How to Build an Omnichannel AI Platform Like LivePerson

Every brand today faces the challenge of customers reaching out on Instagram, sending emails to support, and chatting on the website, sometimes all at once. Handling these channels separately can cause slow replies and miscommunication. To solve this, more businesses are choosing the Omnichannel AI Platform, which brings every interaction together in one connected system.

Platforms like LivePerson show how AI helps businesses connect with customers by understanding context, predicting intent, and offering consistent experiences across all touchpoints. When companies use AI in chat, voice, social, or SMS, they can create customer journeys that feel personal instead of robotic.

In this blog, we’ll show you how to build your own Omnichannel AI Platform like LivePerson. We’ll cover the key technologies, walk through the architecture, and explain development strategies to help you create a system that grows with your business and builds strong customer relationships. With IdeaUsher’s expertise in AI-driven communication systems, we help businesses design scalable omnichannel platforms that unify customer interactions and deliver consistent, intelligent engagement across every touchpoint.

What is an Omnichannel AI Platform, LivePerson?

The Omnichannel AI Platform, LivePerson, unifies customer interactions across web chat, apps, SMS, social, and voice into a single experience. Using AI, automation, and analytics, it enables brands to engage seamlessly from any channel. It helps deliver personalized, efficient, and consistent experiences by blending AI automation with human support.

This platform manages, optimizes, and analyzes customer conversations in real time, combining automation and human intelligence to boost satisfaction, reduce costs, and improve outcomes. When LivePerson talks about “omnichannel,” here’s what they mean and offer:

• Multiple channels: The platform supports web chat, mobile in-app messaging, SMS, WhatsApp, Apple Messages for Business, social messaging apps, and voice calls.
• Unified orchestration: All channel conversations are managed on a single platform, providing the brand with a unified view and seamless routing between bots and human agents.
• AI and humans working together: The system uses AI (natural language understanding, intent classification, chatbot automation, large language models) to handle routine queries, while human agents are brought in for more complex interactions.
• Integrations: It connects to backend systems like CRM platforms, ordering systems, billing, telephony infrastructures, etc., enabling conversational data to tie into a broader enterprise ecosystem. 

Business Model

LivePerson operates primarily as a software-and-services business that provides conversational AI and omnichannel customer-engagement solutions to enterprises. Key points:

• The company offers a cloud-based platform (often referenced as the “Conversational Cloud”) where brands can manage real-time messaging, voice, chatbots, and human-agent interactions across multiple channels (web, mobile, SMS, social, etc.). 
• The platform cuts costs (e.g., automating inquiries) and boosts revenue (such as conversational commerce, higher conversions, better retention). LivePerson reports a “2-3× sales uplift” and “15-20% lower cart abandonment” via conversational commerce.
• LivePerson also builds out its ecosystem with partner programmes, professional services (consulting, implementation, optimisation) and industry-specific features (e.g., for regulated industries).

Revenue Model

LivePerson’s revenue model can be broken into several main streams:

• Subscription / Platform Fees: This is the core recurring revenue: enterprises pay for access to the Conversational Cloud (licenses, seats, messaging volume, or usage tiers). Sources state 93% of the company’s recent revenue is recurring.
• Usage-based / volume-based fees: Some revenue depends on platform usage, like messaging volume, conversations, agents, and channels. For example, a business model site states “usage-based fees… charge customers based on message volume.”
• Professional services and implementation/consulting: When enterprises adopt the platform, LivePerson provides implementation, customization, and training services. These generate mainly non-recurring revenue as supplementary streams.
• Partner/reseller channel revenue: LivePerson’s partner ecosystem features solution providers and integrators who sell or bundle its platform and services, extending reach to SMBs. Its partner program lets providers sell the platform and earn margins.

Workflow of the LivePerson platform

The LivePerson platform’s workflow shows how AI messaging and automation link brands with customers across channels, from engagement to resolution, for personalized and scalable conversations.

1. Customer initiates interaction via any channel

A customer contacts support using their preferred channel, such as web chat, mobile app, SMS, WhatsApp, or Apple Business Chat. The platform ingests the message (or voice call) and begins tracking the conversation context.

2. Intent detection and understanding

Using the built-in NLU (natural language understanding) engine via their “Intent Manager”, the system classifies what the customer wants (the “intent”) and extracts entities/context.

The system may refer to a taxonomy of intents (predefined or custom) to know if this is something to automate, or needs human involvement. 

3. Routing & orchestration

Based on the intent, context, channel, customer profile and business rules, the platform’s “Conversation Orchestrator” component decides:

• Can the request be handled via automation (bot/AI), or should it go to a live agent?
• Which resource (which agent, which bot, which data system) should handle it? 

