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Customer conversations are now happening across multiple channels like voice, chat, email, apps, and even smart assistants. Many businesses still struggle to manage these touchpoints with disconnected tools, which often leads to messy communication and inconsistent experiences. As customer expectations continue to rise, companies need a unified and smarter solution.
A multi-channel platform like Kore can simplify this by ensuring consistent interactions and maintaining context across all channels. With features like AI-powered natural language understanding and seamless integration with existing systems, these platforms can automatically adapt and respond to customer needs in real-time. Instead of relying on isolated tools, businesses can benefit from intelligent agents that effectively connect data, people, and systems.
Over the past decade, we’ve built many multi-channel AI solutions, powered by advanced technologies like conversational AI, cloud computing, and omnichannel integration. So, we’re putting together this blog to share our expertise on the costs of building a multi-channel platform like Kore.ai. Let’s dive in!
Key Market Takeaways for Enterprise AI Assistants
According to MordorIntelligence, the enterprise AI assistant market is entering a period of strong expansion, growing from USD 16.29 billion in 2024 to a projected USD 73.80 billion by 2033, at a compound annual growth rate of 18.8%. This reflects how deeply companies are embedding AI assistants into their operations to drive productivity, automate workflows, and enhance the speed and quality of decision-making across departments.
Source: MordorIntelligence
Major financial institutions are leading the way in using AI assistants internally. Bank of America’s “Erica” supports wealth advisors and private bankers with real-time insights, improving client personalization and efficiency.
JPMorgan Chase’s LLM Suite, accessible to over 60,000 employees, simplifies daily tasks such as writing emails, summarizing legal documents, and conducting research. These implementations demonstrate how AI can streamline complex processes while maintaining security and governance.
Partnerships are proving essential for scaling AI within enterprises. Kyndryl’s collaboration with Google Cloud, which produced 100 scalable AI agents in just 100 days, shows how coordinated innovation can accelerate adoption and impact. Such initiatives highlight the growing focus on building adaptable, high-performing AI ecosystems that meet the evolving demands of modern business.
What is Kore.ai Platform?
Kore.ai is a versatile multi-channel platform that helps businesses create AI-powered virtual assistants and automation solutions. It allows companies to design, build, train, and deploy intelligent assistants that can handle complex tasks across multiple channels, including web, mobile, voice, and popular messaging apps like Teams and Slack.
Unlike simple bots, Kore.ai assistants can understand nuanced intent, manage multi-step workflows, and integrate with core business systems, making them a key part of any digital workforce.
Core Components of Kore.ai
Kore.ai stands out for its robust, modular architecture, designed to deliver a seamless and intelligent AI experience. Each component plays a crucial role in making sure the platform works efficiently across various business functions.
1. Multi-Agent Orchestration & Context Management
Imagine having specialized experts in different areas, like HR, IT, and customer service, all working together. Kore.ai enables you to create multiple AI agents, each designed for a specific function. The platform’s orchestration layer intelligently routes queries to the appropriate agent while maintaining a unified conversation context, even as control switches between agents.
2. Agentic RAG for Enterprise Data Access
Many platforms use basic RAG to pull information from documents. Kore.ai’s Agentic RAG goes further by not just retrieving data but also acting on it. This is tightly integrated with your organization’s Role-Based Access Control, ensuring that the AI only shares data the user is authorized to access.
3. Model Hub for LLM Agnosticism
In the fast-evolving AI landscape, vendor lock-in is a major concern. Kore.ai solves this problem with its Model Hub, which allows businesses to remain LLM-agnostic. You can choose the best AI model for each task, whether it’s OpenAI’s GPT-4 for complex reasoning, Anthropic’s Claude for safety, or a custom fine-tuned model for privacy.
4. Voice Gateway for Telephony Integration
The Voice Gateway is designed for seamless voice interactions, connecting digital assistants to telephony systems. It’s optimized for low-latency, transactional calls, providing a natural voice experience that feels like conversing with a real, knowledgeable agent, not a robotic phone system.
