Voice & Call Center Software Buyer’s Guide for Customer Support

Who This Guide Is For

You need this guide if you are responsible for one of these problems:

1) VP / Director of Customer Support

You’re being asked to improve CSAT, AHT, FCR, and cost per contact at the same time, which usually means the business wants impossible efficiency gains without adding headcount. You need to know:

• What belongs in a true contact center platform vs. a lightweight phone system
• Where AI actually reduces work vs. where it’s mostly marketing
• How to avoid getting trapped in a multi-year contract for features your team won’t use

2) Contact Center Operations Leader / CX Director

Your day-to-day pain is operational, not strategic:

• Agents are spread across too many tools
• QA is manual and incomplete
• WFM is weak or missing
• Supervisors spend too much time listening to random calls and too little time coaching
• Routing is brittle and reporting doesn’t match reality

You need software that improves routing, coaching, analytics, and agent productivity without creating an admin nightmare.

3) IT / RevOps / Enterprise Applications Manager

You care about:

• Security and compliance
• CRM and ticketing integrations
• API quality
• Identity management
• Data retention
• Migration risk
• Telephony architecture

You need a vendor that can integrate with your stack cleanly, not another “platform” that requires a services team every time you change a workflow.

4) Procurement / Finance Leader

You are trying to compare:

• Per-user pricing vs. usage-based pricing
• Annual commitments vs. monthly flexibility
• AI add-ons vs. bundled suites
• Real implementation cost vs. sticker price

You need a practical framework for understanding total cost of ownership over 24 months, not a glossy demo.

Who should NOT read this guide

This guide is probably not for you if:

• You only need a single-line business phone system
• You have fewer than 5 agents and no plan to scale
• You are buying only a speech analytics add-on, not a call center platform
• You are looking for a consumer VoIP tool, not a customer support stack

If you are buying a basic phone solution, stop here. Most enterprise contact center software will be overkill and overpriced for you.

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What Voice & Call Center Software Does

At its core, voice and call center software is the system that sits between your customers and your agents. It handles:

• inbound and outbound calls
• routing and queue management
• IVR/self-service
• recording and quality assurance
• analytics and reporting
• agent assistance
• sometimes omnichannel support
• sometimes workforce management
• increasingly, AI voice agents and conversational automation

The market has split into three broad layers:

• Core CCaaS platforms — telephony, routing, omnichannel, reporting
• AI and optimization layers — real-time guidance, analytics, conversation intelligence, QA automation
• AI agent and voice automation layers — virtual agents, voice bots, and programmable AI workflows

Before vs. After Comparison

The business case in plain English

A 60-agent support team handling 120,000 calls/year can waste a shocking amount of money on inefficient routing and manual QA.

Example:

• 60 agents
• Average loaded cost per agent: $55,000/year
• Current average handle time: 7.5 minutes
• If better routing, agent assist, and knowledge surfacing cut AHT by 45 seconds
• That frees roughly 15,000 hours/year across the team
• Even if only half that converts to real capacity, that is about 7,500 hours
• At a loaded hourly cost of about $26/hour, that is nearly $195,000/year in avoided labor pressure

Now add:

• 100% call coverage for QA instead of 2%
• Fewer compliance misses
• Faster ramp for new agents
• Higher first-contact resolution
• Lower churn from poor service

A good deployment can produce six-figure annual value even before you account for the cost of bad customer experiences. A bad deployment just becomes another expensive dashboard nobody trusts.

The key idea

Do not buy voice software because it sounds modern. Buy it because it solves one of these three problems:

• Reduce live handling cost
• Improve service quality
• Increase automation without breaking customer experience

If it does not clearly do at least one of those, it is not core infrastructure. It is software theater.

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Market Landscape 2026

The 2026 voice and call center market is dominated by one fact: AI is now embedded everywhere, but the quality varies wildly.

Almost every vendor claims AI. That does not mean they do the same thing.

