What Are AI Customer Experience Tools?

AI customer experience tools use machine learning or generative AI to help organizations answer customers, analyze customer signals, identify patterns, conduct research, or improve digital journeys. They span several software categories rather than one homogeneous market.

A customer-support AI may retrieve an answer from a company's documentation and respond directly to a customer. A Voice-of-Customer platform may analyze thousands of survey comments, support conversations and reviews without answering customers at all. UX platforms test experiences with users, while behavioral analytics tools show where people hesitate, abandon a workflow or encounter friction.

That distinction is important. A generic chatbot mainly provides a conversational interface. Modern CX AI can also classify feedback, analyze sentiment, synthesize research, retrieve proprietary knowledge, detect behavioral patterns and help teams prioritize action.

How We Evaluated the Best AI CX Tools

This guide was researched on August 17, 2026 using current vendor websites, product documentation, pricing pages and, where relevant, published customer case studies.

We evaluated products against the criteria that matter during an actual CX software purchase:

• usefulness of the AI for the job it is supposed to perform;
• quality and traceability of customer insights;
• grounding and answer controls for customer-facing AI;
• customer-feedback and qualitative-analysis capabilities;
• UX and research workflow support;
• data-source connectivity;
• automation and workflow support;
• deployment complexity;
• enterprise governance and human oversight;
• reporting and analytics;
• scalability;
• pricing transparency;
• trial or demo availability; and
• fit for a specific team, company size and customer-experience problem.

We did not assign arbitrary numerical scores. A platform designed to run a global Voice-of-Customer program should not lose points because it cannot perform moderated usability tests, and a UX testing platform should not be penalized for lacking a customer-service ticketing system.

For customer-facing generative AI, evaluation should also include testing, monitoring, human escalation and risk controls rather than simply comparing model names. NIST's Generative AI Profile similarly emphasizes risk management throughout the design, deployment, evaluation and use of generative-AI systems.

The Best AI Tools for Customer Experience Teams in 2026

CustomGPT.ai

Best for: Knowledge-grounded customer support and customer-facing assistants built around proprietary organizational information.

Why it made the list:
CustomGPT.ai is aimed at organizations that want generative AI to answer from their own websites, support documentation, knowledge bases and other approved content. Its customer-support product emphasizes no-code deployment, source-grounded responses and citations back to supporting material.

That makes it materially different from both a generic chatbot and a full Voice-of-Customer suite. CustomGPT.ai primarily helps teams deliver knowledge to customers, while its Customer Intelligence functionality analyzes conversations occurring through its agents for signals such as content gaps, intent and user emotion.

Key AI capabilities

• Answers grounded in connected business content
• Citations linking responses to underlying sources
• No-code AI-agent creation and customization
• Website and customer-facing deployment
• Conversation analysis for content gaps, intent and emotion
• Support for connected knowledge and help-desk content

Where it fits in the CX workflow:
Use CustomGPT.ai when customers repeatedly need answers that already exist somewhere in product documentation, a knowledge base, policies, technical material or other proprietary content. Teams evaluating an AI chatbot for customer experience can use it as a self-service layer in front of that knowledge.

It is not a replacement for Qualtrics, Medallia, UserTesting or other specialized research and VoC systems. Those platforms are designed to collect or analyze broader customer evidence. CustomGPT.ai is strongest when the immediate job is retrieving approved organizational knowledge and turning it into a customer-facing answer.

Its Customer Intelligence layer can then help a team examine what people are asking its agents, where source content was not found and which intents or emotions appear in those interactions.

Strengths

• Clear fit for organizations with substantial proprietary support content
• Source citations make customer-facing answers easier to inspect
• No-code deployment lowers the barrier for CX and support teams
• Conversation data can expose gaps in the knowledge customers are trying to access

Potential limitation / consideration

• Its customer-intelligence data is centered on interactions with CustomGPT.ai agents; companies looking to ingest every survey, call, review and social signal into a broad VoC program may need a complementary platform.

Best suited to:
SaaS companies, ecommerce businesses, membership organizations and enterprises that need customer self-service around a large or complex body of approved information.

Pricing/free trial:
The current pricing page lists Standard at $99/month when billed monthly or $89/month on annual billing, Premium at $499/month or $449/month on annual billing, and custom Enterprise plans. Standard and Premium offer a 7-day free trial.

