How to Develop an Enterprise AI Assistant Like Kore.ai

Conversations between people and technology have moved far beyond simple chatbots. Businesses now need to provide fast, intelligent, and context-aware support across multiple touchpoints. Enterprise AI assistants like Kore help achieve this by automating tasks, enhancing communication, and offering personalized experiences. These platforms can understand user intent and analyze data to provide accurate responses. 

They also integrate seamlessly with existing systems to ensure smooth operations. The real value comes from their ability to trigger actions and orchestrate workflows across departments. With this, businesses can improve productivity and create a more unified experience. AI assistants adapt to specific needs and scale as the company grows.

We’ve built many AI-driven assistant solutions for enterprises across various industries that use technologies such as ML, NLP, and robotic process automation. As we’ve this expertise, we’re writing this blog to walk you through the steps to build an enterprise-grade AI assistant like Kore.ai. We’ll explore its architecture, essential features, tech stack, and development roadmap to help you create a powerful, scalable solution for your business.

Key Market Takeaways for Enterprise AI Assistants

According to GrandViewResearch, enterprise AI assistants are becoming a cornerstone of business strategy, with the global market expected to climb from $16.29 billion in 2024 to more than $73 billion by 2033. This growth reflects how quickly companies are embracing AI tools to boost efficiency, reduce costs, and unlock new ways of working. By 2025, an estimated 85% of enterprises plan to use AI agents in some form, marking a major step toward intelligent automation across industries.

Source: GrandViewResearch

Businesses are shifting from experimenting with AI to making it part of their core operations. These assistants are streamlining workflows, improving data-driven decision-making, and cutting down time spent on repetitive tasks. 

The impact is clear: employees gain more time for creative and strategic work, and teams collaborate more effectively across departments.

Leading adopters illustrate how enterprise AI assistants deliver tangible results. ATB Financial has integrated Google Workspace Gemini to automate repetitive workflows, while Banco BV uses generative AI to power research and streamline internal operations. These implementations demonstrate how AI assistants not only reduce manual workload but also improve responsiveness and knowledge sharing, turning AI from a support tool into a central pillar of business transformation.

What is the Kore.ai Platform?

Kore.ai is an enterprise-grade AI assistant platform designed to help businesses create and deploy advanced AI-powered virtual assistants and automate complex workflows. Unlike basic chatbots, Kore.ai’s platform allows organizations to build highly sophisticated AI agents capable of understanding complex tasks and interacting seamlessly with enterprise systems.

Here’s what makes Kore.ai stand out:

1. No-Code/Low-Code Builder 

Kore.ai allows business analysts to design custom conversation flows without writing code. Developers can extend these flows using JavaScript and webhooks for more complex actions.

2. Multi-Agent Orchestration

Multiple specialized AI agents can be created, such as HR or IT Helpdesk agents. These agents work together to route user queries to the right expertise, ensuring efficiency and accuracy.

3. Agentic AI & SmartAssist

Kore.ai’s AI doesn’t just retrieve information; it takes action. Its SmartAssist™ feature allows virtual assistants to carry out multi-step processes, like helping users apply for a loan.

4. Enterprise-Grade Security & RBAC

With role-based access control, Kore.ai ensures that only authorized users can access sensitive data, preventing leaks and ensuring compliance with security protocols.

5. LLM Agnosticism 

The platform allows businesses to use various AI models, such as OpenAI’s GPT-4 or Google’s BERT, offering flexibility in AI selection and better control over cost and data privacy.

6. Pre-Built Industry Solutions & Bots

Kore.ai provides pre-built virtual assistants tailored to industries like banking, healthcare, and retail. These bots can be quickly customized, reducing deployment time for common tasks.

7. Omnichannel Deployment

Once developed, assistants can be deployed across multiple channels, including websites, mobile apps, and messaging platforms. This ensures a consistent user experience across all touchpoints.

8. Kore.ai Voice Gateway

The platform also features a voice gateway optimized for low-latency, transactional voice interactions. This makes voice-based tasks fast and efficient, ideal for business environments.