The market has five distinct segments

1) Core CCaaS platforms

These are the main contact center systems of record:

• Genesys Cloud CX
• Genesys Multicloud CX
• NICE CXone
• Five9
• Talkdesk CX Cloud
• Dialpad Support
• Zendesk Talk
• Freshdesk Contact Center
• LiveAgent Call Center Software
• Twilio Flex

These platforms usually handle:

• voice
• omnichannel routing
• queues
• IVR
• recording
• dashboards
• WFM/WEM in some cases
• APIs and integrations

2) AI agent guidance and real-time assistance

These tools sit alongside the core platform:

• Balto
• Real-Time QA by Balto
• Voice AI Agent by Ada
• AI Voice Agents by Voiceflow
• Voice Agent API by Speechmatics
• Parloa
• AI Voice Agent by aircall
• Five9 Genius AI
• Nuance Communications by Microsoft

These are focused on:

• agent assist
• live coaching
• real-time prompts
• voice automation
• generative support workflows

3) Conversation intelligence and speech analytics

These tools extract insights from calls:

• CallMiner Eureka
• CallMiner Record
• CallMiner Screen Record
• Medallia Conversation Intelligence
• AssemblyAI Conversation Intelligence
• Clara AI Call Analytics Platform
• U-Capture by Uniphore
• U-Trust by Uniphore
• Business AI Cloud by Uniphore
• QA Automation by Tethr

These are used for:

• transcript analysis
• sentiment
• compliance
• QA automation
• root cause analysis
• trend detection
• coaching

4) Workflow, telephony, and SMB-friendly calling tools

These tools often win on simplicity and speed:

• Aircall
• Freshdesk Contact Center
• Zendesk Talk
• LiveAgent
• Dialpad Support

These are attractive when the buyer wants:

• rapid deployment
• cleaner admin experience
• easier CRM sync
• lower implementation overhead

5) Programmable platforms and AI infrastructure

These are for teams that want to build:

• Twilio Flex
• Speechmatics Voice Agent API
• AssemblyAI
• Voiceflow
• Uniphore
• Parloa

These are not “plug and play” in the classic sense. They are better when you have:

• product engineering support
• custom workflows
• multi-step orchestration needs
• global or highly specific use cases

The big 2026 trend: AI is moving from “analysis” to “action”

Five years ago, AI in contact centers mostly meant:

• call transcription
• sentiment scores
• post-call summaries

Now it increasingly means:

• real-time agent guidance
• automatic case updates
• voice bot resolution
• compliance detection during live calls
• intent-based routing
• AI-generated next best action

That shift matters because post-call analytics only tells you what went wrong. Real-time AI can change the outcome while the customer is still on the line.

What is actually changing in buying behavior

In 2026, buyers are more skeptical about “AI platform” claims. They want proof of:

• accuracy
• latency
• workflow fit
• data controls
• measurable lift

If a vendor cannot show:

• reduced AHT
• improved containment
• better QA coverage
• higher conversion
• or lower average handle effort

then their AI is decorative.

How the vendor landscape is structured

Enterprise suite leaders

These vendors are usually best for organizations with:

• 100+ agents
• multiple queues
• global operations
• strict compliance
• deeper WEM needs
• complex integrations

Examples:

• Genesys Cloud CX
• NICE CXone
• Five9
• Talkdesk
• Twilio Flex for custom-built enterprises

Mid-market fast movers

These tend to balance power and deployability:

• Dialpad Support
• Aircall
• Zendesk Talk
• Freshdesk Contact Center
• LiveAgent

These are attractive when the buyer wants:

• faster implementation
• less admin overhead
• reasonably modern AI features
• lower complexity

AI overlay specialists

These tools often attach to a core platform:

• Balto
• CallMiner
• Medallia Conversation Intelligence
• AssemblyAI
• Tethr
• Clara AI
• Uniphore

Good when you already have CCaaS and need:

• better QA
• better insight
• better coaching
• better compliance

Voice bot and automation specialists

These are strongest where automation is the goal:

• Parloa
• Ada Voice AI Agent
• Voiceflow AI Voice Agents
• Speechmatics Voice Agent API
• Nuance Communications by Microsoft
• Five9 Genius AI
• AI Voice Agent by aircall

These matter most when deflection and self-service are strategic priorities.

