Building an AI-Native Website Platform with Sanity, Next.js, AI Agents, CRM & Sales Automation

How We Built an AI-Native Website with Headless CMS, AI Agents, CRM & Lead Automation

Industry: Information Technology | Software Development | Digital Experience Platforms

Client / Project: Murmu Software Infotech — AI-Native Corporate Website & Business Growth Platform

Project Status: Live | Production Website | Ongoing Enhancements

Project Summary:

Murmu Software Infotech modernized its own website from a developer-dependent Next.js and custom .NET administration setup into an AI-native platform using Sanity CMS and Next.js. The platform connects structured publishing, AI content workflows, intelligent chatbots, lead qualification, CRM processes, automated email and WhatsApp follow-ups, and meeting scheduling with human experts.

Project Snapshot

Live Project: https://murmusoftwareinfotech.com

Technology Stack: Sanity CMS, Next.js, React, GROQ, JavaScript/TypeScript ecosystem, Node.js, OpenAI, Google Gemini, MCP, Supabase, Microsoft Graph API, Azure authentication, REST APIs and business automation integrations.

Executive Summary

Murmu Software Infotech needed to transform its corporate website into a more efficient publishing, customer engagement and business development platform. Its previous architecture combined Next.js with custom .NET APIs and an internal administration system, making routine content changes dependent on developers.

We migrated content management to Sanity while retaining Next.js, introduced reusable content schemas and connected AI-oriented knowledge workflows through MCP. The broader platform incorporates AI-powered customer assistance, lead qualification, CRM processes, automated follow-ups and expert meeting scheduling.

The documented result was a reduction in many routine publishing tasks from approximately 2–4 hours to seconds or minutes, alongside greater editorial independence and a more extensible digital architecture.

Key Results at a Glance

Additional implemented capabilities by the project team: AI customer conversations, CRM-connected lead qualification, automated email and WhatsApp follow-ups, and scheduling with human experts.

These capabilities strengthen the platform's conversion workflow. Separate operational reporting is needed to quantify their effect on lead conversion, response time and meeting volume.

Problem, Solution & Implementation

Business Challenges

Challenge 1: Developer-Dependent Website Publishing

The previous website used Next.js with custom .NET APIs and an internally developed content administration layer.

Even ordinary changes to service pages, article content, website sections or metadata often required developer participation.

Cause: Content management and application logic were too closely connected.

Operational impact: Routine updates could take approximately 2–4 hours, slowing marketing execution and consuming engineering time.

Affected users: Content editors, marketing teams and developers.

Challenge 2: Limited Flexibility for Growing Content Requirements

As the website expanded, it needed to manage more service pages, blogs, case studies, SEO landing pages, business information and promotional sections.

The custom administration system required additional development whenever content requirements evolved.

Cause: Limited reusable content structures and insufficient editorial flexibility.

Operational impact: Slower publishing, more implementation effort and less consistency across growing website content.

Affected users: Marketing managers, SEO specialists, content teams and technical administrators.

Challenge 3: Website Knowledge Was Not Fully Reusable by AI

A business website contains valuable information about services, expertise, solutions, pricing approaches and technical capabilities.

When this information is tied too closely to frontend pages or custom administrative logic, reusing it for AI assistants becomes more difficult.

Cause: A content architecture not originally optimized for structured AI access.

Operational impact: Greater effort maintaining consistent business knowledge across website pages and AI experiences.

Affected users: Website visitors, content administrators and AI development teams.

Challenge 4: Customer Enquiries Needed More Intelligent Handling

Traditional contact forms and manually managed conversations do not always capture the context needed to understand a prospective customer's requirements.

Visitors may ask questions outside normal business hours, need cost guidance or want specialist advice before deciding to book a consultation.

Cause: Conventional enquiry journeys rely heavily on manual discovery and follow-up.

Operational impact: Repetitive conversations, additional sales coordination and potential delays between enquiry and response.

Affected users: Prospective customers, marketing staff, customer support and sales experts.

Challenge 5: Lead Nurturing Required Connected Automation

A successful customer journey often involves several stages: initial website discovery, questions, requirement collection, follow-up and final consultation.

Managing these stages across disconnected messages, contact forms and internal records creates unnecessary coordination work.

Cause: Lead information and customer communication need consistent integration with CRM and scheduling systems.

