Oracle · Microsoft · Google · AWS — Four Platforms, One Partner
Chatbots and voice agents that go beyond answering questions — they take action. eAppSys builds enterprise conversational AI across Oracle, Microsoft, Google, and AWS platforms, connecting assistants to your real business systems so they can complete transactions, trigger workflows, and resolve issues end-to-end.
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Conversational AI Development
Your customers ask the same questions hundreds of times a day. Your employees spend hours finding information that should be instant. Your contact centre handles calls that could be resolved in seconds by an AI that knows your systems. Conversational AI solves all three — and eAppSys builds them across Oracle, Microsoft, Google, and AWS so they fit the technology your business already runs on.
The chatbot era is over. What's replacing it is conversational AI that understands what someone means regardless of how they phrase it, maintains context across a multi-turn conversation, and connects to your live business systems to actually do something — not just reply. In 2026, all four major enterprise platforms have moved from rigid intent trees to LLM-powered reasoning. Gartner projects conversational AI could cut contact centre agent labour costs by approximately $80 billion by 2026. The question is no longer whether to deploy it — it's which platform fits your stack, and how to build it properly.
Oracle introduced Ask Oracle — a universal natural-language assistant embedded directly into Oracle Fusion Cloud Applications — at Oracle AI World Las Vegas, October 2025. Unlike standalone chatbot platforms that require integrations, Ask Oracle has native access to Oracle ERP, HCM, SCM, CX, and EPM data and workflows from day one. It sits inside the new AI-first Fusion user experience alongside rapid typeahead search, AI-driven recommendations, and a minimalist role-based workspace designed to surface only what each user needs.
“Integrated AI-powered chat helps your organisation enhance productivity and reduce support costs by integrating AI-powered digital assistant capabilities that improve access to information and execute actions.”
Microsoft Copilot Studio’s May 2026 update marks a significant evolution. As Nitasha Chopra (VP & COO, Microsoft Copilot Studio) described it, teams want to move “beyond conversational experiences to systems that can help get work done by interacting with applications, executing workflows, collaborating across tools, and supporting customers more naturally across channels.” The May 2026 release brings computer-using agents GA, a new workflow experience, real-time voice agents now generally available in Dynamics 365 Contact Center, and a new orchestration layer that improves evaluation performance by approximately 20% while decreasing token consumption by 50%.
“A new orchestration layer in Copilot Studio improves how agents execute business processes with greater accuracy and efficiency… improving evaluation performance by approximately 20% while decreasing net token consumption by 50%.”
Google CX Agent Studio is Google’s next-generation conversational AI platform, powered by Gemini and now part of Gemini Enterprise for Customer Experience (consolidated at Google Cloud Next 2026). As Google Cloud describes it directly: “CX Agent Studio provides a next-generation platform powered by Gemini to rapidly build, evaluate, and deploy highly personalized conversational agents in days versus weeks.” Dialogflow CX was migrated to the unified Conversational Agents console in October 2025, now running on Gemini 2.5 Flash by default. The platform supports multimodal conversations — voice, chat, and image — across 40+ languages.
“Deliver consistent and seamless customer experiences across web, mobile, voice, email, social channels and apps. Out-of-the-box connectors and MCP support let users integrate backend systems, proprietary data sources and applications.”
Best fit: Google Workspace organisations, contact centres needing 40+ language voice agents, or businesses integrating with existing CCaaS platforms (Genesys, Avaya, Cisco) using Google’s AI layer.
Amazon Lex V2 — the same technology that powers Amazon Alexa — is a fully managed conversational AI service with advanced NLU and generative AI capabilities powered by Amazon Bedrock. In 2026 it is a fundamentally different product from its origins: Bedrock GenAI integration, Assisted NLU (dramatically reducing required training data), QnA Intent (eliminating the need to pre-program every question), and Amazon Nova Sonic for speech-to-speech interactions. AWS describes it simply: “Amazon Lex is natively integrated with Amazon Connect, AWS’ omnichannel cloud contact center enabling developers to build conversational bots that can handle customer queries over chat or phone.” MCP support in Amazon Connect lets flow modules become AI agent tools without recoding.
“Support of Model Context Protocol (MCP) allows AI agents in Amazon Connect to directly access and use enterprise systems and tools during customer interactions… Designers can convert existing Amazon Connect flow modules into tools that AI agents autonomously invoke, leveraging existing business logic without extensive recoding.”
We build conversational AI that works consistently wherever your customers and staff interact — with context that travels with the user when they switch channels:
Talk to our Oracle EBS experts today
Map the conversations that matter most — where are the highest volumes, the most repetition, the biggest cost?