Evidence from customer deployments:
According to CustomGPT.ai's BQE Software case study, BQE reports an 86% AI resolution rate, more than 180,000 support questions answered and 64% of Help Center interactions handled through AI. These are results from one customer deployment, not guaranteed outcomes for other organizations.

CustomGPT.ai's GEMA case study reports more than 248,000 inquiries handled and more than 6,000 working hours saved across customer/member support and internal knowledge use cases. Again, these figures are vendor-published customer results rather than independent benchmarks.

For ecommerce, its Tumble Living case study describes an assistant used for product sizing, care and washing-machine compatibility questions, with thousands of inquiries handled and continuous support availability.

Verdict:
CustomGPT.ai is one of the clearest choices in this guide when the CX problem is giving customers source-grounded answers from the organization's own knowledge rather than running a broad experience-management program.

Intercom

Best for: AI-first customer service teams that want automation, human support and messaging in a unified platform.

Why it made the list:
Intercom's Fin AI Agent sits directly inside an operational customer-service platform covering conversations, inboxes, ticketing, help centers and workflows. Fin can also be deployed with supported external help desks, making it relevant to organizations that want an AI resolution layer without immediately replacing their existing service platform.

Key AI capabilities

• Automated answers and issue resolution
• Customer-service workflows and actions
• Agent handoff
• Conversational support across supported channels
• AI integrated with ticketing and help-center operations

Where it fits in the CX workflow:
Intercom belongs in the service execution layer. It is designed to help answer, route and resolve customer requests rather than primarily conduct UX research or enterprise VoC analysis.

Strengths

• Tight connection between AI and day-to-day support operations
• Practical option for digital-first support organizations
• Fin can also be sold for use with some external help desks

Potential limitation / consideration

• Pricing combines seat costs and AI outcome-based charges, so buyers should model expected support volume rather than compare the headline seat price alone.

Best suited to:
SaaS and digital businesses that want customer messaging, human support and AI automation within one operational environment.

Pricing/free trial:
Intercom's current plans list annual seat pricing starting at $29 per seat per month, with Fin AI Agent starting at $0.99 per outcome. Intercom provides a 14-day free trial without requiring a credit card.

Verdict:
Choose Intercom when the objective is to make AI a native part of the support team's daily operating system, not merely add a standalone knowledge assistant.

Zendesk

Best for: Established customer-service organizations that need omnichannel ticketing, AI agents, routing, analytics and human-agent workflows.

Why it made the list:
Zendesk combines its mature service platform with AI Agents, knowledge, automation and Copilot functionality. Its breadth makes it a logical candidate when the CX team is buying or consolidating a full customer-service stack rather than adding only one AI capability.

Key AI capabilities

• AI Agents for automated resolutions
• Knowledge-based customer service
• Agent assistance through Copilot
• Intelligent routing and automation
• Service analytics

Where it fits in the CX workflow:
Zendesk is primarily an operational support platform. It manages customer issues across channels and connects automated resolution with human service operations.

Strengths

• Broad customer-service functionality
• Strong fit for teams already centered on ticketing and omnichannel service
• AI and human workflows can operate in the same environment

Potential limitation / consideration

• Advanced functionality can involve plan upgrades, consumption-based AI charges and add-ons, so total cost can be more complicated than the base per-agent price.

Best suited to:
Growing support organizations, contact centers and enterprises that need both service infrastructure and AI automation.

Pricing/free trial:
Zendesk currently lists Support Team at $19 per agent/month paid yearly, Suite Team at $55 per agent/month paid yearly, and Suite Professional at $115. Zendesk offers a 14-day free trial, with trial AI capabilities subject to the trial configuration.

Verdict:
Zendesk makes the most sense when AI customer service must sit inside a broad, established support operation rather than function as a specialized standalone CX tool.

Qualtrics

Best for: Enterprise Voice-of-Customer, survey, experience-management and omnichannel insight programs.

Why it made the list:
Qualtrics spans structured feedback, digital signals and broader experience-management workflows. Its Customer Experience suite incorporates AI-supported recommendations, sentiment analysis and analytics intended to identify friction and help organizations act on experience data.