How Does the Kore.ai Platform Work?

Kore.ai uses a multi-layered architecture to handle complex tasks. The platform captures user input, processes it through intelligent agents, and executes actions across enterprise systems. It allows businesses to automate workflows while maintaining a seamless and secure experience.

The Three Pillars of Kore.ai Architecture:

• The Interaction Layer: This layer handles all user interactions, whether through websites, mobile apps, messaging platforms like Microsoft Teams or Slack, or voice calls.
• The AI & Orchestration Brain: At the core, this is where the platform’s intelligence resides. It understands user language, makes decisions, and manages the workflows that guide the interaction.
• The Action & Integration Layer: This layer connects to enterprise systems to retrieve data and execute commands, ensuring that the assistant can complete tasks like booking a meeting or placing an order.

Example Journey of a User Query

User Input: An employee types into Slack: “Hey, I need to book a meeting room in New York for 10 people next Tuesday and order catering for the session.”

Step 1: Capture & Understand

The platform captures the user’s message from Slack and processes it through the Natural Language Understanding engine. This engine doesn’t just identify keywords; it fully understands the meaning behind the user’s request:

• Intent: The user’s goal is identified as “BookMeetingRoomAndCatering,” a multi-step task.
• Entities: Relevant details such as the location (New York), attendee count (10), and date (next Tuesday) are extracted.
• Context Awareness: The platform uses contextual information to enhance understanding. It knows the user’s identity, department, and permissions, ensuring that responses are relevant and secure.

Step 2: Orchestrate & Plan

Here, Kore.ai’s Multi-Agent Orchestration feature comes into play. The Orchestrator recognizes that the request requires two specialized agents to be involved:

• A Facilities Agent to handle the room booking.
• A Catering Agent to manage the catering request.

The Orchestrator ensures that both agents can work together within a unified session, directing the conversation flow as needed while keeping the user experience consistent. The user doesn’t need to know when the platform switches between agents; it feels like one seamless conversation.

Step 3: Retrieve & Generate (The AI Power)

Once the tasks are split among the agents, the platform utilizes its Agentic RAG (Retrieval-Augmented Generation) capabilities to take action:

Secure Retrieval

The Facilities Agent queries the company’s room booking system (such as Office 365 or Google Workspace) for available rooms in New York for 10 people. Role-Based Access Control is enforced, ensuring the agent only accesses data and performs actions that the user is authorized to view or modify.

Intelligent Generation

The data retrieved (e.g., available rooms) is passed through a Large Language Model, which generates a human-friendly response: “I found three available rooms: ‘Central Park View,’ ‘Hudson Room,’ and ‘Skyline Suite.’ Which one would you prefer?”

Step 4: Execute & Integrate

Once the user selects a room, the platform shifts from dialogue to action:

• The Facilities Agent reserves the room in the booking system.
• The Catering Agent is triggered to handle the catering part of the request. It guides the user through menu options from an integrated catering service like ezCater, all within the same conversation thread.

The Orchestrator ensures that all interactions remain within the same session, preventing the user from needing to switch between multiple systems or interfaces.

Step 5: Deliver & Confirm

Finally, after executing all actions, the platform delivers a final, clear confirmation to the user:

“Success! I’ve booked the ‘Skyline Suite’ for you in New York next Tuesday. Your catering order for 10 people with ‘City Deli’ has also been placed. You will receive separate email confirmations for both. Is there anything else I can assist you with?”

This confirmation consolidates the actions performed by both agents into a single, coherent message, completing the user’s request with ease. The platform’s ability to integrate multiple systems, manage tasks across different agents, and deliver a smooth, unified experience is what sets Kore.ai apart from simpler solutions.

What is the Business Model of the Kore.ai Platform?

Kore.ai is an enterprise AI platform that helps businesses improve customer and employee experiences with conversational AI and automation. It offers a no-code solution, allowing companies to easily build and manage virtual assistants across multiple channels like WhatsApp, Teams, and Slack. This approach simplifies the deployment of AI-driven automation without needing deep technical expertise.

