Single Layer, Complete Control
Instead of stitching together multiple single-point solutions, Polished.CX operates as one unified live voice layer in your audio pipeline.
Compare Polished.CX
The Market Leader in Live Voice AI.
See how Polished.CX compares against Krisp and Sanas across real-time noise cancellation, accent conversion, translation, latency, and voice preservation.
Head-to-head evaluation
Why enterprise CX teams choose Polished.CX.
While single-feature tools focus only on noise or static accent masking, Polished.CX delivers an all-in-one live voice AI suite that preserves natural speaker identity.
| Features / Capabilities / Nuances | Market LeaderPolished.CX | Krisp | Sanas |
|---|---|---|---|
| 1. Core Architecture & Performance | |||
| Real-time voice-processing architectureDefines how each platform assembles its real-time speech layer - one unified path versus multiple specialized modules, SDKs, or applications. This directly affects integration complexity, orchestration effort, and cumulative latency. | ✓ Unified | Modular | Componentized |
| Unified / no-stitching pipelineChecks whether noise removal, accent transformation, translation, and voice preservation are explicitly handled inside one coordinated real-time pipeline instead of being stitched together as separate processing stages. | ✓ | ✕ Modular | ✕ Modular |
| Overall live-call latencyCompares published whole-call or end-to-end latency targets/typical figures. These numbers should be separated from model-only, frame-buffer, or algorithmic latency benchmarks. | <25 ms <125 ms for accent conversion <750 ms for translation |
? (unknown) approximately < 300 ms accent approximately < 2500 ms |
? (unknown) approximately < 300 ms accent approximately < 3500 ms |
| CPU execution without GPUEvaluates whether core workloads can run efficiently on standard CPU hardware without a dedicated GPU, which affects endpoint compatibility, deployment cost, and enterprise scalability. | ✓ CPU | ✓ CPU | ✓ CPU |
| GPU ExecutionWhether the platform can leverage GPU acceleration for higher throughput on heavier workloads, in addition to running on CPU-only hardware. | ✓ | ✕ | ✕ |
| Processing-location modelShows where audio processing occurs - in-flight, on-device, cloud, or hybrid - and whether different workloads such as translation use a different processing location from noise/accent functions. | In-flight / zero-retention | Local + cloud translation | Local + server-side |
| Audio ingestion / routingCompares the documented ways live audio can enter the platform, including browser, SDK, SIP/WebRTC, mono/stereo, desktop, server, and carrier/VoIP paths. | Mono / stereo / browser / SIP | WebRTC / SIP | Desktop / server / VoIP |
| Published processing scaleCaptures publicly stated production processing volume as an indicator of operational maturity and real-world scale. | - | Conflicting published figures | - |
| Latency benchmarking nuanceExplains how to interpret vendor latency claims so frame processing, algorithmic buffer latency, per-feature pipeline latency, and end-to-end call latency are not treated as equivalent. This is an interpretation row: algorithmic/frame latency, pipeline latency, and end-to-end latency are different measurements. Polished.CX's internal <25 ms figure is explicitly unverified in the research. | <25 ms | 15 ms | <300 ms accent / ~3000 ms translation |
| Adjustable Intensity & Custom PresetsTune how strongly each mode is applied and save reusable presets per team or campaign. | ✓ Per-mode intensity + saved presets | Basic / On/off | ✕ Fixed |
| Concurrent Streams & ScalabilitySimultaneous live streams supported for enterprise call volume. | Unlimited* Elastic, Enterprise-scale (*by plan) |
Limited | Limited |
| 2. Noise Cancellation & Speech Enhancement | |||
| Real-time background noise cancellationCompares real-time removal of environmental noise such as keyboards, HVAC, traffic, pets, and other non-speech distractions while preserving the primary speaker. | Bidirectional | ✓ | ✓ |
| Bidirectional noise suppressionChecks whether noise suppression is documented on both directions of a conversation - the local speaker/microphone side and the incoming/listener side. | ✓ | ✓ | ✓ |
| Background human-voice suppressionSeparates ordinary noise cancellation from the harder task of removing competing human voices, nearby agents, or background conversations without damaging the primary speaker. | ✓ | ✓ | ✓ |
| Acoustic echo cancellation / room reverb removalCompares explicit acoustic echo cancellation (AEC), room-reflection removal, and reverberation cleanup - capabilities distinct from general background-noise suppression. | ✓ | ✓ | ? (unknown) |