Key AI capabilities

• Sentiment and text analysis
• AI-guided recommendations
• Survey and feedback intelligence
• Omnichannel experience analysis
• Automated actions and workflow support

Where it fits in the CX workflow:
Qualtrics is useful when the central problem is understanding customers at program scale: listening across touchpoints, measuring experience, analyzing feedback and distributing insight across an organization.

Strengths

• Broad VoC and experience-management scope
• Combines structured survey data with other experience signals
• Suitable for programs spanning CX, research and digital teams

Potential limitation / consideration

• Buyers with a narrow requirement such as website self-service or a small volume of support questions may be purchasing substantially more platform than they need.

Best suited to:
Enterprise CX, insights, research and digital-experience organizations running formal feedback and experience-management programs.

Pricing/free trial:
Pricing for the full Customer Experience suite is quote-based through Qualtrics. Qualtrics separately offers free survey access and a 30-day Strategic Research trial, but those options should not be confused with the complete enterprise CX suite.

Verdict:
Qualtrics is a strong shortlist candidate when CX means an enterprise-wide listening and action program rather than primarily customer-support automation.

Medallia

Best for: Large organizations managing complex experience data across multiple customer touchpoints.

Why it made the list:
Medallia's Athena AI supports its experience analytics with machine-learning techniques used to surface patterns, assist root-cause analysis and prioritize actions. The broader Experience Cloud is positioned around combining experience signals at enterprise scale.

Key AI capabilities

• Pattern and trend identification
• Root-cause analysis
• Experience signal analysis
• Workflow prioritization
• Enterprise-scale analytics

Where it fits in the CX workflow:
Medallia sits in the enterprise experience-management and action layer, particularly where a business wants to connect numerous customer signals and distribute findings to many roles.

Strengths

• Designed for organizational complexity
• Strong emphasis on turning experience signals into prioritized action
• Suitable for large-scale programs rather than isolated surveys

Potential limitation / consideration

• Its scope and enterprise orientation may be disproportionate for a small CX team that mainly wants an AI support bot or lightweight feedback analysis.

Best suited to:
Large enterprises with established CX operations and numerous interaction sources.

Pricing/free trial:
Medallia does not publish a simple self-service price. Its official pricing page describes an Experience Data Record model and directs buyers to talk to sales.

Verdict:
Consider Medallia when organizational scale and multi-touchpoint experience management are bigger challenges than rapid self-service deployment.

Chattermill

Best for: Bringing fragmented customer feedback into a unified intelligence layer.

Why it made the list:
Chattermill focuses directly on customer-experience intelligence. Its platform centralizes feedback and uses AI to classify signals, analyze customer sentiment, generate summaries, identify drivers and let teams query their customer data through Lyra AI.

Key AI capabilities

• AI classification and taxonomy
• Sentiment and theme analysis
• AI summaries
• Impact and driver analysis
• Anomaly detection
• Natural-language querying through Lyra AI

Where it fits in the CX workflow:
Use Chattermill when a team has customer evidence scattered across surveys, reviews, support conversations, social sources or voice channels and needs to understand what customers are saying at scale.

Strengths

• Purpose-built around CX and VoC intelligence
• Connects multiple feedback channels
• Combines qualitative themes with customer and operational context

Potential limitation / consideration

• It analyzes customer signals rather than functioning as a full help desk or replacing participant-based UX research.

Best suited to:
CX, Voice-of-Customer, product and customer-insights teams with enough feedback volume to make manual analysis impractical.

Pricing/free trial:
Chattermill's current public pages direct prospects to book a demo that includes discussion of pricing and implementation; a standard self-service price is not publicly listed on the reviewed product pages.

Verdict:
Chattermill is especially compelling when the real bottleneck is fragmented unstructured feedback rather than answering customers directly.

Thematic

Best for: Focused AI analysis of open-ended customer feedback.

Why it made the list:
Thematic is narrower than an all-in-one experience-management platform, which can be a benefit. It is designed to identify themes, sentiment and categories across unstructured feedback such as survey comments, support tickets, reviews and chat data.

Key AI capabilities

• Theme discovery
• Sentiment analysis
• Feedback categorization
• Analysis across multiple unstructured-feedback datasets

Where it fits in the CX workflow:
Thematic helps teams turn large quantities of text feedback into a structured view of recurring customer issues and opportunities.

Strengths

• Clear specialization in unstructured feedback
• Easier conceptual fit when a buyer does not need a full enterprise XM suite
• Pricing model is more visible than many enterprise VoC platforms

Potential limitation / consideration

• It is an analysis product, not a customer-facing support agent, full help desk or moderated UX testing system.

