8 Best Conversational AI Agents for Retail

Introduction:

Retail contact centers absorb a specific mix of demand. Order status, returns, refunds, sizing, product defects, and loyalty questions arrive in waves that spike during promotions and holiday peaks. Staffing for that peak is expensive, and understaffing it costs revenue and repeat contacts. Conversational AI agents for retail change the math by resolving routine contacts automatically across voice and chat, then routing the rest to agents with full context.

The shift is already underway. Gartner projected that 80% of customer service organizations would use generative AI in production by 2025. For retail and ecommerce, the question has moved from whether to deploy a retail chatbot to which platform resolves contacts accurately, connects to store systems, and holds up under audit. This guide compares 8 conversational AI agents on features, integrations, retail fit, and verified reviews, so you can shortlist the right platform for your contact center.

What is an agentic AI platform for retail and ecommerce?

An agentic AI platform for retail runs software agents that reason across a task, call systems, and complete an action, rather than returning scripted replies. A traditional retail chatbot matches a question to a canned answer. An agentic agent reads the order record, checks the return policy, issues the credit, and updates the ticket, then hands off to a human when the case needs judgment.

The difference shows up in outcomes. A read-only bot deflects a WISMO contact by pasting a tracking link. An agentic agent authenticates the shopper, pulls the live carrier status, explains the delay, and offers a resolution the customer accepts. The best retail AI platforms extend that capability across channels, connect to CRM and order management, and score their own performance so operations teams catch failures. That combination of action, integration, and measurement separates an agentic platform from a scripted bot.

How to evaluate a conversational AI agent for retail?

Start with containment quality. Vendors quote containment or deflection rates, but a bot that ends a chat without solving the problem inflates that number while pushing the shopper to call back angrier. Ask for resolution rate on real retail intents, transcripts of contained conversations, and the escalation logic that decides when a human takes over. A platform that measures its own accuracy and surfaces failures gives you a truer picture than one that reports containment alone. This is where a rigorous evaluation framework pays off.

Then test integration and governance against your stack. The agent needs live access to order management, CRM, returns, and loyalty systems to resolve contacts instead of describing them. For enterprise retail, confirm PCI DSS handling for payment data, SOC 2 and GDPR compliance, role-based access, and an audit trail for every automated decision. Run a scoped pilot on your two highest-volume intents, measure resolution rate and CSAT against your current baseline, and check how much tuning the agent needs once traffic patterns shift.

Compare the 8 best conversational AI agents for retail

8 best conversational AI agents for retail

1. Level AI

Level AI runs an omnichannel contact center platform built for retail and ecommerce. Its AI virtual agent resolves WISMO, returns, and loyalty contacts across voice and chat around the clock, and routes complex cases to human agents with full context. The platform pairs that automation with quality assurance, generative coaching, and voice-of-customer analytics on one conversation data layer, so the same customer truth that trains the agent also scores agent performance and surfaces product defects. That shared data layer lets retail operations catch a sizing complaint or a defective SKU pattern from live conversations, not a monthly report. Level AI works across voice, chat, and omnichannel workflows, and holds consistency for loyalty and membership handling across every touchpoint.

Key features

• AI virtual agent that resolves order status, returns, refunds, and loyalty contacts across voice and chat 24/7

• Automated QA that scores 100% of interactions against your rubrics with QA-GPT

• Real-time agent assist that surfaces relevant policies and next-best actions during live contacts

• Generative coaching that builds personalized agent improvement plans from conversation data

• Voice-of-customer analytics that extract product defects, sizing complaints, and retention patterns from interactions

• Reporting that ties automation and agent behavior to CSAT, AHT, sales conversion, and adherence

Integrations

Level AI connects to CRM and ticketing systems, and plugs into the contact center stack through its integration library, so the virtual agent reads and writes order, customer, and case data during a live contact. The platform meets GDPR, HIPAA, PCI, ISO 27001, and SOC 2 requirements, which matters for retailers handling payment and personal data at scale.

Best suited for

Level AI fits enterprise retail and ecommerce contact centers that run high contact volume across voice and chat and need automation, QA, coaching, and analytics on one platform rather than four stitched-together tools. Retailers with peak-season surges, strict return and credit policies, and audit requirements get the most from the shared data layer, because the same conversation data that powers the virtual agent also documents compliance and feeds product and process improvement.