| Noise-processing latencyCompares published speed for noise/voice-isolation processing, retaining each vendor's original measurement context rather than forcing unlike benchmarks into one metric. | <50 ms | 15 ms | ? (unknown) |
| ASR pre-processing / WER improvementEvaluates whether speech enhancement is positioned as an upstream ASR pre-processor and whether the research provides a measurable Word Error Rate (WER) improvement in noisy conditions. | ✓ approximately 73% reduction | ✓ Pre-processor | ✓ ~40% WER reduction |
| Carrier-grade audio reconstructionChecks for network-level reconstruction/upscaling of degraded telecom audio. This is more than muting noise: it aims to restore low-quality voice signals across carrier or VoIP networks. | ✓ UHD | ✓ HD | ✓ AI HD |
| Multi-Speaker HandlingSeparates and processes multiple speakers sharing a single line. | ✓ Speaker-aware processing | ✕ | ✕ |
| 3. Accent Conversion & Voice Preservation | |||
| Real-time accent conversion / localizationConfirms the core ability to modify or localize an accent during a live conversation while keeping the interaction natural and intelligible. | ✓ Accent Localization | ✓ Accent Conversion | ✓ Accent Translation |
| Accent-conversion latencyCompares the published processing delay for accent transformation, a critical metric because added delay can quickly make live conversation feel unnatural. | <125 ms | <300 ms | <300 ms |
| Bidirectional accent conversionChecks whether accent conversion is explicitly supported for both sides of the call - speaker/agent output and listener/customer input - rather than only one direction. | ✓ Bidirectional / Speaker + listener | ✓ Speaker + listener | ? (unknown) |
| Source-accent coverageCompares the documented range of input accents the model can recognize and transform, including whether support is presented as global/universal or as a defined list of regional accents. | Global / Broad / 63 accents / languages | ? (unknown) | India / Philippines / Africa / Middle East |
| Target/output accent coverageCompares the documented target accents or localization outputs, such as neutralized speech, US/UK English, or dynamically matched listener accents. | Neutralized / Localized / 63 regions | US / UK | US / UK |
| Speaker identity preservation during accent conversionEvaluates whether accent conversion preserves the speaker's biometric identity, vocal fingerprint, pitch, rhythm, and recognizable personal voice instead of replacing it with a generic synthetic speaker. | ✓ Speaker’s voice | ✓ Pitch / intonation | ? (unknown) |
| Emotional prosody / warmth preservationCompares preservation of emotional inflection, warmth, cadence, pitch contour, and prosody so transformed speech still carries the speaker's intent and human character. | ✓ | ✓ | ✓ |
| Wireless-headset latency caveatCaptures any documented hardware caveats that can compound real-time latency, especially Bluetooth/wireless headset delay during computationally sensitive accent conversion. | None | ? (unknown) | ? (unknown) |
| 4. Real-Time Language Translation | |||
| Speech-to-speech translationConfirms real-time spoken-language translation that outputs speech in another language, rather than stopping at text transcription or text-only translation. | ✓ Realtime SDK/API | ✓ SDK/API | ✓ |
| Supported translation languagesCompares the breadth of documented real-time translation coverage. Counts are kept as reported because vendor snapshots use slightly different language/dialect definitions and version dates. | 84 | 61 | 25 |
| Any-to-any language translationChecks whether any supported source language can translate directly to any supported target language, rather than operating through a limited set of fixed language pairs. | ✓ | ✓ | - Not public |
| Translation latencyCompares published end-to-end or pipeline delay for cross-language speech translation, which typically requires more buffering and semantic context than noise or accent processing. | <750 ms | <2000 ms | <3000 ms |
| Own-voice mimicry in translationEvaluates whether translated speech is rendered in a voice resembling the original speaker instead of a generic text-to-speech voice, preserving continuity and authenticity across languages. | ✓ Speaker’s own voice | ✕ | ✕ |
| Tone / pitch / intent preservation in translationCompares how well translation preserves prosody, pitch, tone, and communicative intent - the qualities most likely to make translated speech sound natural rather than robotic. | ✓ | Limited | Limited |