Best suited to:
CX, product and research teams that already collect feedback but struggle to analyze it consistently.

Pricing/free trial:
Thematic's Foundation plan is currently listed at $25,000 per year for up to 25,000 comments, while Enterprise pricing is customized. The vendor directs prospects to book a demo.

Verdict:
Thematic is a practical shortlist option when open-text analysis itself is the problem you need to solve.

Dovetail

Best for: Qualitative research synthesis, research repositories and reusable customer knowledge.

Why it made the list:
Dovetail combines research-repository workflows with AI-powered analysis and customer intelligence. Its current product can answer research questions with citations back to underlying evidence, summarize material and surface information through semantic search.

Key AI capabilities

• Cited AI answers over research data
• Summarization
• Semantic search
• Research and customer-evidence synthesis
• Cross-project knowledge discovery

Where it fits in the CX workflow:
Dovetail is useful after interviews, research sessions, feedback, calls or other customer evidence has been collected and the organization needs to make that evidence findable and reusable.

Strengths

• Good fit for qualitative evidence
• Research can remain connected to source material
• Helps prevent valuable findings from disappearing in one-off study folders

Potential limitation / consideration

• Dovetail does not replace participant recruitment and testing platforms in every workflow; teams that need to run usability sessions may pair it with a tool such as UserTesting.

Best suited to:
UX researchers, product researchers, product managers and organizations building a shared repository of customer evidence.

Pricing/free trial:
Dovetail currently offers a $0 Free plan, while Enterprise uses custom pricing. Its research-repository offering also promotes a 60-day full-access trial with no credit card.

Verdict:
Dovetail is strongest when customer insight already exists but is fragmented, difficult to retrieve or repeatedly re-researched.

UserTesting

Best for: Learning how real people experience a product, prototype, website or journey.

Why it made the list:
UserTesting differs from feedback-analysis platforms because it helps teams generate new customer evidence through direct research with participants. Its current platform includes moderated and unmoderated research plus AI capabilities for test creation, summaries, analysis, sentiment and insight discovery depending on plan.

Key AI capabilities

• AI-generated research test drafts
• AI summaries and analysis
• Sentiment analysis
• Insight discovery
• Assistance across qualitative research workflows

Where it fits in the CX workflow:
Use UserTesting when behavioral analytics tells you what happened, but the team still needs to watch and hear customers explain why an experience is confusing, persuasive or difficult.

Strengths

• Direct access to human research evidence
• Supports moderated and unmoderated methods
• AI speeds research preparation and synthesis without eliminating participant evidence

Potential limitation / consideration

• It is a research platform, not a replacement for always-on support automation or an enterprise VoC listening layer.

Best suited to:
UX research, design, product and customer-insights teams validating products and experiences before or after launch.

Pricing/free trial:
Current enterprise plans use request-pricing packaging. UserTesting states that buyers can request a platform trial, and it also offers a limited way to run a free test before purchase.

Verdict:
UserTesting is the strongest fit in this list when the question is not simply "what are customers saying?" but "what happens when real people actually try this experience?"

Contentsquare

Best for: Behavioral and digital-experience analysis across websites and apps.

Why it made the list:
Contentsquare helps teams understand digital behavior through journeys, heatmaps, session replay, funnels, feedback and its Sense AI capabilities. Current versions combine behavioral analytics with Voice-of-Customer functionality and AI-driven analysis.

Key AI capabilities

• Sense conversational analytics
• AI summaries and analysis
• Behavioral journey analysis
• AI-assisted survey creation and survey summaries
• Digital-friction identification

Where it fits in the CX workflow:
Contentsquare is useful when a CX or product team needs to understand where users abandon, hesitate, encounter errors or behave differently from expectations in a digital journey.

Strengths

• Connects quantitative behavior with qualitative feedback
• Strong visual evidence through replay, heatmaps and journeys
• Entry-level plans make experimentation possible before enterprise purchase

Potential limitation / consideration

• Digital behavioral evidence cannot by itself explain every motivation; complex questions may still require interviews, usability testing or broader VoC analysis.

Best suited to:
Digital CX, ecommerce, product, UX and conversion teams optimizing websites and applications.