It also ensures context is preserved across channel shifts (e.g., starting on web chat then switching to voice).

4. Automated conversation/bot handling

If the request can be automated, the platform triggers a bot or AI agent to respond. With the “Conversation Builder” (drag-and-drop/point-and-click interface), the company constructs flows for common intents.

The automation may pull relevant data from backend systems (CRM, order system, knowledge base) via integrations. The platform provides “Functions” (serverless customization) to connect to systems.

Example: A message arrives: “Where is my order?” → Bot asks for order number → fetches status via integration → responds to customer.

5. Human agent handoff when needed

If the automation cannot handle (e.g., complex query, high-value customer, escalation, sentiment triggers), the system hands off seamlessly to a human agent. The orchestrator retains the context so the customer doesn’t repeat themselves.

Agents use agent-assist tools like suggested responses, knowledge base retrievals, and real-time insights. These tools help them work more efficiently.

6. Multichannel continuity

Throughout its lifecycle, the platform brings together interactions from different channels like voice, chat, SMS, and messaging apps. It keeps a single conversation history, allows easy channel switching, and maintains consistent context.

For voice, the platform may integrate or convert to messaging (or route between them) to leverage automation.

7. Data, analytics & optimization

The platform captures conversation logs, intents, outcomes, agent performance, automation containment rates, sentiment, etc. These feed into analytics (e.g., the “Conversational Intelligence” suite).

Brands use these insights to:

• Understand which intents are frequent, and fine-tune automation models.
• Identify drop-off points or poor experiences.
• Link conversation outcomes to business metrics (sales conversions, CSAT, cost savings).

Workflow automation tools (e.g., via embedded Workato) allow triggering external business-system events from conversational triggers (cart abandonment → proactive messaging).

Types of Omnichannel AI

Omnichannel AI varies based on how organizations utilize artificial intelligence to connect, automate, and personalize customer interactions, representing different maturity stages from basic support to advanced, AI-driven personalization across all touchpoints.

1. Reactive Support & Unified Interaction

This type handles incoming customer-initiated interactions across channels, but ensures those channels are unified so the context of the customer is preserved.

Key features:

• AI chatbots and voice assistants understand prior interactions.
• Centralized customer profile across all touchpoints.
• Context-aware routing to the right department or agent.

Why it matters: When a customer contacts a telecom company about a billing issue via chat and later calls support, the AI system shares chat details with the agent, eliminating repetitive questions and enhancing satisfaction.

2. Proactive & Predictive Engagement

Rather than only reacting when the customer reaches out, this type uses AI to anticipate needs across channels and trigger an engagement (message, offer, content) proactively.

Key features:

• Predictive analytics for next-best-action.
• Automated engagement decisions based on customer data.
• Real-time triggers for offers or support outreach.

Why it matters: A customer frequently checks ticket prices on an airline’s website but doesn’t book. AI predicts their intent and sends a personalized email or app notification with a discount or flexible date option.

3. Channel Integration & Automation Orchestration

This type emphasizes the technical orchestration and automation of multiple channels: chat, email, voice, social, app, in-store. AI coordinates and automates the workflow across them.

Key features:

• Unified backend connecting chat, email, SMS, app, and voice.
• Automated workflows for follow-ups and updates.
• Centralized analytics dashboard for monitoring all channels.

Why it matters: Imagine a customer books a hotel room via the website. The AI system automatically sends a confirmation email, an SMS reminder the day before check-in, and an in-app welcome message when the customer arrives.

4. Hyper-Personalisation & Advanced Experience (AI-Driven)

This type goes beyond “any channel” to deliver deeply personal experiences, immersive features (voice, visual, AR) and uses advanced AI (vision, LLMs, agentic AI) across channels.

Key features:

• Real-time personalization using customer data.
• AI-powered recommendations, AR/VR try-ons, and voice assistance.
• Intelligent agents that manage end-to-end journeys autonomously.

Why it matters: A fashion retailer’s app allows customers to upload a photo and see how outfits would look on them through AR. AI also recommends complementary accessories based on style preferences and past purchases.

How 90% of Businesses Leverage Omnichannel AI for Seamless CX?

The global Omni-channel retail solutions market was valued at USD 8.28 billion in 2024 and is forecast to grow at a CAGR of 12.03% during 2024–2034, reaching about USD 25.83 billion. This growth is driven by increased adoption of integrated retail strategies, improved customer experience tech, and the rise of seamless online–offline shopping globally.