Operational impact: Greater manual effort and reduced visibility into enquiry status and next actions.

Affected users: Sales representatives, CRM administrators and business development teams.

Project Objectives

The project aimed to transform MSI's website from a primarily content-publishing system into an integrated AI-native digital business platform.

The core objectives were:

1. Modernize content management: Replace developer-dependent administration with an editor-friendly headless CMS.
2. Accelerate publishing: Allow marketing teams to publish and update content without routine code changes.
3. Improve content reusability: Manage services, case studies, FAQs, articles, SEO metadata and landing pages through structured models.
4. Connect AI with business knowledge: Make approved website content accessible to AI assistants, MCP tools and content agents.
5. Strengthen search readiness: Support SEO, Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) through better content structure.
6. Automate customer engagement: Use an intelligent chatbot to answer questions, identify needs and capture enquiries.
7. Connect CRM and lead workflows: Organize qualified enquiries and support personalized follow-ups through email and WhatsApp.
8. Simplify expert consultations: Enable customers to progress from a website conversation to a scheduled meeting with a human specialist.

The goal was not simply a redesigned website. It was to create a more flexible content and customer engagement architecture that could continue evolving with the business.

Our Solution

We modernized the website using Sanity Headless CMS as the structured content management layer while retaining Next.js and React for frontend delivery. Instead of managing content through the previous custom administration system, our team implemented reusable schemas for website pages, services, articles, case studies, FAQs, metadata and calls to action.

GROQ queries connect the CMS to the Next.js application, enabling content to be retrieved and presented through reusable frontend components. MCP-based access and AI content workflows extend the structured content foundation beyond traditional publishing.

We also connected AI-powered customer engagement capabilities, including business-aware conversations, lead qualification, CRM processes and scheduling workflows. The broader integration described by the project team incorporates automated email and WhatsApp follow-ups to maintain communication after the initial enquiry.

Human experts remain part of the process for technical discovery, complex questions and commercial decisions.

The result is a production website platform supporting content operations, AI-enabled customer assistance and connected sales workflows rather than treating these as entirely separate systems.

Major Features & Modules

Module 1: AI-Native Headless Content Management

Features:

• Sanity Studio content administration.
• Structured schemas for services, articles, case studies and FAQs.
• Reusable page sections and promotional content.
• Editable SEO fields and content metadata.
• GROQ-powered content retrieval.
• Independent editorial workflows.

Business benefit: Marketing teams can publish and maintain content more efficiently while reducing routine developer involvement.

Module 2: Next.js Website Experience

Features:

• Next.js and React frontend.
• Reusable page components and layouts.
• Responsive website experiences.
• Dynamic content rendering.
• Technical SEO and metadata handling.
• Integration-ready application architecture.

Business benefit: The company retains control over design, performance, functionality and user experience without coupling every content update to a software release.

Module 3: AI Content Agent & MCP Integration

Features:

• Structured CMS content as AI-accessible business knowledge.
• MCP-based content access.
• AI-assisted content creation and updates.
• Schema-aware content operations.
• Support for content enrichment and structured information reuse.

Business benefit: Website content becomes more useful across publishing, AI assistance and future automation workflows.

Module 4: SEO, AEO & GEO Content Management

Features:

• Editable page titles and descriptions.
• Structured FAQs and key facts.
• AI summaries and direct-answer content fields.
• Internal linking and content relationships.
• SEO-ready landing-page structures.
• Structured data integration where appropriate.

Business benefit: Teams can manage search-focused information more consistently and prepare content for traditional and AI-driven discovery.

Important distinction: These capabilities improve the platform's optimization foundation; they do not independently guarantee search rankings or AI citations.

Module 5: AI Chatbot & Intelligent Customer Assistance

Features:

• Conversational AI using OpenAI and Gemini.
• Answers informed by business-specific knowledge.
• Customer requirement discovery.
• Custom API-connected business tools.
• Project cost and hiring estimation utilities.
• Voice-enabled interaction.
• Human support escalation.

Business benefit: Visitors can receive immediate assistance and move from basic questions to more meaningful business actions.

Module 6: AI Lead Qualification & CRM Workflows

Features:

• Website enquiry and contact capture.
• Conversational requirement collection.
• Identification of customer needs and service interests.
• CRM-connected lead records.
• Lead status and follow-up management.
• Transfer of relevant enquiry context to business teams.