Pricing/free trial:
Contentsquare currently offers a Free plan at $0. Experience Analytics Growth starts at $49 on the annual pricing shown on its current pricing page, while more advanced plans require sales engagement. New accounts receive a 15-day Growth trial without automatic charges.

Verdict:
Contentsquare is the most relevant product in this guide when your biggest CX blind spot is what customers actually do inside a digital experience.

How Is AI Used in Customer Experience?

AI in CX generally performs five jobs:

1. Answering: retrieving knowledge and resolving customer questions.
2. Classifying: organizing survey responses, tickets, reviews and conversations.
3. Synthesizing: turning large bodies of qualitative evidence into themes or summaries.
4. Detecting: identifying behavioral patterns, anomalies, sentiment or friction.
5. Assisting: helping human support agents, researchers and analysts work more quickly.

The most important buying question is therefore not, "Which company has the most AI?" It is, "Which customer-experience job do we need AI to perform?"

Where AI Helps CX Teams — and Where Humans Still Matter

AI is especially useful when the work involves repetition or scale. Retrieving an answer from a large knowledge base, classifying thousands of comments, summarizing transcripts or finding recurring patterns are all tasks where automation can remove substantial manual effort.

Humans remain critical where context, judgment or responsibility matters more than throughput. That includes sensitive escalations, ambiguous customer situations, strategic interpretation, relationship management, research design and governance.

The strongest implementation model is usually not "AI instead of people." It is AI handling high-volume retrieval and first-pass analysis while humans own exceptions, judgment, research quality and accountability.

For customer-facing generative AI, teams should explicitly define what happens when the system lacks sufficient evidence, when escalation occurs, what outputs are monitored and how performance is evaluated over time. NIST's generative-AI risk guidance reinforces the importance of ongoing measurement and risk management rather than treating deployment as the end of the process.

9. Buyer's Decision Framework

Which Type of AI CX Tool Do You Actually Need?

The easiest way to choose among AI customer experience tools is to start with the bottleneck, not the vendor category.

If your bottleneck is repetitive customer questions

Look at knowledge-grounded customer-support AI.

Best-fit examples: CustomGPT.ai, Intercom or Zendesk, depending on whether you need a specialized knowledge layer or a broader service platform.

Choose this category when customers repeatedly ask questions whose answers already exist in documentation, help-center articles, policies or product information.

If your bottleneck is thousands of survey responses, reviews and tickets

Look at Voice-of-Customer and feedback-intelligence software.

Best-fit examples: Qualtrics, Medallia, Chattermill or Thematic.

The central capability is not generating customer-facing answers. It is identifying recurring themes, sentiment, drivers and trends across large volumes of customer evidence.

If your bottleneck is fragmented qualitative research

Look at a research repository and AI synthesis platform.

Best-fit example: Dovetail.

This category makes past interviews and research reusable instead of allowing valuable findings to disappear after a project ends.

If your bottleneck is understanding usability problems

Look at UX research and user-testing software.

Best-fit example: UserTesting.

Behavioral data may tell you a user abandoned checkout. Direct testing can reveal that the person misunderstood the shipping options, did not trust a message or could not find the next action.

If your bottleneck is digital drop-off or unexplained behavior

Look at behavioral and digital-experience analytics.

Best-fit example: Contentsquare.

Use this category to investigate journeys, funnels, clicks, errors, hesitation and other behavioral evidence at scale.

Why two tools may be better than one "all-in-one" platform

Many mature CX teams need both an action layer and an insight layer.

For example, a company could use CustomGPT.ai to answer product questions from approved documentation while using Chattermill to analyze feedback collected across many channels. A product team could combine Contentsquare's behavioral evidence with UserTesting research to understand both where users fail and why.

The right architecture is determined by the customer journey and existing data, not by a desire to reduce the stack to one logo.

How to Choose an AI Tool for Customer Experience

Use these questions during vendor evaluations:

• What exact customer problem are we trying to solve?
• Does the system need to answer customers or analyze customers?
• What proprietary content or customer data must it access?
• Which data sources are essential on day one?
• How does the vendor measure answer or insight quality?
• Can customer-facing answers be traced to supporting sources?
• What does the system do when it lacks sufficient information?
• When does a human take over?
• Can administrators review problematic outputs?
• Which channels are supported?
• Does it integrate with our help desk, CRM, survey, product or research stack?
• How are permissions, privacy and retention managed?
• How much configuration is required before useful results appear?
• Does pricing scale by users, interactions, responses, AI outcomes or data volume?
• What happens to costs if usage triples?
• Which capabilities are included in the plan versus sold as add-ons?
• Can we test the product with our own content and realistic customer scenarios?
• What baseline metrics will we capture before deployment?
• How will we measure success after 30, 60 and 90 days?