Product reviews

Level AI holds a 4.7 out of 5 rating on G2 across more than 200 reviews, with most feedback from mid-market and enterprise teams. Reviewers cite a straightforward implementation and reporting that lets teams track performance accurately. Purple, the sleep-products retailer, reached 100% QA coverage on voice conversations over two minutes and cut insight analysis from weeks to hours after deploying Level AI.

2. Gorgias

Gorgias is an ecommerce-native helpdesk with built-in automation for online stores. It centralizes email, chat, social, and SMS in one ticketing workspace and connects tightly to Shopify, so support agents see order and customer data next to each conversation. Its AI agent handles common ecommerce contacts like order tracking, returns, and product questions, and automates repetitive ticket actions.

Read: Gorgias is a strong fit for direct-to-consumer brands that run their storefront on Shopify and want an ecommerce AI chatbot tied to their commerce data.

Key features

• Unified helpdesk across email, chat, SMS, and social

• AI agent for order status, returns, and product FAQs

• Native Shopify integration with order and customer context in the ticket

• Automation rules and macros for repetitive ticket actions

• Revenue tracking that attributes sales to support conversations

Integrations

Gorgias integrates with Shopify, BigCommerce, and Magento, plus reviews, subscriptions, and marketing apps common in the DTC stack. The Shopify connection is the deepest and drives most of its automation value.

Best suited for

Gorgias suits SMB and mid-market DTC brands on Shopify that want an ecommerce helpdesk with automation out of the box. Buyers scaling AI automation should model per-resolution and ticket-based pricing carefully.

Product review

Gorgias holds a 4.6 out of 5 rating on G2 from roughly 550 reviews and a matching 4.6 on Capterra. Reviewers praise ease of use and Shopify integration, and flag that value-for-money scores drop once AI resolution and ticket-overage charges add up.

3. Fin by Intercom

Fin, formerly Intercom, sells an AI agent that resolves customer queries end to end across live chat, email, phone, WhatsApp, SMS, and Slack. It runs on Intercom's proprietary support model and draws answers from your help content and connected data. Salesforce signed a definitive agreement to acquire Fin for roughly $3.6 billion, which brings the agent into the Agentforce and Salesforce ecosystem. Fin fits retailers already running Intercom for support who want to automate a large share of incoming contacts on the same platform.

Read: Teams evaluating it against a purpose-built CX platform often compare Level AI and Fin directly.

Key features

• AI agent that resolves contacts across chat, email, phone, and messaging channels

• Answers grounded in help center content and connected data sources

• Per-resolution pricing tied to successful automated resolutions

• Native Intercom inbox, ticketing, and workflow tooling

• Reporting on resolution rate and deflection

Integrations

Fin connects to the Intercom platform and common CRM, commerce, and knowledge sources, with an expanding path into Salesforce systems following the acquisition. It works best when Intercom is already the support system of record.

Best suited for

Fin suits support teams standardized on Intercom that want to automate chat and email contacts without adding a separate vendor. Seat-based pricing on higher tiers can raise cost as the team grows.

Product review

Fin by Intercom holds a 4.5 out of 5 rating on G2 across more than 3,800 reviews. Reviewers cite quick, accurate answers and an intuitive interface, and note that some features sit behind higher pricing tiers.

4. Ada

Ada is a no-code conversational AI platform for automated customer service. Retail and ecommerce teams build bots that answer FAQs, track orders, and handle returns across chat and messaging, in multiple languages. Ada connects to help-desk systems and syncs knowledge automatically, so content updates flow into the bot. It fits digital-first support teams that want to stand up automation without heavy engineering.

Read: Buyers weighing Ada against other options often read up on AI customer service agents to compare resolution depth.

Key features

• No-code builder for conversation flows

• Multilingual automation across chat and messaging channels

• Automatic knowledge sync from connected help centers

• Generative answers grounded in your content

• Analytics on automated resolution and containment

Integrations

Ada integrates with Zendesk, Salesforce, and other help-desk and CRM systems, with strong Zendesk Help Center sync that populates the bot automatically as articles change.

Best suited for

Ada suits digital-first retail support teams that want fast, no-code deployment for chat automation. Teams with messy, off-script contacts should test how the bot handles edge cases before scaling.

Product review

Ada holds a 4.6 out of 5 rating on G2 from support managers and builders. End-user sentiment runs lower on public review sites, where shoppers report context loss between turns and difficulty reaching a human, so weigh administrator and shopper experience separately.

5. Sierra

Sierra builds conversational AI agents tuned to a brand's voice and policies, delivered through a managed engagement model. Its agents handle customer service contacts across chat and voice, and the vendor works closely with each company to configure behavior. Sierra targets consumer brands that want a polished agent experience without building it in-house. Retailers shortlisting it frequently review a side-by-side of Level AI and Sierra to compare deployment control and measurement depth.