• Enterprise SaaS Subscriptions: The flagship XO Platform is offered via tiered subscription models, ranging from $50,000 to $500,000+ annually. These subscriptions include omnichannel deployment, agent creation, lifecycle management, and access to Kore.ai’s AI-Agent Marketplace.
• Custom AI Solutions & Professional Services: Kore.ai generates substantial revenue through tailored AI chatbot and virtual agent projects for large enterprises like JPMorgan Chase. These bespoke solutions contribute around 20% of the company’s revenue, often based on one-off or contract-based projects.
• Data-as-a-Service: By leveraging anonymized chat logs and AI interaction data, Kore.ai trains specialized industry models and sells insights to third parties. This data-driven service provides a lucrative and sensitive revenue stream.
• AI Innovation & Multimodal Integration: Kore.ai continues to innovate, incorporating advancements such as GPT-5-powered dialogue management, visual AI responses, and AR/VR interfaces to stay competitive and grow market share.

Financial Overview

Kore.ai’s estimated annual revenue is around $525.8 million, with a revenue per employee of about $435,298. As of Q2 2023, the company has seen growth in its IoT connectivity segment, although IoT solutions have experienced some declines due to shifts in customer project demands. 

While adjusted EBITDA has increased, net losses have also risen, attributed to investments in growth, team expansion, and operational improvements.

The company handles approximately 450 million interactions daily, serving over 200 million consumers and 2 million enterprise users worldwide, reflecting the large scale and demand for its services.

Funding History:

Kore.ai has raised a total of $223.5 million across several funding rounds:

• Series B and Venture rounds (2018-2019), amounts undisclosed.
• Series C: $50 million in September 2021, aimed at scaling AI solutions.
• Debt Financing: $20 million in September 2021.
• Series C Extension: $3.5 million in November 2021, led by NVIDIA.
• Series D: A major round of $150 million in January 2024, led by FTV Capital, with participation from NVIDIA and existing investors like Vistara Growth and Sweetwater PE. This latest funding will support further market expansion and innovation in AI solutions.

With strong leadership and a growing global customer base, Kore.ai is well-positioned for future growth and innovation in AI-powered automation.

Over 50% of U.S. Companies Believe in Enterprise AI Assistants 

According to a seminal MIT Sloan study, over 50% of U.S. companies with 5,000+ employees have now adopted AI, with even higher rates in larger firms. This isn’t just about testing new tech; AI is becoming a central part of their operations. Large enterprises are using AI assistants to streamline tasks, improve decision-making, and enhance customer experiences. 

With AI in place, businesses can handle complex operations faster and more efficiently. As more companies embrace this technology, staying competitive requires integrating AI into everyday processes.

1. Automating and Elevating Customer Experience

The frontline of AI adoption is customer service. But we’ve moved far beyond simple, scripted chatbots.

The “Zero-Wait” Support Model

Enterprises are deploying sophisticated AI assistants that handle millions of routine inquiries instantly, order status, password resets, and appointment scheduling. This doesn’t just reduce costs; it achieves a “zero-wait” state for customers, dramatically boosting satisfaction.

24/7 Omnichannel Presence

These AI agents provide a consistent experience across web chat, mobile apps, social media, and voice, ensuring brand continuity and support anytime, anywhere.

For example, Amtrak’s “Julie” Virtual Assistant has been operational for over a decade, handling millions of customer inquiries each year. Julie answers questions, books tickets, and provides travel information. 

This AI assistant has been credited with a 25% increase in bookings per year and answering over 5 million questions annually, delivering an 800% ROI by freeing human agents to handle more complex issues.

2. Streamlining Complex Internal Operations

The most significant impact is happening inside the organization. AI assistants are becoming the central nervous system for enterprise workflows.

The Internal Help Desk, Reinvented

Employees no longer have to wait for IT or HR teams to handle routine requests. AI assistants can reset passwords, request software, provide HR policy details, or submit expense reports. The AI doesn’t just provide an answer; it executes the action by integrating seamlessly with systems like ServiceNow, Workday, and Slack.