5. Extensibility through APIs & SDKs
While Kore.ai offers powerful low-code tools for most workflows, it also recognizes that some businesses have unique needs. The platform is designed to be easily extensible. With robust APIs and SDKs (JavaScript, Python), pro-code developers can customize and extend the platform to integrate with legacy systems or implement complex logic.
Cost to Build a Multi-Channel Platform Like Kore
Building a multi-channel platform like Kore.ai can be a significant investment, with costs varying based on the complexity and scope of the project. Key expenses include developing the core system, integrating communication channels, and ensuring strong security measures. By limiting integrations and simplifying certain features, the cost can be kept more manageable.
| Phase | Step/Sub-Step | Key Activities | Low-End Cost | High-End Cost |
| Phase 1: Strategy & Design (The Foundation) | ||||
| Define Use Cases and AI Strategy | Requirements gathering, ROI modeling, Technical Feasibility Study, and defining core agents | $15,000 | $35,000 | |
| Architect Multi-Agent Workflow | Defining Orchestration Logic, system architecture design, Shared State Schema | $20,000 | $45,000 | |
| Phase 1 Subtotal | Focus on scoping and architecture | $35,000 | $80,000 | |
| Phase 2: Core Development & Integration (The Build) | ||||
| Build a Low-Code Core + Pro-Code Extensions | Developing core dialogue engine, customizing open-source framework, API integration | $45,000 | $120,000 | |
| Integrate Multi-Channel Communication | Building 3-4 channel adapters (Web Widget, WhatsApp, Slack), implementing context service | $35,000 | $85,000 | |
| Implement Secure Enterprise RAG + RBAC | Setting up Vector DB, knowledge pipeline, and implementing RBAC security layer | $40,000 | $90,000 | |
| Phase 2 Subtotal | Focus on core development, integration, and multi-channel support | $120,000 | $295,000 | |
| Phase 3: Deployment, QA, & Management (The Launch & Scale Prep) | ||||
| Add Analytics, Monitoring & Model Hub | Creating tracking dashboards, setting up Model Abstraction Layer for LLMs | $25,000 | $60,000 | |
| Quality Assurance (QA) & User Acceptance Testing | End-to-end testing, load testing, bug fixes, UAT | $20,000 | $45,000 | |
| Deployment, Infrastructure & Initial Opex | Setting up cloud infrastructure (AWS/Azure/GCP) for MVP and hosting costs | $10,000 | $30,000 | |
| Phase 3 Subtotal | Focus on deployment, testing, and scaling | $55,000 | $135,000 | |
| Summary and Key Cost Drivers | ||||
| Total Estimate (MVP) | $210,000 | $510,000 |
The above cost breakdown to build a multi-channel platform like Kore.ai is just an estimate, with actual costs typically ranging from $210,000 to $500,000, depending on your specific needs. For a more accurate estimate, feel free to connect with us for a free consultation tailored to your project.
Factors Affecting the Cost of a Multi-Channel Platform Like Kore
Building a multi-channel conversational AI platform is complex, and its cost can vary based on key factors like use case complexity, system integration, and channel support. These decisions impact the overall budget significantly. By planning carefully and focusing on priorities, you can manage the costs effectively.
1. Complexity of Use Cases and AI Strategy
The complexity of the tasks your AI platform will handle is the most significant factor driving costs. It’s the difference between creating a basic FAQ bot and developing an AI that orchestrates complex business processes.
- Low Complexity (Tier 1): Basic FAQ handling and single-turn Q&A. Development costs range from $20,000 to $50,000.
- Medium Complexity (Tier 2): Multi-step dialogs and integration with 1-2 systems. Expect costs between $50,000 to $150,000.
- High Complexity (Tier 3): Multi-agent orchestration and complex workflows, such as personalized financial advice or IT support. Costs typically range from $150,000 to $300,000+.