Business benefit: Sales teams can work from more structured enquiry information instead of starting every conversation without context.

Module 7: AI Email & WhatsApp Follow-Up Automation

Features:

• Follow-up workflows connected with lead activity.
• AI-assisted personalized communication.
• Email and WhatsApp engagement channels.
• Enquiry reminders and next-step communication.
• Continued lead nurturing and human follow-up.
• CRM workflow updates.

Business benefit: Helps reduce repetitive communication work and maintain continuity after the initial enquiry.

Follow-up workflows should enforce channel-specific consent, approved WhatsApp messaging requirements and unsubscribe or opt-out preferences. Channel providers and production configuration should be confirmed before publishing detailed implementation claims.

Module 8: Human Expert Meeting Scheduling

Features:

• Meeting requests initiated from customer engagement workflows.
• Microsoft Graph API integration.
• Azure-based authentication.
• Scheduling workflows.
• Human consultation handoff.
• Calendar-based meeting coordination.

Business benefit: Qualified visitors have a more direct route from an enquiry to a conversation with an experienced technical or business expert.

How the System Works

The platform connects two major workflows: content operations and customer conversion.

Workflow A: AI-Native Content Publishing

Marketing Editor → Sanity Studio → Structured Content → GROQ / MCP → Next.js Website + AI Knowledge Workflows

1. A content editor creates or updates a service page, article or case study in Sanity.
2. The structured content model stores reusable business information and metadata.
3. GROQ enables the Next.js application to retrieve the required content.
4. Published information appears on the website through frontend components.
5. MCP-connected AI workflows can access relevant structured information for approved tasks.
6. Search engines and website visitors can discover the published content.

Roles: Content Editor, SEO Specialist, Developer, AI Workflow.

Integrations: Sanity CMS, GROQ, Next.js, MCP and AI content tools.

Workflow B: Visitor-to-Expert Conversion

Website Visitor → AI Chatbot → Lead Qualification → CRM → Email / WhatsApp Follow-Up → Meeting Scheduling → Human Expert

1. Visitor discovery: A potential customer reaches the website through search, referral or direct navigation.
2. AI engagement: The chatbot answers initial business questions using relevant company knowledge.
3. Requirement discovery: The conversation identifies the customer's service needs, project requirements and interest in consultation.
4. Lead capture: Contact details and enquiry context are collected.
5. CRM workflow: The enquiry is transferred into the connected lead-management process.
6. Follow-up automation: Appropriate email or WhatsApp communication continues the customer journey.
7. Meeting scheduling: A sales-ready customer can proceed toward a scheduled consultation.
8. Human engagement: A company expert handles detailed requirements, solution planning and commercial discussion.

Roles: Website Visitor, AI Assistant, CRM Workflow, Marketing Team, Sales Expert.

Integrations: AI Chatbot, Business APIs, CRM, Email, WhatsApp, Microsoft Graph and Scheduling.

Infographic recommendation: Present these two connected workflows as a clean, horizontal diagram with a shared central website platform. Use one visual hierarchy, limited labels and no decorative icon clusters.

Before vs. After

Transformation Summary

Before: A functional website where routine publishing and evolving content needs created ongoing engineering work.

After: A headless, structured and AI-connected website platform supporting independent publishing, customer engagement and business automation.

Technical Architecture

The platform follows a modular architecture separating content management, frontend experiences, AI integrations and business workflows.

Frontend Layer

Technologies: Next.js, React.

Responsibilities:

• Public website presentation.
• Responsive page rendering.
• Reusable UI components.
• Dynamic content integration.
• SEO metadata and frontend navigation.
• Customer-facing AI interaction.

Content Management Layer

Technologies: Sanity CMS, Sanity Content Lake, GROQ.

Responsibilities:

• Structured business content storage.
• Editorial content management.
• Reusable content relationships.
• Service, blog and case study publishing.
• SEO and AI-oriented content fields.
• Content delivery to frontend applications.

AI & Agent Integration Layer

Technologies: OpenAI, Google Gemini, MCP and connected content tools.

Responsibilities:

• Business knowledge-grounded responses.
• Conversational customer assistance.
• AI content operations.
• Tool and function-based actions.
• Access to approved structured content.
• Human support handoff.