The practical takeaway

Do not evaluate all vendors against the same checklist.

A buyer replacing a legacy phone system should optimize for:

• speed of deployment
• telephony reliability
• routing
• admin simplicity

A buyer layering AI onto an existing enterprise CCaaS should optimize for:

• integration depth
• analytics quality
• model transparency
• coaching impact

A buyer building custom voice workflows should optimize for:

• APIs
• orchestration
• developer controls
• latency
• governance

If you do not distinguish these buying modes, you will compare products that should never have been in the same shortlist.

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The 8 Things That Matter Most

Below is the evaluation framework I would use if I had to shortlist vendors in 30 minutes.

1) Routing and call handling depth — 18%

Why it’s critical: Routing is the heart of call center software. If routing is weak, everything else is decoration.

What to evaluate:

• skills-based routing
• intent-based routing
• queue logic
• overflow handling
• callback handling
• transfers and consults
• IVR flexibility
• business hours / holiday routing
• multilingual routing

What good looks like:

• Administrators can change routing rules without vendor intervention
• IVR logic is easy to test and version
• Calls reach the right queue on the first attempt
• You can route on customer metadata, not just menu selections

Red flag: The vendor demos routing with a toy example but cannot handle real-world exceptions like VIP tiers, SLA priority, language, fraud flags, or multi-brand support.

Ask the vendor:

• Can routing be changed by admins without professional services?
• Can you route based on CRM, order status, or customer type?
• How do you handle overflow and failover when a queue is overloaded?

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2) AI capability that actually changes outcomes — 15%

Why it’s critical: AI is now table stakes, but most “AI” is still after-the-fact reporting. You want AI that reduces effort during or before the interaction.

What to evaluate:

• real-time agent assist
• virtual agents / voice bots
• post-call summaries
• next-best-action suggestions
• sentiment and intent detection
• compliance prompts
• auto-dispositioning

What good looks like:

• AI performs at low latency
• Human agents trust suggestions
• The system shows measurable lift in AHT, containment, or conversion
• You can tune prompts, intents, and policies

Red flag: Vendors brag about generative AI but cannot prove accuracy, latency, or workflow adoption in production.

Ask the vendor:

• Which AI functions are real-time vs. post-call?
• What accuracy and latency do you guarantee?
• What measurable customer outcomes have you improved?

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3) Omnichannel and journey consistency — 12%

Why it’s critical: Customers do not think in channels. They think in problems. Your software should preserve context across voice, chat, email, and social.

What to evaluate:

• voice + digital channel support
• interaction history
• case continuity
• screen-pop across channels
• handoff quality
• unified reporting

What good looks like:

• One customer record across channels
• Agents see prior context instantly
• Customers do not repeat themselves
• Reporting covers all interactions consistently

Red flag: The vendor says “omnichannel” but voice and digital still live in separate admin systems and separate reporting models.

Ask the vendor:

• Is channel context unified or stitched together?
• Can a customer move from chat to voice without losing history?
• Are voice and digital analytics modeled together?

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4) Analytics, QA, and conversation intelligence — 15%

Why it’s critical: If you cannot measure what happens on calls, you cannot improve it. Manual QA alone is too slow and too biased.

What to evaluate:

• speech-to-text quality
• sentiment analysis
• topic clustering
• QA scorecards
• compliance detection
• call summarization
• trend identification

What good looks like:

• Coverage across all calls, not random samples
• Searchable transcripts with useful accuracy
• QA automates repetitive scoring
• Supervisors can coach based on data, not anecdotes

Red flag: The solution produces pretty dashboards but no actionable workflow for coaching, compliance, or root-cause remediation.

Ask the vendor:

• What percentage of interactions can be analyzed automatically?
• How do you validate transcript accuracy for domain-specific terms?
• Can insights be pushed into coaching workflows or only viewed in reports?

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5) Integrations and API quality — 12%

Why it’s critical: Call center software only works when it fits the rest of the stack: CRM, ticketing, identity, BI, workforce systems, and data pipelines.