Omnichannel AI is transforming how businesses connect and personalize across digital and physical touchpoints. Although over 90% of enterprises use omnichannel strategies, only 8% are true AI experts. Successful AI integration in omnichannel systems drives impact, growth, and measurable ROI.

Adoption and Market Momentum

Recent surveys reveal that B2C marketers using AI in omnichannel marketing jumped from 77.2% to 95.4% in 2024, marking one of the fastest adoption rates across enterprise tech. Despite challenges such as data integration and scalability, the appeal of AI-driven consistency across customer journeys is undeniable.

Up to 73% of shoppers engage multiple touchpoints before buying, averaging six interactions. Firms using AI across these journeys see 287% higher purchase rates than single-channel efforts, showing integrated intelligence boosts conversions.

Quantifiable Business Impact

AI-powered omnichannel systems are delivering tangible, high-value results across industries.
According to consolidated benchmarks:

• Average Order Value (AOV) rose from $85 to $106 after AI adoption, a 25% jump.
• Repeat Purchase Rates improved by 21% (38% → 46%), while churn dropped by 32% (22% → 15%).
• Campaign ROI increased by 62%, jumping from 4.2x to 6.8x.
• CSAT scores doubled, a 67% for AI-powered omnichannel vs. 28% for traditional setups.

Even more striking, retailers using omnichannel AI have reported a 25% boost in online sales, 15% growth in-store, and 12% rise in AOV, underscoring that AI unification across channels enhances both digital and physical experiences.

Enterprise Success Stories

Discover how leading global enterprises are leveraging AI-driven omnichannel strategies to achieve remarkable growth, efficiency, and customer satisfaction.

1. Coca-Cola: Scaling Personalization and Sales

Coca-Cola’s omnichannel transformation through SuperAGI resulted in a 25% increase in retention, 30% rise in sales, and 35% ROI improvement within six months. AI-enhanced personalization also drove 20% higher conversion rates and a 15% AOV uplift, while customer satisfaction improved by 18%.

2. Citibank: Omnichannel AI in Banking

By unifying customer profiles and real-time interactions through AI, Citibank achieved a 30% increase in customer lifetime value and 20% boost in service conversion rates, while significantly cutting response times and improving client satisfaction.

3. Matahari: Record-Breaking ROI

Indonesian retail giant Matahari deployed Insider’s AI-powered CDP and journey orchestration tools, achieving a 356x ROI,  one of the highest recorded in the sector. Email open rates surged from 5–10% to 30%, and mobile engagement soared across customer journeys.

4. Sephora & TGH Urgent Care: Industry Diversity in Impact

Sephora’s omnichannel AI chatbot, Virtual Artist, delivered 25% higher sales through personalized recommendations, while TGH Urgent Care saw a 40% reduction in incoming calls and 80% call answer rate improvement using LivePerson’s AI voice solutions.

The Bigger Picture: Omnichannel AI as a Business Growth Engine

The integration of AI into omnichannel frameworks is now a key strategic advantage. Companies using data-driven personalization, AI customer support, and real-time orchestration see ROI over 300%, quicker resolutions, and stronger brand loyalty.

As enterprises scale, the winning formula lies not just in adopting omnichannel strategies but in infusing them with intelligent automation, predictive insights, and unified data flows that make every interaction seamless and context-aware.

Key Features of an Omnichannel AI Platform like LivePerson

An omnichannel AI platform like LivePerson connects businesses with customers via websites, apps, messaging, and voice assistants. It uses conversational AI, automation, and analytics to provide consistent, human-like experiences for many users. 

Below are the core features that make an omnichannel AI platform both powerful and indispensable for modern enterprises.

1. Unified Messaging Across All Channels

A true omnichannel AI platform links customer interactions across touchpoints like web chat, WhatsApp, SMS, Messenger, and Apple Business Chat. It allows agents and bots to preserve context, ensuring seamless conversations and consistent experiences across channels.

2. AI-Powered Conversational Intelligence

LivePerson-like platforms rely on a strong conversational AI that uses natural language understanding and machine learning to interpret user intent, sentiment, and tone in real time. This allows accurate responses, routing complex queries to humans, and ongoing learning from interactions to enhance future replies.

3. Smart Routing & Agent Handoff

The platform routes conversations based on intent, topic, or urgency, ensuring queries reach the right agent or bot. Escalations between AI and humans are seamless, maintaining history and context to avoid repetition.

4. Integrated Analytics & Conversation Insights

An omnichannel AI platform provides analytics tracking customer behavior, engagement, and satisfaction across channels. These insights help businesses measure response times, agent performance, and conversation quality, highlighting automation and personalization opportunities.