Backend & Business API Layer

Technologies: Node.js APIs and custom REST integrations in the documented chatbot system.

Responsibilities:

• Backend workflow orchestration.
• Business tool integration.
• Chatbot actions.
• Lead-processing logic.
• CRM and scheduling integration.
• Communication workflow triggers.

Data & CRM Layer

Technologies: Supabase for the documented chatbot solution; CRM and lead-management systems connected to the broader platform.

Responsibilities:

• Conversation and workflow-related records.
• Captured customer enquiries.
• Lead qualification context.
• Lead management and follow-up status.
• Customer engagement information.

The specific CRM provider, data model and deployment boundaries should be confirmed from the production implementation.

Scheduling & Communication Layer

Technologies: Microsoft Graph API, Azure authentication, email and WhatsApp integrations.

Responsibilities:

• Expert meeting coordination.
• Calendar-related workflows.
• Customer follow-up communication.
• Lead nurturing.
• Scheduling handoff.

Hosting & Deployment

The public website is deployed and operational. The exact hosting provider, CI/CD pipeline, CDN arrangement, production environments and disaster recovery configuration have not been disclosed in the available project documentation.

These should be added from the actual infrastructure records before publication.

Architecture Summary

Sanity Content Lake → GROQ → Next.js Website

Sanity Content → MCP → AI Content and Knowledge Workflows

Website → AI Chatbot → Business APIs → CRM / Automation → Microsoft Graph Scheduling → Human Expert

Engineering Challenges & Solutions

Engineering Challenge 1: Separating Content from Application Logic

Problem: The original content administration approach required developers to participate in many routine publishing tasks.

Our approach: We introduced Sanity as the structured content layer while retaining Next.js as the frontend application.

Outcome: Content editors gained more independence, and routine publishing became significantly faster.

Engineering Challenge 2: Designing Reusable Content Schemas

Problem: Different website content types needed flexibility without creating separate custom administration logic for every new page or section.

Our approach: We organized services, blogs, case studies, FAQs, promotional sections and metadata into reusable Sanity content models.

Outcome: Content became easier to manage, relate and reuse across website experiences.

Engineering Challenge 3: Connecting CMS Knowledge With AI

Problem: AI assistants needed relevant and maintainable business context rather than relying solely on general language-model knowledge or separately maintained documents.

Our approach: We moved the chatbot toward CMS-driven knowledge access and connected structured website information with MCP-oriented workflows.

Outcome: The architecture established a shared content foundation for publishing and AI-assisted experiences.

Engineering Challenge 4: Preserving Human Expertise in AI Workflows

Problem: Automated assistance cannot independently resolve every technical, commercial or complex customer enquiry.

Our approach: The AI engagement solution incorporated lead qualification, custom business tools, human escalation and scheduling integrations.

Outcome: Automation can handle initial interactions while specialists remain available for deeper discussions and business decisions.

Security, Integrations & Scalability

Security and scalability were relevant to this implementation because the platform connects public website content, AI systems, customer conversations and business workflows.

Documented Architectural Controls

Structured content separation: Sanity manages website content separately from the Next.js presentation layer, allowing the application and CMS to evolve independently.

Authenticated scheduling integration: The documented AI engagement implementation uses Azure authentication with Microsoft Graph API for scheduling-related operations.

Business API integration: Custom APIs connect AI conversations to business utilities and application workflows.

Human escalation: Complex enquiries can be handed to human operators rather than relying exclusively on automated responses.

Permissions & Data Governance

Sanity CMS access, CRM staff permissions and customer conversation access require appropriate authorization boundaries.

The production implementation should be documented with its actual role definitions, authentication providers, API authorization approach and audit logging configuration.

No unverified claim is made here about specific security certifications, encryption implementations or completed compliance audits.

Customer Data & Communication

Lead capture and follow-up workflows should maintain clear consent records, minimize unnecessary personal information, enforce appropriate CRM access and respect email and WhatsApp communication preferences.

AI-generated communications also require safeguards against incorrect customer details or unsupported commitments.

Reliability & Scalability

The separation of CMS, frontend, AI services and business APIs allows individual parts of the platform to be maintained or scaled according to demand.

Further technical documentation should confirm actual production measures for caching, monitoring, retry handling, rate limits, backups, incident recovery and deployment automation.