What to evaluate:

• native integrations
• API completeness
• webhooks
• sandbox environments
• data sync latency
• CRM screen pops
• admin automation

What good looks like:

• Salesforce, Zendesk, ServiceNow, Dynamics, and other core tools integrate cleanly
• APIs are documented and stable
• Event data is usable for analytics
• You are not forced to rely on brittle middleware

Red flag: The vendor has “integrations” but only for shallow click-to-call or basic logging.

Ask the vendor:

• What can be done via API vs. UI only?
• How often do you break backward compatibility?
• Can we ingest raw interaction events into our warehouse?

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6) Security, compliance, and data controls — 12%

Why it’s critical: Voice data is sensitive. It often contains PII, payment data, health data, or regulated disclosures.

What to evaluate:

• SOC 2 / ISO certifications
• GDPR / HIPAA / PCI support
• call recording controls
• retention policies
• redaction
• role-based access
• data residency
• audit logs

What good looks like:

• Strong certification posture
• Fine-grained access controls
• Secure handling of recordings and transcripts
• Clear answer on where data is stored and processed

Red flag: The vendor is vague about sub-processors, retention, or how AI models use customer data.

Ask the vendor:

• Where is data stored and processed?
• How do you handle payment and health information in recordings?
• Can we enforce retention and deletion policies ourselves?

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7) Implementation speed and admin usability — 8%

Why it’s critical: A powerful platform that takes 9 months to launch is often a bad decision unless you have enterprise-grade pain and a real implementation team.

What to evaluate:

• time to first queue
• admin interface quality
• change management burden
• migration tooling
• training requirements
• dependency on professional services

What good looks like:

• Core setup in weeks, not quarters
• Admins can manage the system after go-live
• Low ongoing configuration burden
• Clear documentation and support

Red flag: Everything requires a professional services engagement, and the vendor cannot explain what your team can own after implementation.

Ask the vendor:

• What can we configure without your services team?
• What does a standard implementation timeline look like?
• How much internal admin effort should we expect after launch?

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8) Pricing transparency and total cost of ownership — 8%

Why it’s critical: Contact center pricing gets ugly fast: per-seat fees, AI usage, telephony minutes, add-on analytics, implementation, and support tiers.

What to evaluate:

• per-user vs. usage-based pricing
• AI feature pricing
• telephony and minute costs
• add-on modules
• professional services
• annual minimums
• contract flexibility

What good looks like:

• Clear price sheet
• Transparent add-on structure
• Predictable usage assumptions
• No hidden fee explosion in year 2

Red flag: The vendor will not give you a real quote until you commit to a discovery process, and the “starting price” is irrelevant to your deployment size.

Ask the vendor:

• What is included in base pricing?
• Which features are metered?
• What does a 24-month TCO usually look like for our size?

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Use-Case Matching: Which Type of Buyer Are You?

Different buyers should optimize for different things. This matters more than brand.

1) Growth-stage SaaS or ecommerce support team

Typical profile: 20–100 agents, fast growth, lean ops, heavy CRM dependency

What matters most:

• speed of deployment
• easy admin
• clean integrations
• call routing
• quality of reporting
• basic AI help without heavy services

Prioritize:

• Aircall
• Dialpad Support
• Zendesk Talk
• Freshdesk Contact Center
• LiveAgent

Why: These buyers usually do not need a heavyweight enterprise stack. They need something that is deployable, understandable, and integrates with Zendesk, Salesforce, HubSpot, or Intercom without months of work.

Avoid:

• Overbuying WEM features you won’t use
• Complex implementation-heavy enterprise suites
• Programmable platforms unless you have engineering support

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2) Enterprise customer support operation

Typical profile: 100–5,000+ agents, multiple regions, regulatory pressure, layered support tiers

What matters most:

• routing sophistication
• omnichannel consistency
• security/compliance
• WEM/QA capabilities
• global scalability
• integration depth

Prioritize:

• Genesys Cloud CX
• NICE CXone
• Five9
• Talkdesk CX Cloud
• Twilio Flex if customization is a strategic requirement

Why: Large operations need an actual platform, not just a phone layer. They typically need better governance, better analytics, and more control over routing and automations.