Key features

• Conversational agents tuned to brand voice and guidelines

• Voice and chat customer service automation

• Managed configuration and deployment support

• Guardrails and policy controls on agent behavior

• Reporting on resolution and customer experience

Integrations

Sierra connects to CRM, order, and knowledge systems to resolve contacts, with configuration handled through its managed model rather than self-serve setup.

Best suited for

Sierra suits consumer brands that want a managed build and are comfortable with less self-serve customization. Teams that need to reshape agent behavior frequently should confirm turnaround on changes.

Read: Retailers comparing options often start with a roundup of Sierra alternatives, where Ada and Level AI appear as common substitutes.

Product review

Sierra holds a 4.3 out of 5 rating on G2 across a small review base. Reviewers praise the interface and integrations, and flag limited customization and context loss in longer conversations, so validate against your real contact mix.

6. Yellow.ai

Yellow.ai runs a multichannel conversational AI platform for voice and chat automation across enterprise use cases. Retailers use it to build bots that handle order queries, returns, and support contacts across web, app, and messaging channels in many languages. The platform targets global enterprises that need broad channel and language coverage.

Read: Teams weighing regional coverage against resolution depth often compare Yellow.ai alternatives before committing.

Key features

• Multichannel bots across web, app, voice, and messaging

• Voice and chat automation for support and commerce contacts

• Multilingual coverage for global deployments

• Generative and intent-based conversation handling

• Analytics on containment and engagement

Integrations

Yellow.ai integrates with CRM, commerce, and contact center systems, and offers prebuilt connectors for common enterprise tools across regions.

Best suited for

Yellow.ai suits global enterprises that need wide channel and language coverage. Reviewers report that customization can be less intuitive, so budget time for configuration.

Product review

Yellow.ai holds a 4.4 out of 5 rating on G2 across roughly 100 reviews, with an 87% recommendation rate on Gartner Peer Insights. Reviewers cite quick setup and note concerns about communication and support consistency.

7. Kore.ai

Kore.ai is a low-code enterprise conversational AI platform for building and running virtual assistants. Retail and ecommerce IT teams use it to design bots that handle order status, returns, and support contacts across voice and digital channels, with granular control over dialog and integrations. The platform targets IT-led deployments that need enterprise governance and customization.

Read: Buyers researching the broader category often start with a guide to agentic AI tools for enterprise contact centers.

Key features

• Low-code platform for building virtual assistants

• Voice and digital channel automation

• Enterprise dialog design and orchestration controls

• Prebuilt models and connectors for common use cases

• Analytics and monitoring for deployed bots

Integrations

Kore.ai integrates with contact center platforms, CRM, and enterprise systems, with extensive connector coverage suited to complex IT environments.

Best suited for

Kore.ai suits enterprise IT teams that want deep control over conversation design and governance. The breadth of features carries a learning curve, so plan for a longer build.

Product review

Kore.ai holds a 4.6 out of 5 rating on G2 across more than 460 reviews, with strong marks on Gartner Peer Insights. Reviewers praise low-code flexibility and integration depth, and note the platform can feel complex for new builders.

8. Cognigy

Cognigy runs a conversational and voice AI platform built for enterprise contact centers. Retailers use it to automate voice and chat contacts, connect bots to contact center infrastructure, and route complex cases to agents. The platform targets voice-heavy enterprise deployments that need tight contact center integration.

Read: Teams comparing bot-first tools against full agent platforms often read up on virtual agents versus chatbots first.

Key features

• Conversational and voice AI automation

• Prebuilt contact center and telephony integrations

• Low-code flow builder for dialog design

• Generative AI features for dynamic responses

• Analytics and reporting on automated interactions

Integrations

Cognigy integrates with major contact center platforms, CRM, and knowledge systems, with strong coverage for voice and telephony environments.

Best suited for

Cognigy suits enterprise contact centers with heavy voice volume that need bots wired into existing telephony and agent workflows.

Product review

Cognigy holds a 4.6 out of 5 rating on G2 and high marks on Gartner Peer Insights. Reviewers cite ease of use, fast build times, and a strong library of prebuilt integrations.

How to choose the right agentic AI platform for retail and ecommerce?

Match the platform to your contact volume, channel mix, and systems. A Shopify DTC brand with chat-heavy support has different needs than an enterprise retailer running voice and chat across regions with strict compliance. Rank your top five retail intents by volume, then score each platform on resolution rate for those intents, live access to your order and CRM data, and the effort required to keep the agent accurate as traffic shifts.

