Enterprise AI Orchestration
Designed multi-agent conversational workflows capable of coordinating CRM systems, AI agents, document automation, and enterprise integrations in real time.
Schedule a FREE Consultation Call with Our Experts
Email Us
[email protected]
Call Us (USA)
+1 952 800 2042
Call Us (INDIA)
+91 79 4898 8801
Cnnect unifies AI, CRM, documents, & enterprise workflows into one intelligent conversational platform.
Implemented Azure AI Foundry v2.0 Modern Agents to orchestrate conversational workflows across multiple enterprise systems. Enabled dynamic context injection, intelligent intent routing, and streaming AI responses for seamless conversational experiences. Integrated resilient retry handling and session-aware processing to maintain stability during high-concurrency enterprise operations.
Integrated asynchronous webhook processing with HubSpot CRM to automate proposal creation workflows based on deal-stage triggers. Generated structured SharePoint folder hierarchies and dynamically populated DOCX proposal templates using CRM data and AI-generated summaries. Streamlined enterprise proposal operations while reducing manual document preparation efforts.
Developed secure OAuth handling directly inside conversational workflows using the Model Context Protocol (MCP). Enabled users to authenticate with third-party platforms like HubSpot without interrupting ongoing AI conversations. Maintained seamless workflow continuity by resuming paused operations immediately after successful authorization.
Integrated Microsoft Teams and Graph API services to deliver proactive workflow notifications, Adaptive Cards, and enterprise alerts directly within Teams conversations. Enabled automated user resolution, deep-linked workflow actions, and real-time operational updates across enterprise teams. Improved collaboration visibility while reducing dependency on manual communication.
Implemented Azure AI Search-powered Retrieval-Augmented Generation (RAG) workflows for intelligent document indexing and contextual AI retrieval. Enabled users to upload multiple documents, query content conversationally, and receive highly accurate responses across session-scoped files. Improved retrieval speed and accuracy through parallel indexing and hybrid ranking mechanisms.
Engineered XML-level DOCX editing workflows capable of updating document content while preserving original layouts, branding, fonts, tables, and formatting structures. Ensured AI-generated text inherited existing style properties for visually consistent enterprise documents. Added version-controlled recovery mechanisms to safely restore previous document versions when required.
Implemented a global CRM lookup cache with asynchronous refresh handling to optimize HubSpot property retrieval and improve UI responsiveness. Reduced API rate-limiting issues by maintaining portal-level cached CRM properties and refreshing them through background synchronization processes. Enabled high-speed enterprise CRM queries across large operational datasets.
Designed a tiered memory architecture combining in-memory session handling, Azure Table Storage persistence, and Azure AI Search-based long-term retrieval. Implemented sliding-window summarization and lazy session rehydration to optimize token usage and maintain stable AI performance during multi-hour conversations. Reduced latency and stabilized enterprise-scale conversational workloads through intelligent session lifecycle management.
Designed multi-agent conversational workflows capable of coordinating CRM systems, AI agents, document automation, and enterprise integrations in real time.
Built asynchronous webhook-driven automation pipelines capable of orchestrating proposal generation, project provisioning, and enterprise notifications across multiple systems.
Implemented OAuth and MCP-driven authentication flows to maintain secure access across HubSpot, Microsoft Teams, SharePoint, and enterprise APIs.
Engineered session-scoped RAG indexing, hybrid retrieval ranking, and intelligent document processing workflows for high-accuracy conversational AI responses.
Developed XML-level document editing capabilities preserving enterprise branding, layouts, formatting, and content structures during AI-driven modifications.
Leveraged asynchronous FastAPI endpoints, Azure storage systems, and scalable AI orchestration patterns to support enterprise-scale conversational workloads.
Automated proposal generation workflows reduced manual effort by dynamically creating SharePoint structures, populating DOCX templates, and generating AI-driven summaries directly from CRM events.
Unified CRM operations, document handling, project onboarding, and enterprise notifications into a conversational interface, minimizing repetitive administrative tasks.
Implemented tiered memory management, session summarization, and Azure-backed persistence to maintain fast response times and predictable token usage during multi-hour AI sessions.
Enabled highly accurate conversational retrieval across multiple uploaded documents through session-scoped indexing, hybrid ranking, and parallel retrieval strategies.
Delivered XML-level document editing workflows that maintained original fonts, layouts, tables, spacing, and branding while allowing AI-generated content updates.
Integrated secure in-chat OAuth handling and MCP execution to enable smooth authentication across HubSpot and enterprise services without disrupting conversations.
Enabled proactive Microsoft Teams communication with Adaptive Cards, workflow alerts, project provisioning updates, and deep-linked enterprise actions.
Built a decoupled, asynchronous architecture capable of supporting concurrent AI sessions, enterprise integrations, RAG indexing, and workflow automation at scale.