Avoid:

• Buying on AI marketing alone
• Choosing a tool that cannot support complex org structures
• Assuming basic telephony tools will scale cleanly to enterprise needs

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3) AI-forward contact center transformation buyer

Typical profile: already has a CCaaS platform, wants measurable AI lift in coaching, automation, or QA

What matters most:

• real-time guidance
• conversation intelligence
• AI accuracy
• workflow adoption
• compliance automation
• analytics granularity

Prioritize:

• Balto
• CallMiner Eureka / Record / Screen Record
• Medallia Conversation Intelligence
• AssemblyAI Conversation Intelligence
• Clara AI
• Tethr QA Automation
• U-Capture / U-Trust / Business AI Cloud
• Real-Time QA by Balto

Why: These buyers already have the telephony layer. Their pain is not “can customers call us?” It is “how do we improve every call without adding supervisors?”

Avoid:

• Replacing your whole stack when a focused AI layer would solve the problem
• Buying analytics without a coaching workflow
• Expecting transcript tools alone to change behavior

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4) Industry-specific or regulated buyer

Typical profile: healthcare, finance, public safety, insurance, or any compliance-heavy operation

What matters most:

• certifications
• retention policies
• auditability
• redaction
• role-based access
• specialized workflows
• voice data governance

Prioritize:

• NICE CXone
• Genesys Cloud CX
• Five9
• Dialpad Support in some regulated SMB/mid-market cases
• Nuance Communications by Microsoft
• U-Capture by Uniphore
• NICE Public Safety
• Parloa for regulated automation use cases

Why: If you are regulated, architecture and compliance matter as much as features.

Avoid:

• Tools with vague security posture
• Vendors that cannot explain where data lives
• AI agents that cannot be governed or audited

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Vendor Evaluation Questions

Use these in RFPs or vendor demos. They are written to expose gaps quickly.

Features

• Which channels are native vs. add-on?
• How do you handle IVR, routing, and callback management?
• Can admins modify call flows without vendor support?
• Do you support warm transfer, consult transfer, and conference calls?
• What are your agent assist capabilities in real time?
• How do you handle post-call summaries and dispositions?
• What percentage of interactions can be auto-analyzed?
• Do you support outbound dialing, predictive dialing, or campaign management?

Integration

• Which CRM and ticketing systems have native integrations?
• How deep is your Salesforce/Zendesk/ServiceNow integration?
• Can we access raw event data through API or webhooks?
• Do you support SSO, SCIM, and role-based provisioning?
• How do you integrate with data warehouses or BI tools?
• What limitations exist on API rate limits or exported objects?
• Can we build custom workflows with your API without professional services?

Security and compliance

• What certifications do you hold today?
• Where is customer data stored, processed, and backed up?
• How do you handle call recording retention and deletion?
• Can we redact payment or health information automatically?
• Do AI features train on our data by default, opt-in, or never?
• What audit logs are available to customers?
• Can we restrict access by region, business unit, or role?

Pricing

• What is included in base licensing?
• Which features are metered or usage-based?
• Are minutes, recordings, transcripts, or AI tokens extra?
• What are the implementation and onboarding fees?
• What annual minimums or commit levels apply?
• What happens to pricing after the first contract term?
• What professional services are required vs. optional?

Implementation

• What is the typical time to go live for a team our size?
• What internal resources do you need from us?
• What migration tools do you provide?
• What parts of the configuration can our team own after launch?
• How do you support change management and agent training?
• What does success look like in the first 90 days?

Support and operations

• What support tiers are included?
• What is your standard response time for critical issues?
• How do you handle platform incidents and status communications?
• What customer success resources are assigned?
• How often do you release product updates, and how are they communicated?

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Common Mistakes to Avoid

Mistake #1: Buying “AI” without defining the outcome

If you cannot say whether you want lower AHT, higher containment, better QA coverage, or higher conversion, you are not ready to buy AI.

Consequence: You end up paying for summaries and dashboards that do not change operations.

Do instead: Define one primary metric and one secondary metric before demoing anything.

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Mistake #2: Choosing a platform that is too large for your operating model

Enterprise platforms are powerful, but they are not free in complexity.