| Custom vocabulary / jargon dictionaryChecks for administrator/developer controls that force preferred translations for brand names, product terms, acronyms, medical terminology, and other domain-specific jargon. | ✓ | ✕ | - Not found |
| Translation processing dependencyShows whether translation runs inside the same real-time pipeline as other voice functions or depends on a separate cloud/service layer, which affects privacy, architecture, and integration. | Unified pipeline | Cloud | ? (unknown) |
| 5. Developer APIs, SDKs & Voice-Agent Tooling | |||
| Streaming API / SDK availabilityCompares whether developers can integrate the vendor as a programmable streaming voice layer through APIs/SDKs rather than relying only on a packaged desktop application. | ✓ Extensive | ✓ | ✓ API only |
| WebSocket streamingChecks for explicit WebSocket support for continuous low-latency audio streaming, a common integration pattern for real-time voice applications and AI agents. | ✓ | ✓ | ? (unknown) |
| Developer language / platform breadthCompares the breadth of documented developer bindings and runtime targets, such as C++, Python, JavaScript/WASM, iOS, Android, or server-side environments. | C++, Python, JavaScript/WASM, iOS, Android, APIs, Rust, Go | C++ / Python / JS / iOS / Android | API only |
| Human-to-human SDK specializationEvaluates whether the vendor has a developer product explicitly specialized for human-to-human calls, including noise, background-voice, echo, and accent processing. | ✓ Unified platform | ✓ SDK | ? (unknown) |
| Human-to-AI / voice-agent SDK specializationEvaluates whether the developer stack is explicitly designed for voice agents and human-to-AI interaction, rather than only human-to-human call enhancement. | ✓ | ✓ | ? (unknown) |
| Turn predictionChecks for an audio model that predicts when a speaker has finished a turn, helping voice agents respond naturally without waiting too long or interrupting early. | ✓ | ✓ | ? (unknown) |
| Interruption / barge-in classificationChecks for tooling that distinguishes a real user interruption/barge-in from backchannels such as "uh-huh" or "right," improving voice-agent conversational control. | - | ✓ <200 ms | - |
| Fine-grained frame / sample-rate controlsCompares low-level developer control over frame duration, audio sample rate, and model/runtime parameters - important for engineers optimizing latency and quality. | ✓ 8 ms | ✓ | ? (unknown) |
| Drop-in ASR enhancement SDKChecks whether the platform can be inserted directly ahead of an ASR/voice-agent pipeline as a drop-in enhancement layer without re-architecting the telephony stack. | ✓ Unified pipeline | ✓ Pre-processor | ✓ |
| 6. Enterprise Deployment & Integrations | |||
| Desktop application (Mac/Windows)Compares availability of a conventional managed desktop client for agent or employee deployment on Mac/Windows endpoints. | ✓ | ✓ | ✓ |
| VDI / thin-client supportChecks documented support for virtualized enterprise environments such as Citrix, VMware Horizon, AWS WorkSpaces, and thin-client operating systems used heavily in BPOs. | ✓ | ✓ | ? (unknown) |
| Browser extension / CCaaS bridgeChecks for a browser-based bridge/extension that synchronizes with web CCaaS applications and applies voice processing without deep native integration. | ✓ | ✓ | ? (unknown) |
| Browser SDKChecks for a dedicated in-browser developer SDK, such as JavaScript/WASM, for embedding voice processing directly into web applications. | ✓ | ✓ | ? (unknown) |
| SIP / WebRTCCompares native or SDK-level support for SIP and WebRTC, two core real-time communications protocols used to embed voice processing into telephony and browser calling stacks. | ✓ | ✓ | ? (unknown) |
| Carrier / VoIP network embeddingChecks for infrastructure-level embedding directly into carrier, VoIP, switch, or SIP-trunk environments rather than requiring endpoint software on every agent device. | ✓ Limited | ? (unknown) | ✓ Limited |
| Centralized enterprise portalCompares availability of a centralized admin portal for enterprise deployment, configuration, model management, and large-scale operational control. | ✓ | ✓ | ✓ |
| Uptime & Reliability SLAContractual availability guarantees for production workloads. | 99.9% / Custom enterprise SLA | ? (unknown) | ? (unknown) |
| 7. Enterprise Administration | |||
| SSOChecks support for enterprise Single Sign-On (SSO) and documented identity-provider integrations used to centralize secure access. | ✓ | ✓ Entra | ✓ Okta / Google / Entra |
| SCIM provisioningChecks automated enterprise user provisioning/deprovisioning, including explicit SCIM support where documented. | ✓ | ✓ | ✓ |