The more sophisticated and intelligent the AI, the higher the cost in strategy, design, and development. A well-defined AI strategy that focuses on critical use cases helps keep costs manageable.
2. Integration with Legacy Systems
A major portion of the cost arises from connecting the AI platform with your existing enterprise systems. This “plumbing” requires careful planning and effort.
- Modern APIs: Integrating with a single modern system like Salesforce or Zendesk may cost between $5,000 and $15,000.
- Multiple Systems & Legacy APIs: Integrating with 3-5 core enterprise systems (e.g., CRM, ERP, HRIS) can cost $40,000 to $100,000.
- Complex Legacy Systems: Legacy systems without modern APIs can dramatically increase costs. Custom connectors for these systems can cost between $25,000 and $50,000+ each.
3. Number of Communication Channels
The more channels you want your platform to support, the higher the cost. Each new channel requires a new adapter, which involves additional development and testing.
- Digital-First Channels (Lower Cost): Adding basic channels like web or mobile chat typically costs $5,000 to $15,000 per channel.
- Popular Messaging Platforms (Medium Cost): Channels like WhatsApp, Facebook Messenger, or Microsoft Teams can add $10,000 to $25,000 per channel.
- Voice Interactions (Highest Cost): Adding voice capabilities with a dedicated Voice Gateway and quality speech-to-text/text-to-speech integration could increase costs by $75,000 to $150,000+.
4. AI Model Customization and Fine-Tuning
While off-the-shelf Large Language Models can work for general use cases, customizing the AI model for specialized enterprise tasks requires ongoing investment.
- Prompt Engineering & RAG Setup (Base Cost): Designing optimized prompts and setting up a basic Retrieval-Augmented Generation pipeline costs between $15,000 and $40,000.
- Secure RAG with RBAC (Medium Cost): Implementing Role-Based Access Control for data security and retrieval adds $30,000 to $60,000.
- Fine-Tuning / Custom Model Training (High Cost): Fine-tuning an open-source model with custom datasets and extensive resources can cost $50,000 to $200,000+.
Expect continuous investment in AI optimization. Customizing the model is not a one-time task; it requires ongoing efforts to maintain accuracy and relevancy.
AI Multi-Channel Platforms Can Boost Customer Satisfaction by 45%
The key to this improvement lies in how AI eliminates wait times and ensures fast resolutions. It also maintains consistent support across all channels, allowing customers to switch seamlessly between them.
1. Instant, 24/7 Support
The frustration of waiting for a support agent, especially with restrictive business hours or long hold times, is a common customer pain point.
How AI Works: An AI-driven platform can resolve most queries immediately, any time of the day or night. Whether it’s tracking an order or resetting a password, customers receive instant, accurate responses.
Customers no longer have to endure the stress of waiting in queues or adjusting to business hours. Immediate resolutions, no matter the time, build trust and enhance satisfaction.
For example, Spotify uses AI assistants across its help center and social media platforms to quickly handle routine inquiries, like account recovery or technical support, without customers needing to wait for a human response.
2. Omnichannel Consistency
Customers today seamlessly transition between platforms, starting on WhatsApp, moving to a website chat, and finishing on email. Traditional support systems often force them to repeat their issue, which can be incredibly frustrating.
How AI Works: AI-powered platforms can maintain a persistent conversation history across all channels. This means that whether the customer switches from chat to phone, the AI (or human agent) picks up where they left off, knowing the conversation’s context and progress.
3. Hyper-Personalization
Customers are more than just ticket numbers. The traditional, one-size-fits-all approach to customer support often feels impersonal.
How AI Works: By tapping into CRM systems and customer data (with consent), AI can personalize interactions. It might greet a premium customer by name or proactively offer assistance based on past purchases or browsing behavior.
Why This Matters: Personalization makes customers feel valued and understood, turning transactional queries into meaningful relationships. This emotional connection is a key driver of loyalty.