5. Low-Code/No-Code Bot Builder

Modern platforms like LivePerson have low-code/no-code bot builders that enable non-technical users to quickly design, train, and deploy intelligent bots. This allows businesses to automate routine tasks such as order tracking, appointment booking, and FAQs.

6. Real-Time Customer Context & Personalization

The platform integrates with CRM systems, data warehouses, and customer history databases to personalize each interaction. By analyzing previous purchases, browsing patterns, and behavioral data, the AI delivers context-aware responses and tailored recommendations that enhance engagement and loyalty.

7. Voice & Messaging Integration

Beyond text-based chat, omnichannel AI platforms unify voice and digital messaging into a single communication ecosystem. This means a customer can start a query via chat, continue it through voice assistance, and receive follow-ups via messaging, without any break in continuity.

8. Omnichannel Workflow Automation

This feature lets businesses automate recurring customer journeys like scheduling, reminders, Returns, and service requests across channels. It cuts manual work and ensures quick, consistent, error-free responses on chat, email, social media, or messaging apps.

9. Proactive Engagement & Automation

Omnichannel AI platforms do more than just respond. Using predictive analytics, they proactively engage customers with targeted messages, reminders, and offers based on customer behavior and stage in their journey, increasing conversions and retention.

10. Human-AI Collaboration Dashboard

A unified agent interface allows human support teams to collaborate seamlessly with AI, showing real-time AI-suggested responses, intent detection, and sentiment cues. This empowers agents to intervene when needed and maintain conversation flow without losing context.

Development Process of an Omnichannel AI Platform

At IdeaUsher, we follow a structured, research-driven, and agile development process to build scalable omnichannel AI platforms that deliver seamless customer experiences across chat, voice, social, and web touchpoints.

1. Consultation

We start by meeting with you to learn about your audience, how you plan to use our services, and your business goals. Next, we look at which channels your customers use most, like web, WhatsApp, social media, or IVR, and decide what kind of automation such as support, sales, or engagement that will work best for your platform.

2. Platform Architecture & Channel Mapping

Next, we design a unified architecture that connects all communication channels through a central AI hub. This includes defining channel APIs, message routing logic, and integrating NLP and sentiment analysis layers to ensure a consistent experience across all touchpoints.

3. AI Model Development & Training

Our AI engineers build and fine-tune conversational models using frameworks like Rasa, Dialogflow, or custom LLM integrations. We train the models on real customer datasets, FAQs, and conversation logs to improve intent detection, emotional understanding, and contextual awareness.

4. Omnichannel Integration & Workflow Automation

This phase involves connecting all digital channels like email, chat, SMS, voice, and social, into a single ecosystem. We build automated workflows for routine interactions such as onboarding, feedback collection, lead qualification, and support queries, ensuring smooth cross-channel transitions.

5. Frontend Development

Our designers create intuitive admin dashboards and customer interfaces for real-time analytics, AI-human handoff control, and performance monitoring. The goal is to make managing conversations effortless while maintaining an elegant and cohesive design language across platforms.

6. Security Implementation

To ensure complete data privacy and trust, we integrate role-based access control (RBAC), data encryption, and compliance modules aligned with standards like GDPR, HIPAA, or ISO 27001, depending on your industry.

7. Testing & Quality Assurance

We conduct rigorous testing for performance, scalability, and multi-channel reliability. Each channel is tested independently and in cross-communication mode to ensure zero message loss, context continuity, and natural conversation flow.

8. Deployment & Continuous Optimization

Once approved, we deploy the platform to the production environment and monitor live performance. Post-launch, our AI team continuously re-trains the models using real-world data and user feedback, optimizing intent recognition and engagement rates over time.

Cost to Build an Omnichannel AI Platform like LivePerson

Building an omnichannel AI platform like LivePerson involves stages from consultation and design to AI training, integration, and ongoing optimization. Cost varies with complexity, channels, and AI sophistication.

Total Estimated Cost:  $66,000 – $140,000

Note: These cost estimates provide a clear picture of the investment required to develop a secure, scalable, and AI-driven omnichannel communication platform.

Consult with IdeaUsher to receive a customized quote and a detailed development strategy tailored to your business objectives, integration needs, and technical requirements.

Recommended Tech Stack for Omnichannel AI Platform Development

Building an omnichannel AI platform demands a well-selected tech stack for seamless communication, strong AI interactions, and smooth enterprise integration.Below is the recommended technology stack categorized by major components.