| Centralized user managementCompares organization-level controls for managing users, teams, permissions, and enterprise deployments from a central administrative layer. | ✓ | ✓ | ✓ |
| Audit logsChecks for organization-level audit logging that helps enterprise IT and security teams trace administrative activity and changes. | ✓ | ✓ | ? (unknown) |
| Team-level webhooksChecks for team/organization webhooks that allow enterprise systems to receive events and automate downstream workflows. | ✓ | ✓ | ? (unknown) |
| Remote model updatesChecks whether administrators can centrally push or manage AI model updates across deployed endpoints without manual per-device intervention. | ✓ | ? (unknown) | ✓ |
| 8. Security, Privacy & Compliance | |||
| SOC 2Compares whether SOC 2 is publicly verified in the supplied research. | ✓ Type II ★Observation period | ✓ | ✓ |
| HIPAACompares whether HIPAA is publicly verified in the supplied research. | ✓ | ✓ | ✓ |
| GDPRCompares whether GDPR is publicly verified in the supplied research. | ✓ | ✓ | ✓ |
| ISO 27001Compares whether ISO 27001 is publicly verified in the supplied research. | ✓ Observation period | ? (unknown) | ✓ |
| PCI DSSCompares whether PCI DSS is publicly verified in the supplied research. | ✓ | ? (unknown) | ✓ |
| Audio retentionCompares how raw/processed audio is handled and retained, including in-flight zero-retention, on-device processing, and any cloud-streaming exceptions for specific workloads. | In-flight: Zero / On-device: Zero | On-device: Retained / Cloud: Retained | On-device: Zero / Cloud: ? (unknown) |
| Voice-fingerprint consent controlsEvaluates governance around biometric voice fingerprints - explicit opt-in, encryption, scoping, revocability, and whether an equivalent user-control mechanism is documented. | ✓ Opt-in / Encrypted / Revocable | ✕ | ✕ |
| Zero-knowledge / local privacy modelCompares privacy architecture for keeping sensitive voice data local or minimizing server knowledge, while distinguishing true on-device/zero-knowledge claims from in-flight private processing. | Private-by-design / In-flight | Local core models / Translation in the cloud | Zero-knowledge local / Translation in the cloud |
| 9. Commercial Model, Use Cases & Strategic Positioning | |||
| Pricing transparencyCompares how much public pricing information is available and whether the commercial motion is self-serve/per-user versus custom enterprise quoting. | ✓ Full Transparent / Public tiers + Enterprise Transparency | ✓ Public tiers + Enterprise quote | ✕ Custom quote |
| Primary contact-center focusCompares how central enterprise contact centers are to each vendor's product strategy versus adjacent markets such as meetings, remote work, developer infrastructure, healthcare, or telecom. | ✓ + broader enterprise | ✓ + broader markets | ✓ Core focus |
| BPO / offshore talent use caseEvaluates fit for global BPO and offshore-agent environments, where accent intelligibility, noise, language breadth, VDI compatibility, and rapid rollout directly affect operations. | ✓ Strong fit | ✓ Strong fit | ✓ Strong fit |
| Healthcare positioningCompares explicit healthcare positioning and the strength of supporting privacy/compliance evidence for regulated deployments. | ✓ HIPAA | ✓ HIPAA | ✓ HITRUST-led |
| EdTech positioningCompares whether education/EdTech is an explicit go-to-market use case rather than an incidental application of the underlying voice technology. | ✓ Strong fit | ✕ | ✕ |
| Remote worker / hybrid meeting marketCompares strategic emphasis on individual remote workers, hybrid teams, and general meeting productivity beyond contact-center use cases. | ✓ Strong fit for Content Creators | ✓ | ✕ Secondary |
| AI-agent developer marketCompares how strongly each vendor targets engineers building conversational AI agents, including SDK maturity, voice-agent primitives, integrations, and public documentation depth. | ✓ Strong fit | ✓ | ? (unknown) |
| Mobile consumer translationChecks for a consumer/mobile product specifically designed for travel, live conversation, listening, or video-call translation outside the enterprise contact-center stack. | ✓ iOS & Android | ✕ | ✓ |
| Distinctive strategic moatSummarizes the most defensible strategic differentiation identified across both research files - the capability cluster each vendor appears strongest at defending in enterprise evaluation. | Unified + 84 languages + Speaker’s voice + Content Creators + Developers | Meetings + VDI + Developer | CCaaS |
Human Identity Comes First
Other accent tools output synthetic, robotic speech that alienates customers. Polished.CX preserves the speaker's true human identity.
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