Consequence: Longer implementation, more admin burden, and low adoption.

Do instead: Match platform complexity to team maturity. A 30-agent support org should not buy like a 3,000-agent contact center.

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Mistake #3: Assuming omnichannel means unified

Some vendors mean “we sell voice and chat.” That is not the same as a shared customer context.

Consequence: Agents still repeat questions, and reporting remains fragmented.

Do instead: Ask to see a real omnichannel handoff live in the demo.

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Mistake #4: Ignoring implementation cost

Licensing is only part of the bill. Services, migration, training, and integration can easily add 20–50% in year one.

Consequence: Budget surprises and delayed ROI.

Do instead: Ask for a full 24-month cost model, not a seat price.

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Mistake #5: Overlooking data quality

AI is only as good as your routing, tagging, and transcript quality.

Consequence: Bad insights, bad coaching, and low trust.

Do instead: Budget 2–4 weeks for data cleanup if your current taxonomy is messy.

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Mistake #6: Underestimating change management

Agents resist tools that slow them down. Supervisors resist tools that make them accountable.

Consequence: Low adoption, workarounds, and shadow processes.

Do instead: Involve frontline managers and QA leads in vendor selection.

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Mistake #7: Buying analytics without operational follow-through

If insights do not feed coaching, policy changes, or automation, they go nowhere.

Consequence: Pretty dashboards, zero behavior change.

Do instead: Require a closed loop from insight to action.

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Mistake #8: Accepting vague security answers

“Industry standard security” is not an answer.

Consequence: Compliance and procurement delays, plus real risk if sensitive data is involved.

Do instead: Get the specifics in writing: certifications, data handling, retention, access, and AI training policies.

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Evaluation Timeline & Process

A disciplined selection process usually takes 6–8 weeks. Faster than that usually means you skipped something important.

Week-by-week evaluation plan

Weighted evaluation matrix template

\*Admin usability can be folded into implementation speed or treated separately if needed.

Scoring guidance

Use a 1–5 scale:

• 1 = poor fit
• 2 = weak
• 3 = acceptable
• 4 = strong
• 5 = excellent

Do not average scores blindly. If a vendor scores 1 or 2 on security or integration, that usually overrides a high total score.

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Implementation Reality Check

Implementation is where many buying decisions become painful.

Expected timeline by company size

Key success factors

• Clear ownership between CX, IT, and operations
• Clean call taxonomy and queue design
• Realistic rollout scope by channel, region, or team
• Integration testing before launch
• Agent training that focuses on the new workflow, not just the UI
• Supervisor buy-in for QA, coaching, and reporting changes

What vendors won’t tell you

1) Your first routing model will probably be wrong

That is normal. The first version usually over-simplifies edge cases.

2) Speech analytics takes tuning

Domain terms, accents, jargon, and abbreviations need calibration.

3) AI summaries are not always trustworthy on day one

They improve, but only after tuning and feedback loops.

4) The admin burden does not disappear

Someone still has to maintain queues, dispositions, permissions, and scorecards.

5) “Quick deployment” often means “minimal configuration”

That can be fine for SMBs, but it is often not enough for serious support operations.

Practical advice

• Budget 2–4 weeks for data cleanup
• Budget 1–2 weeks for agent and supervisor training
• Budget extra time for security and procurement reviews if you handle regulated data
• Pilot with real calls, not sandbox examples

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Red Flags — When to Walk Away

These are deal-breakers, not minor annoyances.

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How This Guide Was Created

This guide was created using a buyer-focused review process for the voice and call center subcategory, with emphasis on:

• platform capabilities
• use-case fit
• pricing models
• implementation complexity
• security posture
• vendor segmentation across SMB, mid-market, and enterprise needs

Review date: 2026-09-09 Update frequency: Quarterly, or sooner if major market changes affect platform positioning, pricing, or product capability. Independence disclaimer: This guide is editorially independent and intended to help buyers compare categories and vendors objectively. It is not sponsored by any listed product vendor.