Choose the right platform for your stack, design conversation flows, define intents, and plan system integrations.
Build the agent, connect it to your knowledge base and backend systems, configure NLU and RAG.
Simulation testing, conversation evaluation, accuracy review, edge case handling, and compliance check.
Go live with analytics from day one, then iterate based on real conversations and satisfaction scores.
Certified Across All Four Platforms
Oracle, Microsoft, Google, and AWS — we give you independent advice on which platform fits, not one we’re incentivised to recommend.
We Know Your Systems Already
200+ Oracle professionals plus deep Microsoft and AWS expertise. The integrations between your conversational AI and your business applications are our daily work.
We Measure What It Delivers
Self-service resolution rate, deflection from live agents, response accuracy, customer satisfaction — we configure analytics from day one so you can see the impact.
We’re There After Go-Live
Conversational AI improves with use. Our managed support includes conversation analysis, monthly tuning, new intent training, and platform update adoption.
Your customers shouldn’t have to wait on hold for a question an AI could answer instantly. Your employees shouldn’t spend time on queries a properly connected assistant could handle in seconds. eAppSys builds the conversational AI that closes that gap — on the platform that fits your business, with the integrations that make it actually useful.
One use case per platform — with the source and the outcome, not just the concept.
The questions we get asked most before a conversational AI project starts.
A basic chatbot follows a fixed script — it can only respond to questions it was explicitly programmed to handle, and fails the moment a user phrases something differently. Conversational AI uses natural language understanding (NLU), large language models (LLMs), and retrieval-augmented generation (RAG) to understand what someone means, maintain context across a multi-turn conversation, and connect to live business systems to take action. Modern enterprise platforms — Oracle Digital Assistant, Microsoft Copilot Studio, Google CX Agent Studio, Amazon Lex with Bedrock — move far beyond scripts to genuine dialogue that can resolve issues end-to-end.
Oracle Digital Assistant (Ask Oracle) is the best fit for Oracle Fusion Cloud customers — native access to ERP, HCM, SCM, and CX with no integration overhead and no additional licence cost. Microsoft Copilot Studio (May 2026) is ideal for Microsoft 365 and Dynamics 365 environments — Teams, SharePoint, Dynamics, and voice agents now GA. Google CX Agent Studio (Gemini Enterprise) is strongest for Google Workspace organisations, 40+ language voice contact centres, or CCaaS deployments with Genesys and Avaya. Amazon Lex with Amazon Connect is the natural choice for existing AWS infrastructure and contact centre deployments. eAppSys advises independently across all four.
RAG (Retrieval-Augmented Generation) allows a conversational AI to search your actual company knowledge — policies, product documentation, procedures, contracts — and use that to answer questions accurately, rather than relying on a fixed model that might be out of date or make things up. The agent can show users exactly which document an answer came from. All four enterprise platforms support RAG: Oracle OCI Generative AI Agents, Microsoft Azure AI Search over SharePoint, Google CX Agent Studio data stores, and Amazon Kendra with Lex.
Yes — voice is now a first-class channel on all four platforms. Microsoft Copilot Studio introduced real-time voice agents in May 2026, now generally available in Dynamics 365 Contact Center. Google CX Agent Studio supports voice with human-like voices in 40+ languages and direct audio-to-audio (A2A) translation in 10 core languages. Amazon Lex powers IVR through Amazon Connect using Alexa-grade voice accuracy with 8kHz telephony audio sampling, with Nova Sonic for speech-to-speech. Oracle Digital Assistant supports voice within Oracle Fusion Applications. eAppSys designs agents that maintain context when a customer moves from chat to voice or email.
Simple, focused assistants — such as an HR policy chatbot, order status agent, or IVR replacement — can be live in 2–4 weeks using prebuilt templates. Google CX Agent Studio offers 35 prebuilt agent templates. Microsoft Copilot Studio uses generative AI conversation boosters to dramatically reduce build time. More complex deployments — voice, multiple channels, deep system integrations, custom NLU training — typically take 6–10 weeks. Gartner estimates conversational AI could cut contact centre agent labour costs by approximately $80 billion by 2026.
We implement multiple accuracy safeguards: RAG grounds answers in your actual documents rather than LLM general knowledge; content safety thresholds block harmful or off-topic responses; confidence thresholds route low-certainty answers to a human agent rather than guessing; source citations show users exactly where an answer came from; and we use platform-native evaluation tools — Google CX Agent Studio’s multimodal conversation simulator and automated evaluation, Microsoft Copilot Studio’s test sets and runtime tracing, and Amazon Lex’s built-in analytics — to test extensively before go-live. Conversation logs are reviewed and used to improve accuracy over time.