Knowledge Management on Demand

With Agentic RAG, AI assistants can securely query vast internal knowledge bases, past project reports, and compliance documents to provide employees with precise, cited answers in seconds, turning collective organizational knowledge into an actionable asset.

3. Driving Data-Driven Decision Making

In large organizations, data is often siloed and underutilized. AI assistants are acting as a universal translator for enterprise data.

Natural Language Business Intelligence

Instead of requiring data experts to write complex SQL queries, executives and managers can simply ask the AI questions in plain English, such as, “What were the Q3 sales figures for the Midwest region compared to the forecast?” 

The AI queries data warehouses, synthesizes the information, and delivers a natural language summary with key insights, making data accessible to everyone.

For example, Coca-Cola’s Revenue Growth Management system leverages an AI-powered assistant to help managers quickly access and understand sales data. 

4. Optimizing Specialized Core Functions

AI is not a one-size-fits-all solution. Leading companies are deploying specialized agents for specific business units to drive efficiency in targeted areas.

• In HR: AI assistants screen candidates, schedule interviews, and answer FAQs about benefits, freeing up human resources for more strategic tasks, like talent development and employee engagement.
• In IT: AI assists in automating ticket routing, conducting initial diagnostics, and even executing remediation scripts for common issues.
• In Finance: AI helps process invoices, check for compliance, and generate financial reports, allowing finance teams to focus on more critical activities.

For example, Unilever’s HR recruitment process has been dramatically streamlined with the help of the AI assistant “Ulla.” Ulla screens thousands of graduate applications, conducts initial interviews via chat, and uses gamified assessments to identify top candidates. 

How to Develop an Enterprise AI Assistant Like Kore.ai?

We help enterprises design and deploy intelligent AI assistants that truly transform how people work and serve. Our approach combines practical strategy, strong architecture, and seamless integration to create solutions that last. Over the years, we have developed many AI assistants like Kore.ai for our clients, and each one has been built with care and purpose.

1. Define the Enterprise AI Ecosystem

We begin by understanding each client’s ecosystem, mapping out domains like Work, Service, and Process. Our team studies workflows across HR, IT, and Customer Service to identify automation opportunities. We then define clear data access and governance policies to ensure compliance and secure operations.

2. Design the Multi-Agent Architecture

We create specialized AI agents for each domain, such as HR, IT, or Finance. A central Supervisor Agent coordinates these agents to ensure smooth orchestration. Through context-sharing, the agents collaborate effectively, delivering unified and intelligent outcomes across the organization.

3. Build RAG & Governance Layer

Our team builds a secure retrieval-augmented generation layer that connects to enterprise data. Role-based access control and compliance filters protect sensitive information. With vector databases, we enable precise and context-aware data retrieval for smarter responses.

4. Enable LLM Agnosticism

We design platforms that support multiple large language models, ensuring flexibility and scalability. Using adapters for prompt optimization, we fine-tune model performance. Fallback mechanisms guarantee reliability even when one model underperforms.

5. No-Code + Pro-Code Framework

We empower users at all levels. Business teams can design AI workflows using intuitive no-code tools, while developers use SDKs for deeper customization. Every solution is thoroughly tested and evaluated before deployment to ensure quality and compliance.

6. Integrate Omnichannel Experience

We unify customer and employee experiences across channels like chat, voice, email, and portals. Our integrations include voice gateways for call automation and connectors for CRMs, EMRs, and ERPs. This ensures real-time synchronization and seamless communication across all touchpoints.

Common Challenges of an Enterprise AI Assistant 

Developing an enterprise AI assistant comes with its own set of challenges. After helping numerous clients tackle these hurdles, we’ve identified the most common issues organizations face and the best ways to overcome them. 

The key to a successful, scalable AI assistant lies in addressing these obstacles early on. Below, we’ll break down four critical challenges and our approach to solving them.

Challenge 1: Maintaining Context Across Specialized Agents

Your AI assistant works fine when the conversation stays simple, but once it needs to handle multiple tasks, like checking a shipping status and processing a return, it starts to lose track. The bot forgets previous details, asking the user to repeat information as it switches between different agents. This disrupts the user experience, making the bot feel more robotic and less intelligent.