For customer-facing AI, do not evaluate accuracy using polished demo questions alone. Build an evaluation set from real support queries, ambiguous questions, outdated terminology, missing-information cases and queries that should trigger escalation.

10. FAQs

What is the best AI tool for customer experience?

There is no single best AI CX tool for every organization. CustomGPT.ai is particularly relevant for source-grounded customer self-service; Qualtrics and Medallia address broad enterprise experience management; Chattermill and Thematic focus on feedback intelligence; UserTesting specializes in participant-based UX research; and Contentsquare is designed around digital behavior. The best choice depends on the job you need the AI to perform.

What AI tools do customer experience teams use?

CX teams use several categories of AI software: customer-support agents, Voice-of-Customer platforms, text and sentiment analytics, research repositories, usability-testing tools, behavioral analytics and agent-assistance systems. A mature CX stack may contain multiple categories because answering a customer's question and analyzing thousands of customers' comments are fundamentally different workflows.

How can AI improve customer experience?

AI can reduce the time required to retrieve answers, analyze large quantities of feedback, summarize research, identify patterns and triage repetitive work. The practical benefit depends on the workflow: support AI can increase access to information, while feedback AI can help CX teams understand recurring pain points faster. Human review remains important for sensitive cases, strategy, research design and governance.

What is the best AI tool for analyzing customer feedback?

For dedicated feedback analysis, Chattermill and Thematic deserve consideration. Chattermill is oriented toward unifying feedback across many sources and turning it into broader CX intelligence, while Thematic offers a more focused approach to identifying themes and sentiment in unstructured comments. Qualtrics and Medallia are stronger candidates when feedback analysis is part of a much larger enterprise experience-management program.

Can AI analyze customer sentiment?

Yes. Several current CX platforms use AI or machine-learning techniques to classify sentiment or emotion in customer text. However, sentiment should be treated as a signal rather than unquestionable truth. Sarcasm, domain-specific language and ambiguous comments can create classification errors, so important decisions should preserve access to the underlying customer evidence.

Can AI replace customer service agents?

AI can automate repetitive questions, retrieval and some transaction-oriented workflows, but that does not eliminate the need for human service professionals. Escalations, unusual circumstances, vulnerable customers, relationship management and judgment-heavy cases still benefit from human decision-making. A stronger design is to determine which work can safely be automated and define explicit escalation paths for the rest.

What is the difference between CX AI and a regular chatbot?

A regular chatbot describes an interface: the user sends a message and receives a response. CX AI describes a much broader set of capabilities. It may retrieve company knowledge, analyze surveys, detect themes in tickets, summarize research, identify behavioral friction or assist human agents. Some CX tools have no customer-facing chat interface at all.

What should enterprises consider before using AI for customer-facing answers?

Enterprises should evaluate source grounding, permissions, escalation behavior, monitoring, privacy, security, model and vendor governance, data retention and measurable answer quality. Testing should include realistic failure cases, not only straightforward FAQs. NIST's Generative AI Profile provides a useful cross-sector risk-management reference for organizations evaluating generative-AI systems.

11. Conclusion: Choose the Customer Problem Before the AI Platform

The phrase AI tools for customer experience now covers products that perform very different jobs. That is why feature-count comparisons can be misleading.

Choose CustomGPT.ai, Intercom or Zendesk when your priority is resolving customer questions. Look toward Qualtrics, Medallia, Chattermill or Thematic when the bottleneck is understanding customer signals at scale. Use Dovetail or UserTesting for evidence-driven research workflows, and Contentsquare when the unanswered question is how customers behave inside a digital journey.

For many organizations, the best architecture will be a combination: one platform helping customers in the moment and another helping the company learn from customer behavior over time.

If your immediate priority is giving customers instant answers based on your company's own support documentation and proprietary knowledge, explore how CustomGPT.ai approaches AI-powered customer support and review its current pricing options.