Business benefit: A modular architecture provides greater flexibility for future website enhancements and integrations without requiring the entire platform to be rebuilt.

Evidence, Growth & Conversion

Business Outcomes & ROI

The strongest verified business impact is the change in website publishing operations.

Outcome 1: Faster Routine Content Publishing

Previously: Routine content updates often required approximately 2–4 hours.

After migration: Many routine updates could be created and published within seconds or minutes, depending on complexity.

Business value: Faster content execution and less time waiting for routine development work.

Outcome 2: Greater Marketing Independence

The content team can manage many services, articles, FAQs, metadata fields and reusable website sections directly in Sanity.

Business value: More editorial control, fewer routine development requests and more flexible campaign execution.

Outcome 3: Better Structured Content Reusability

The website now manages structured information that can be delivered through Next.js and accessed by AI-oriented workflows.

Business value: A more maintainable information architecture and a stronger foundation for consistent business knowledge across channels.

Outcome 4: Connected AI Customer Engagement

The broader implemented platform includes knowledge-grounded AI conversations, business tools, lead qualification, human escalation and meeting scheduling.

Business value: A more connected enquiry journey, reducing the need for visitors to navigate entirely separate assistance and booking processes.

Outcome 5: CRM and Follow-Up Automation

The project team describes integrated CRM workflows and AI-powered email and WhatsApp follow-up capabilities.

Business value: Improved continuity between enquiry capture, lead handling and customer communication.

The number of follow-ups triggered, response rate and resulting sales conversion require production reporting.

ROI Measurement

This project is an internal production implementation, and no audited financial ROI has been published.

For a future quantified ROI report, MSI can measure:

• Publishing time savings: Number of routine content updates × average handling time saved.
• Engineering efficiency: Reduction in developer hours spent on editorial tasks.
• Marketing throughput: Number of content updates and campaigns published per month.
• AI operational efficiency: Number of enquiries handled or assisted without repetitive manual responses.
• Lead qualification: Percentage of captured enquiries meeting agreed qualification criteria.
• Sales effectiveness: Qualified enquiries, booked meetings, attended consultations and resulting opportunities.
• Automation performance: Email and WhatsApp follow-up delivery, responses, consent compliance and meaningful next actions.

The case study demonstrates measurable publishing efficiency and documented functional improvements. Additional analytics are needed to establish attributable revenue growth or financial payback.

Client Feedback & Testimonials

Client: Murmu Software Infotech

Project Relationship: Internal implementation / First-party case study

Project Ownership: MSI's product, engineering, content and business development teams.

Because MSI developed the platform for its own operations, the project serves as an internal transformation case study rather than an independent external customer engagement.

The published experience demonstrates a clear operational improvement: content editors gained more direct control over publishing, reducing their dependence on developers for routine updates.

Approved Client Testimonial: Not supplied. Add a signed, approved statement from an internal project owner or business stakeholder if available.

Live Project Reference: Murmu Software Infotech Website

Supporting Project References:

• Original Sanity + Next.js Migration Case Study
• AI Chatbot with OpenAI, Gemini & Human Support

For prospective buyers, a guided live demonstration and approved product screenshots can provide stronger evidence than an unverified testimonial.

Future Enhancements

The existing platform provides a foundation for further development. The following are possible enhancements, not claims of completed functionality.

Advanced AI Content Operations

Introduce more automated editorial research, content quality review, duplicate-content detection, structured content recommendations and approval workflows.

Multilingual AI Website Experiences

Expand content and AI engagement to additional languages and country-specific experiences for international markets.

Advanced CRM Intelligence

Explore predictive lead scoring, account enrichment, opportunity insights and AI-generated sales summaries with appropriate human oversight.

Marketing Attribution & Conversion Analytics

Connect search, paid campaigns, website behavior, chatbot engagement, CRM records and attended meetings into more complete conversion reporting.

AI-Powered Personalization

Use approved customer context and explicit business rules to personalize relevant website content and service recommendations.

Broader Enterprise Integrations

Extend the platform through approved integrations with ERP, customer portals, support desks, ecommerce platforms and internal business applications.

Workflow Reliability Improvements

Expand observability, message delivery monitoring, retry handling, incident reporting and AI workflow quality evaluation as operational requirements evolve.

The roadmap should prioritize enhancements based on actual user behavior, business needs, system reliability and measurable value.