Our Solution:

We ensure that the AI assistant maintains full awareness of the conversation by using a centralized context store. All user interactions, such as intents, entities, and conversation history, are logged in a single session object. This allows each agent to pick up where the last one left off, preserving the context.

How it works:

• Every user interaction is captured in a unified session object.
• When an agent finishes its task, it writes its results back into this store, and the next agent picks up the conversation with all the context it needs.

This system is further enhanced by Knowledge Graphs, which help the AI understand relationships between different data points, such as “User -> belongs to -> Department -> has access to -> System,” making interactions more intelligent and relevant.

Challenge 2: Integration with Outdated Systems

Many organizations still rely on legacy systems, such as old mainframes or custom databases, which are crucial but don’t integrate well with modern AI tools. Without the ability to communicate with these systems, an AI assistant is useless.

Our Solution:

Rather than trying to connect the AI directly to the legacy system, we create an abstraction layer that bridges the gap between old and new technologies.

How it works:

• We build API Gateways to manage requests to different backend systems securely.
• For systems without APIs, we develop microservices or use webhooks that can interact with the legacy systems through available interfaces like direct database connections or even terminal scripts.
• These services then expose modern REST APIs that the AI assistant can easily interact with, ensuring that the system works well with the existing infrastructure and is ready for future needs.

Challenge 3: LLM Bias and Inaccuracy

AI often makes confident but incorrect statements; this is known as “hallucination.” In an enterprise context, where accuracy is critical, a single incorrect response can erode trust and lead to costly errors, especially in areas like compliance or financial data.

Our Solution:

To ensure accuracy, we ground the AI in real, validated data and continually improve its performance.

How it works:

• Agentic RAG (Retrieval-Augmented Generation): This approach forces the AI to base its responses on trusted, enterprise-specific data, ensuring that it can only draw information from authorized knowledge bases, documents, or databases.
• Prompt Evaluation & Fine-Tuning: We continuously test and refine AI prompts to reduce ambiguity and bias.
• Reinforcement Learning from Human Feedback (RLHF): Feedback loops are built directly into the interface (e.g., thumbs up/down), helping the AI learn and adapt its responses over time, increasing its reliability.

Challenge 4: Balancing No-Code and Pro-Code

No-code platforms offer quick and easy tools for business teams to design simple workflows, but they fall short when more complex logic is needed. On the other hand, relying too much on developers can slow down the project, creating a bottleneck and a frustrating balance between speed and control.

Our Solution:

We combine the best of both worlds: no-code tools for rapid iteration and pro-code development for complex customizations.

How it works:

• Business Analysts use intuitive, no-code platforms to design core workflows, define intents, and manage common responses.
• Pro-Code Developers extend these flows, adding advanced integrations, custom logic, and data transformations where needed.

This hybrid development model ensures agility for the business teams (handling 80% of the work) while providing the developers with full control over complex functionality. With collaborative workflows, version control, and deployment pipelines in place, we ensure that both speed and precision are maintained.

Tools & APIs for an Enterprise AI Assistant Like Kore

Building an enterprise-grade AI assistant might seem complex, but you can think of it as layering intelligence on top of strong foundations. You would start with robust NLP frameworks that can truly understand context, add secure data pipelines that can handle sensitive information, and then integrate APIs that let the assistant interact seamlessly across platforms.

1. Core Programming Languages

A strong foundation begins with choosing the right programming languages for scalability, reliability, and performance.

• Python is the go-to language for building the intelligence layer of the assistant. Its extensive ecosystem of machine learning and NLP libraries makes it ideal for data analysis, custom model training, and intelligent reasoning.
• Node.js is perfect for creating high-performance API services and managing real-time communication. It powers orchestration layers, event-driven workflows, and integrations with third-party platforms.
• Java remains a powerhouse in enterprise systems, especially when integrating with legacy software. Its stability and mature ecosystem make it ideal for secure backend systems and API management.