4. Proactive Support
Traditional support systems react to customer complaints; AI takes a proactive stance by anticipating issues before customers even know they exist.
How AI Works: The platform analyzes data to foresee potential problems. For instance, if a delivery is delayed, the system can proactively notify the customer of the new ETA. If a user struggles with a web form, the AI might offer help before frustration sets in.
Domino’s Pizza, for example, uses AI to send real-time order updates via notifications and SMS, so customers don’t have to ask, “Where’s my order?” before it arrives.
5. Seamless Human Handoff
AI is not meant to replace humans but to enhance their ability to serve customers more effectively. When complex issues arise, AI ensures customers are handed off to the right expert with all the context they need.
How AI Works: For complex issues, AI gathers all the relevant information and then transfers the conversation to the most suitable human agent, who already knows the problem and can focus on resolution immediately.
Bank of America’s virtual assistant, Erica, handles millions of simple requests each year. But when a customer needs to dispute a transaction, Erica seamlessly hands off the case to a specialized fraud agent, complete with all the details.
Business Cost-Saving Opportunities with AI Multi-Channel Platforms
Investing in an AI-powered multi-channel platform like Kore.ai offers businesses a strategic way to streamline operations and reduce costs. By automating routine tasks across customer service, IT helpdesks, and voice channels, companies can achieve significant labor cost savings and boost productivity.
Research indicates a return on investment ranging from 200% to 400% within the first 18-24 months. Here’s how different areas benefit:
1. Customer Support
Automating Tier-1 and Tier-2 support queries can significantly reduce the workload on live agents. By handling routine inquiries through AI, businesses can save on labor costs and ensure quicker resolution times for customers.
| Assumption | Value |
| Agent Full-Time Equivalents (FTEs) | 50 |
| Fully Loaded Cost per FTE | $60,000/year |
| Total Annual Support Labor Cost | $3,000,000 |
| Inquiries Handled per Agent | 15 inquiries/hour |
| Total Annual Inquiry Volume | 1,380,000 inquiries |
Total Annual Inquiry Volume: ~1.38 million inquiries (50 agents * 15 inquiries/hour * 8 hours * 230 days)
AI Platform Impact:
- Conservative Automation Rate: Automating 30% of total inquiries.
- Inquiries Automated by AI: 414,000 inquiries/year
- Cost per Inquiry: ~$2.17 (Total Labor Cost / Total Inquiries)
- Annual Labor Cost Savings: ~$900,000 (414,000 inquiries * $2.17)
For example, JP Morgan Chase reported that its COiN platform, which reviews commercial loan agreements, has cut down 360,000 hours of work done by lawyers and loan officers annually. In customer service, they use AI to handle a vast number of routine queries, directly reducing the need to scale their human agent workforce proportionally to customer growth.
Estimated Savings: ~$900,000 annually in labor cost avoidance from customer support automation.
2. Internal IT & HR Help Desk
Automating routine internal requests like password resets, software access, and HR policy inquiries allows IT and HR teams to focus on more complex, strategic tasks. This leads to cost savings and boosts employee productivity across the organization.
| Assumption | Value |
| IT/HR Support Staff (FTEs) | 10 |
| Fully Loaded Cost per FTE | $75,000/year |
| Total Annual Internal Support Cost | $750,000 |
| Average Handle Time for Routine Ticket | 15 minutes |
| Routine Tickets as % of Total | 40% |
AI Platform Impact:
- Routine Ticket Automation: Automating 40% of internal tickets.
- FTE Reduction: 2-3 FTEs through attrition or redeployment, freeing up resources for more value-added work.
- Annual Labor Cost Savings: $150,000 (2 FTEs * $75,000)
IBM, for instance, uses its own AI to manage internal IT support, deflecting over 40% of support tickets. This reduces the time spent on routine tasks, enabling IT professionals to focus on critical infrastructure and innovation projects, resulting in millions of dollars saved annually.