Voice & Call Center solutions in the catalogue (40)

• AI Voice Agent by aircall — Automate calls, boost efficiency, and free your team for meaningful conversations.
• AI Voice Agents by Voiceflow — Design and deploy human-like AI voice agents for automated customer interactions.
• Aircall by aircall — The customer communication and intelligence platform.
• Balto by Balto — Real-time AI guidance for perfect customer conversations.
• Business AI Cloud by Uniphore — A Sovereign, Composable, and Secure AI platform for business.
• Call Center Software by LiveAgent — Virtual call center software with IVR, call routing, and unlimited recordings for efficient phone support.
• CallMiner Eureka by CallMiner — Analyze omnichannel customer interactions to improve CX, agent performance, and business efficiency.
• CallMiner Record by CallMiner — A full-featured recorder for live monitoring, instant playback, and enhanced analytics.
• CallMiner Screen Record by CallMiner — Gain robust insight into agent effectiveness with screen recordings of customer interactions.
• Clara AI Call Analytics Platform by Clara AI — AI-powered call analytics for customer insights and system integration.
• Conversation Intelligence by AssemblyAI — Power conversation intelligence tools with accurate, real-time speech-to-text and AI insights.
• Cresta by Cresta — Generative AI for real-time agent coaching, automated QA, and supervisor tooling.
• Dialpad AI by Dialpad — The AI-powered Customer Intelligence Platform for unified communications.
• Dialpad Support by Dialpad — AI-powered contact center solution for enhanced customer service and agent productivity.
• Five9 by five9 — Anticipate needs, empower agents, and build trust that drives lasting connections.
• Five9 Genius AI by five9 — Elevate CX with AI Agents, Agent Assist, and intelligent automation.
• Five9 Virtual Contact Center (VCC) by five9 — Deliver exceptional customer experiences with a comprehensive, scalable cloud contact center.
• Freshdesk Contact Center by Freshworks — Cloud-based modern phone system for efficient customer call management across teams of all sizes.
• Genesys Cloud CX by Genesys — AI-Powered Experience Orchestration: Unifying Customer and Employee Journeys at Scale.
• Genesys Multicloud CX by Genesys — Multicloud contact center software for enterprise customer experience.
• Medallia Conversation Intelligence by Medallia — Uncover insights from every customer conversation to improve service.
• NICE CXone by NICE — The AI-first cloud customer experience platform for contact centers.
• NICE Public Safety by NICE — Enhancing public safety and justice operations with intelligent solutions.
• Nuance Communications by Microsoft — Intelligent, AI-powered customer engagement in every channel.
• Parloa by Parloa — Conversational AI for contact centers, scaling voice and chat automation.
• PolyAI by PolyAI — Enterprise voice AI agents handling inbound calls in 45 languages.
• QA Automation by Tethr — Automate conversation scoring, reduce manual QA, and gain a complete view of agent performance.
• Real-Time QA by Balto — Automates real-time quality assurance and coaching for 100% of customer calls.
• Sprinklr Service by Sprinklr — Reimagine every support conversation with AI that understands and unifies CX.
• Talkdesk by Talkdesk AI — AI-powered cloud contact center for seamless customer experiences.
• Talkdesk CX Cloud by Talkdesk AI — AI-powered cloud contact center platform for seamless omnichannel customer service.
• Talkdesk Express by Talkdesk AI — AI-powered customer service for small businesses, designed for ultra-fast setup.
• Talkdesk Identity by Talkdesk AI — AI-powered identity verification for secure contact centers.
• Twilio Flex by Twilio — A fully programmable cloud contact center platform for limitless customization.
• U-Capture by Uniphore — Capture, control, and access high-quality, AI-ready voice and screen data from every conversation.
• U-Trust by Uniphore — Secure and seamless customer verification with voice biometrics and authentication.
• Vapi by Vapi — Developer-first voice AI infrastructure for production voice agents.
• Voice Agent API by Speechmatics — Build responsive AI voice agents with sub-second, speaker-aware STT/TTS across 55+ languages.
• Voice AI Agent by Ada — Autonomously resolve customer calls with AI, reducing costs and boosting CSAT.
• Zendesk Talk by zendesk — Integrated voice and call center solution for seamless customer support.