1. Frontend Development

Use frameworks like React.js, Angular, or Vue.js to build interactive and responsive user interfaces. These technologies support real-time dashboards, chat interfaces, and admin panels, ensuring consistent experiences across devices.

2. Backend Development

For backend services, choose Node.js, Python (Django/Flask), or Java (Spring Boot) to efficiently handle high traffic and real-time communication, providing the scalability needed for complex workflows.

3. AI & NLP Engine

Boost conversational intelligence using TensorFlow, PyTorch, spaCy, or Hugging Face Transformers for natural language understanding. Utilize APIs like Google Dialogflow, Microsoft LUIS, or OpenAI API to speed up development and improve accuracy.

4. Omnichannel Communication Layer

Integrate messaging APIs like Twilio, WhatsApp Business, Slack, Microsoft Teams, and Facebook Messenger to enable seamless conversations across various channels, accommodating user platform preferences.

5. Cloud Infrastructure & Hosting

Deploy on scalable cloud platforms like AWS, Google Cloud Platform (GCP), or Microsoft Azure. These providers offer AI toolkits, autoscaling, secure storage, and high availability, crucial for handling omnichannel traffic efficiently.

6. Integration & Middleware

Adopt GraphQL or RESTful APIs for integration with CRMs, ERPs, and analytics platforms. Middleware tools such as Apache Kafka, MuleSoft, or RabbitMQ can help manage event-driven communication and data exchange between services.

Challenges & How to Overcome Those?

Developing an omnichannel AI platform is complex, requiring system integration, consistency, and security across customer touchpoints. Key challenges can be addressed with strategic design, strong data governance, and adaptive AI infrastructure.

1. Data Silos and Integration

Challenge: Data spread across CRM, ERP, analytics, and chat systems causes fragmented insights, leading to inconsistent customer understanding and poor personalization across channels.

Solution: We solve this by implementing a unified Customer Data Platform (CDP) that centralizes structured and unstructured data. Through ETL/ELT pipelines, API gateways, and standardized data schemas like JSON and XML, we ensure real-time synchronization and semantic alignment across systems.

2. Maintaining Consistency Across Channels

Challenge: Different AI models, tones, or logics across channels often cause inconsistent service experiences, damaging brand reliability and user trust.

Solution: We maintain consistency by using a shared knowledge base and a centralized NLP intent recognition model across all AI agents. Continuous A/B testing ensures tone, logic, and accuracy remain aligned across chat, voice, and social interfaces.

3. AI Model Management and Drift

Challenge: Different AI models for personalization, recommendation, and intent recognition face model drift, resulting in performance inconsistencies and reduced relevance.

Solution: We deploy MLOps pipelines for continuous model integration, monitoring, and retraining. With model dashboards and explainable AI (XAI) practices, we ensure transparency, reliability, and trust in model behavior over time.

4. Integration with Legacy Systems

Challenge: Many enterprises still rely on legacy CRM and ERP systems without modern APIs, slowing down AI platform integration.

Solution: We integrate legacy environments through API wrappers and middleware connectors, enabling new AI services to access existing data. Over time, a hybrid migration strategy and enterprise service bus (ESB) streamline communication between old and new systems.

Conclusion

Building an Omnichannel AI Platform like LivePerson requires a deep understanding of customer engagement, seamless integration across channels, and advanced conversational AI. Such a platform empowers businesses to offer consistent, personalized experiences while optimizing operational efficiency. From natural language processing to predictive analytics, every component plays a key role in enhancing customer interactions. By investing in scalable architecture and data-driven intelligence, companies can create a unified ecosystem that not only meets customer expectations but also strengthens long-term brand loyalty.

Build Your Own Intelligent Omnichannel Platform with IdeaUsher!

At IdeaUsher, we specialize in creating AI-driven omnichannel communication platforms that redefine customer engagement. From real-time messaging and NLP-powered chatbots to seamless cross-channel integration, we help businesses deliver unified customer experiences.

Why Partner with Us?

• Expertise in Conversational AI: We design intelligent systems that enhance human-like interactions across all touchpoints.
• Custom-Built Solutions: Every business is unique, and our AI solutions are tailored to your goals, audience, and infrastructure.
• Proven Experience: With successful AI and automation projects across industries, we bring innovation and reliability to every build.
• Scalable Architecture: Our platforms grow with your business, supporting thousands of concurrent users effortlessly.

Explore our portfolio to see how we’ve empowered brands to build innovative AI solutions that deliver seamless, connected, and personalized customer experiences.

Contact IdeaUsher today for a free consultation and start building your omnichannel AI platform that connects, converts, and retains customers.

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