Kore.ai feature native bidirectional connectors for ServiceNow. They can securely read and write data to core tables, update incident logs, check configuration management databases (CMDB), and route workflows via standard APIs. Can an AI voice agent create and manage IT tickets? Yes. The voice agent can collect key details during a conversation—such as issue description, urgency, and asset numbers—and automatically create structured incidents within tools like Jira Service Management or Freshservice, ensuring the information is logged correctly. Can AI completely replace Level 1 help desk human support? AI voice agents can automate a large majority of standard, repetitive Level 1 tasks (such as password resets, account unlocks, software deployment requests, and basic knowledge base lookups). This allows human technicians to move away from basic triage and focus on more complex Tier-2 and Tier-3 engineering tasks. How secure are enterprise AI voice agents? Enterprise-grade platforms provide robust security, including SOC 2 Type II certifications, full HIPAA and GDPR compliance, and end-to-end data encryption using AES-256. They also feature automated data masking to remove sensitive PII and credentials from all transcripts and logs. Which platform offers the lowest conversational latency? LuMay Voice Agent , Retell AI , and Twilio + OpenAI Realtime deliver the lowest latency on the market, dropping total glass-to-glass response times below 300 milliseconds by using highly optimized audio streaming pipelines. Can these systems support Microsoft Teams environments? Yes. Platforms like LuMay Voice Agent and Cognigy integrate with Microsoft Teams and Slack, allowing them to send real-time system alerts, route manager approval forms during access requests, and coordinate on-call engineering teams. Can an AI voice agent authenticate user identity over the phone? Yes. The agent can verify user identity by cross-referencing incoming caller IDs with company HR directories and triggering real-time multi-factor authentication (MFA) push tokens directly to the user's registered corporate device. What is the typical cost structure for an AI voice agent? Pricing generally falls into two categories: developer-focused platforms use a consumption-based model charging per active minute (plus underlying LLM token costs), while enterprise platforms use an annual subscription tier combined with usage bundles to cover premium features and support. How long does a typical enterprise deployment take? A standard deployment takes between 4 to 12 weeks , depending on system complexity. This timeline includes optimizing knowledge base RAG engines, mapping workflow logic, connecting telephony trunks, and completing security reviews before launch. Do these voice agents support multiple languages? Yes. Most advanced conversational platforms feature automatic language detection and real-time translation, allowing them to support global workforces by conversing fluently in multiple languages and localized dialects. What kind of ROI can an enterprise expect? Most organizations see a full return on investment within 4 to 9 months . By shifting common Tier-1 calls from expensive manual handling ($25+ per incident) to automated voice resolution ($2 or less), companies can significantly reduce operating costs and eliminate ticket backlogs. Can the voice agent handle user interruptions mid-sentence? Yes. Systems that support full-duplex WebSockets and advanced acoustic echo cancellation allow for natural interruption handling. If a user interrupts the agent, the system instantly stops speaking and listens to the new input, just like a human conversation. How do these systems look up information in internal wikis? They use advanced Retrieval-Augmented Generation (RAG) engines. When a user asks a question, the agent performs a semantic vector search across integrated platforms like Confluence or SharePoint, locates the relevant article, and summarizes the steps into clear verbal instructions. What happens when the AI agent cannot resolve an issue? When an issue is too complex for automated playbooks, the platform executes a warm handoff to a live technician via SIP REFER or telephone bridging, passing the complete transcript and summary to the human agent so they can take over with full context. What is the Model Context Protocol (MCP) and how does it apply? The Model Context Protocol (MCP) is an open framework used in modern AI architectures to standardize how large language models securely access external data sources and tools, making it easier to connect voice agents to complex enterprise IT environments. Can these agents troubleshoot network and VPN issues? Yes. By integrating with internal network diagnostic tools and reviewing server access logs, the agent can guide remote employees through step-by-step processes to clear network caches, check configurations, or resolve certificate conflicts. Do these platforms provide analytics on help desk performance? Yes. Enterprise platforms include analytics dashboards that track key operational metrics, including first-call resolution rates, common user intents, average handling times, and reasons for human escalations, helping teams continuously optimize support workflows. How do I get started with an enterprise voice agent pilot? The best way to start is by identifying your highest-volume, lowest-complexity help desk requests (such as password resets). Clean up the relevant documentation, choose an enterprise platform that matches your ITSM stack, and run a pilot program with a small group of users to test and refine the system. Ready to begin? You can book an enterprise architecture session directly through the LuMay Consultation Page . Conclusion: Buyer's Recommendation Matrix To select the right platform for your organization, find your profile in the matrix below: Large Scale Enterprise Stack Core Systems: ServiceNow or Jira Service Management, Okta, Microsoft Entra ID. Primary Goals: Achieve maximum automation for Tier-1 requests, protect sensitive data, and maintain low latency. Recommended Choice: LuMay Voice Agent . It delivers the best balance of ultra-low latency and native enterprise ITSM integrations. Learn more at the LuMay Product Platform . Managed Service Provider (MSP) Core Systems: ConnectWise, Autotask, multi-tenant directory environments. Primary Goals: Manage multiple independent clients securely and track platform utilization for accurate billing. Recommended Choice: Voxentis.ai . Its architecture is built specifically to handle multi-tenant isolation and client billing tracking. In-House Engineering Team Core Systems: Custom internal tools, cloud communication infrastructures, custom APIs. Primary Goals: Maintain complete programmatic control over code, models, APIs, and voice components. Recommended Choice: Retell AI or Vapi . These API-driven platforms offer outstanding developer experiences for teams building custom voice tools