Estimated Savings: ~$150,000 annually in internal support labor cost savings.
3. Sales & Pre-Qualification
AI can be used to qualify leads 24/7, ensuring human sales reps only engage with high-quality, sales-ready leads. This leads to significant cost avoidance and helps accelerate revenue growth by increasing the conversion rates of qualified leads.
| Assumption | Value |
| Sales Development Representatives (SDRs) | 5 FTEs |
| Fully Loaded Cost per SDR | $70,000/year |
| Leads Qualified per SDR per Month | 100 leads/month |
| Cost to Qualify One Lead | ~$58 per lead |
AI Platform Impact:
- Lead Qualification Automation: AI handles 50% of all inbound leads.
- Cost Avoidance: Without AI, the company would need to hire an additional 2.5 SDRs to manage a 50% increase in lead volume, costing $175,000.
- Improved Efficiency: The existing SDR team, now focused only on AI-qualified leads, can convert leads more effectively, avoiding the need to expand the team immediately.
For example, Autodesk implemented an AI-powered chatbot named “Ava” to handle pre-sales queries. They reported a 32% increase in qualified leads and a reduction in lead-acquisition costs, as their human sales team became significantly more efficient by focusing their efforts where it mattered most.
Estimated Savings: ~$175,000 in cost avoidance by not hiring additional SDRs to manage increased lead volume.
Conclusion
Building a multi-channel AI platform like Kore.ai requires a strong understanding of conversational orchestration, AI model integration, and enterprise data security. While the investment can be significant, the potential return is transformative. It opens up new revenue streams, boosts customer engagement, and ensures your communication systems stay ahead of the curve. With the right partner, like Idea Usher, businesses can create their own AI-powered ecosystem, delivering results faster, more securely, and more efficiently.
Looking to Develop a Multi-Channel Platform Like Kore?
At Idea Usher, we specialize in transforming complex concepts into dynamic, scalable solutions that give your multi-channel platform a distinct competitive advantage. Our approach goes beyond just coding; we design intelligent ecosystems that integrate seamlessly, empowering your platform to adapt, evolve, and thrive across multiple channels.
Why Choose Idea Usher?
- World-Class Engineering: With over 500,000 hours of development experience, our team, comprising experts from top tech companies, delivers the expertise and precision needed for your project’s success.
- Built for Intelligence: We create the backbone of platforms like Kore, including multi-agent orchestration, secure RAG with RBAC, and an adaptable LLM model hub, ensuring your platform is both robust and future-proof.
- Scalable and Seamless: Whether it’s low-code dialog flows or advanced custom integrations, we ensure that your platform not only integrates smoothly with existing systems but also grows alongside your ambitions.
We’ve helped businesses across industries launch cutting-edge AI solutions.
Explore our latest projects to see how we can bring your vision to life.
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FAQs
A1: Building a platform like Kore.ai usually takes around 5 to 8 months. This time frame depends on the platform’s complexity, the number of required integrations, and the number of communication channels you want to support. The more channels and integrations involved, the longer the process might take, but it’s all about delivering a solution that fits your needs.
A2: Yes, you can definitely integrate your own LLM or fine-tuned model. Thanks to a modular Model Hub architecture, you can securely deploy custom or private LLMs. This flexibility gives you the control you need to leverage your specific models while ensuring everything runs smoothly and securely.
A3: Industries like Banking, Healthcare, Insurance, Retail, and HR Tech tend to see the highest ROI from platforms like these. These sectors often manage large volumes of customer interactions and data, making AI platforms a game-changer by improving efficiency, personalizing services, and automating processes.
A4: If you plan to run omnichannel operations, especially in sectors like healthcare or support, it’s highly recommended to include a voice gateway. Voice gateways are essential for ensuring real-time continuity across all touchpoints, making it easier to provide consistent service whether the interaction is via chat, voice, or another channel.