Weigh consolidation against point tools. Running automation, QA, coaching, and analytics on one platform means the agent, the scorecards, and the insights all learn from the same conversation data, which cuts integration overhead and closes the loop between what the bot does and how you improve it. A stack of separate tools can work, but every handoff between systems adds cost and blind spots. Decide which model fits your team before you shortlist vendors.

What are the benefits of using retail AI agents?

• Faster resolution on routine contacts. Order status, returns, and sizing questions get answered in seconds across voice and chat, which lowers wait times during peaks.

• Lower cost per contact. Automating high-volume intents deflects contacts from human queues, so agents spend time on cases that need judgment.

• Consistent policy handling. The agent applies return, credit, and loyalty rules the same way every time, with an audit trail for each decision.

• Around-the-clock coverage. Shoppers get accurate answers outside business hours without added headcount.

• Product and process intelligence. Conversation data surfaces defect patterns, sizing complaints, and retention signals that feed merchandising and operations.

• Scalable peak handling. Automation absorbs holiday and promotion surges without the cost of staffing for the spike.

What are the top Retail AI agent use cases?

• Order status and WISMO. The agent authenticates the shopper, pulls live carrier status, and explains delays across voice and chat.

• Returns, refunds, and exchanges. The agent applies policy, initiates the return, and issues the credit, then documents the action.

• Product and sizing questions. The agent answers fitment, availability, and specification questions, and recommends alternatives.

• Loyalty and membership. The agent handles points, tiers, and account questions consistently across channels.

• Post-purchase support. The agent handles warranty, defect, and missing-item cases, and routes complex claims to agents with context.

• Omnichannel availability. The agent checks online and in-store availability so a shopper who saw an item in a store can find it online. These map directly to documented retail AI agent use cases.

How Much Ongoing Maintenance Does a Retail AI Agent Require?

Ongoing effort depends on how the platform learns and how much of the tuning falls on your team. Retail traffic shifts constantly because new SKUs launch, promotions change return windows, and seasonal demand reshapes contact mix. An agent that scores its own performance and surfaces failures reduces manual tuning, because operations teams see exactly where accuracy drops instead of hunting for it. A platform that treats automation, QA, and voice-of-customer analytics as one system flags a new failure pattern from live conversations and feeds the fix back into the agent.

How to tell if a conversational AI platform is enterprise ready?

Enterprise readiness starts with security and compliance. For retail, confirm PCI DSS handling for payment data, SOC 2 Type II, GDPR and CCPA coverage, ISO 27001, role-based access control, and data residency options that match your regions.

Ask for the audit trail on automated decisions, because a retailer under scrutiny needs a record of what the agent did and why. A platform that cannot document its own actions is not ready for regulated, high-volume retail.

Then test scale, reliability, and measurement. The platform should hold performance during peak traffic, integrate with your order, CRM, and contact center systems through supported connectors, and report resolution quality rather than raw deflection. Check for enterprise support terms, uptime commitments, and a track record with retailers at your contact volume.

A platform that scores every interaction, ties agent behavior to CSAT and AHT, and proves resolution quality gives you the evidence an enterprise rollout requires.

Conclusion: Why consider Level AI for your retail and ecommerce contact center?

Level AI fits retailers that want automation, quality assurance, coaching, and voice-of-customer analytics on one conversation data layer, so the agent that resolves contacts and the scorecards that measure your team learn from the same customer truth. That model turns everyday retail conversations into product feedback, compliance evidence, and coaching, not just deflected tickets.

Purple, the sleep-products retailer, saw the difference in reporting speed.
As Angie McDonald, Director of Analytics and Workforce Planning, put it in Purple's case study: "In the past we had to download all of the results and go through call by call. But in Level, I'm doing something similar now and can do it in a couple of hours. I have fewer resources than I had in the past, and I can still deliver the insights."

For enterprise retail and ecommerce teams weighing conversational AI agents, Level AI pairs a purpose-built AI stack with a platform that measures its own accuracy, which is the combination that holds up in production and under audit.

Upgrade Your Retail Customer Experience with AI

Explore how enterprise retailers use Level AI to automate repetitive inquiries, improve customer satisfaction, and empower support teams.

Schedule a demo

Upgrade Your Retail Customer Experience with AI

Explore how enterprise retailers use Level AI to automate repetitive inquiries, improve customer satisfaction, and empower support teams.

Schedule a demo