Why it matters: Using a polyglot approach ensures flexibility, enabling teams to leverage the strengths of each language for specific tasks while maintaining performance and scalability.

2. AI, ML, and LLM Frameworks

This layer forms the brain of your AI assistant, powering its ability to understand, reason, and respond naturally.

• LLM Providers (OpenAI, Anthropic Claude, Meta Llama) offer state-of-the-art large language models that drive conversational intelligence. A model-agnostic approach lets you choose the best model for each task, whether it is GPT-4 for deep reasoning or Claude for safer, controlled responses.
• Hugging Face Transformers provides access to thousands of pre-trained models for tasks like intent detection, entity recognition, and summarization, allowing faster customization without building models from scratch.

Why it matters: Combining multiple AI and ML frameworks helps future-proof your system, prevents vendor lock-in, and enhances specialization across various use cases.

3. RAG Frameworks

To ensure factual and contextually relevant responses, RAG frameworks connect your AI assistant to enterprise-specific knowledge.

• LangChain and LlamaIndex help build powerful RAG pipelines by connecting LLMs with data retrievers and tools. LangChain enables modular orchestration, while LlamaIndex structures and indexes private data sources for more relevant context.
• Vector Databases (Pinecone, Weaviate) store and retrieve vector embeddings, numerical representations of enterprise knowledge, for lightning-fast, context-aware information retrieval.

4. Voice and NLP Engines

For voice-driven interactions, specialized engines are essential for converting speech into actionable intent.

• ASR Engines (Google Speech-to-Text, Amazon Transcribe) provide real-time, accurate transcription of speech into text, customizable for specific industries or jargon.
• NLU Frameworks (Rasa, Wit.ai) enable structured understanding of user intent and entities, offering a lightweight, open-source alternative or complement to large-scale LLMs for dialogue management.

5. Data Layer and Persistence

Efficient data management can truly define how smart your AI assistant feels. You might rely on PostgreSQL or MongoDB to organize data with speed and reliability, while Neo4j helps it understand complex connections. Elasticsearch will then ensure your assistant can quickly find insights and respond almost instantly when it matters most.

6. Security, Identity, and Access Control

Security will always sit at the core of any enterprise AI system because trust is non-negotiable. You could use OAuth or JWT to control identity flow while key vaults handle encryption with precision. When RBAC frameworks are applied correctly, your assistant can safely operate within strict access boundaries and maintain full compliance.

7. Enterprise System Integration APIs

An AI assistant can truly shine when it starts acting within enterprise systems instead of just talking about them. You might connect it with Salesforce or SAP so it can update records or check real-time data. With ServiceNow and EHR integrations, it could even automate workflows and deliver secure, context-aware support where accuracy really counts.

Conclusion

Enterprise AI assistants like Kore.ai are quietly transforming how automation scales across complex systems. True success will always depend on building a solid architecture that is secure, reliable, and well-orchestrated rather than just deploying another chatbot. You could explore how Idea Usher might help you design multi-agent frameworks with strong RAG pipelines and enterprise-ready AI systems that truly fit your business needs. Idea Usher’s experts can create and deploy one that is scalable, secure, and built to drive measurable growth.

Looking to Develop an Enterprise AI Assistant Like Kore?

At Idea Usher, we’re not just building AI for conversation. We create intelligent digital workforces that drive real business value. Our enterprise AI assistants don’t just answer questions. They automate key processes, understand context, and seamlessly integrate with your existing business systems.

Why Partner with Idea Usher?

• Elite Technical Expertise: Our team has over 500,000 hours of coding experience, with former MAANG/FAANG developers bringing top-tier engineering skills to the table. You can count on us for high-quality, reliable solutions.
• Architecture That Scales: We use technologies like Python, Node.js, RAG, and Vector DBs to ensure your assistant is secure, scalable, and built to evolve with your business needs.
• Seamless Enterprise Integration: Whether it’s Salesforce, ServiceNow, SAP, or custom APIs, we specialize in integrating AI into the tools your business already relies on, ensuring a smooth and powerful connection.

Ready to move from simple Q&A to automated, intelligent action?

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