AI Document Automation System
Extracts data from documents, validates fields, generates summaries and triggers email or workflow actions.
Anonymized Case Studies
AI, GenAI, RAG, MCP and Agentic AI solution examples
Explore representative implementation patterns without client names. These examples show how Munimax can support AI automation, enterprise search, tool integration, multi-agent orchestration and operational intelligence projects.
Project Examples
Sample solution types Munimax can deliver
These examples help customers understand the practical project work we can support through freelance, fixed-scope or retainer engagement.
Legacy System Modernisation
Converts old desktop, spreadsheet or manual systems into web applications with APIs and modern dashboards.
Business Dashboard & Reporting Portal
Tracks finance, sales, operations, project or support metrics with role-based access and automated reporting.
MCP & Agentic AI Proof of Concept
Connects AI assistants with databases, APIs, files and internal tools using MCP and agentic workflow patterns.
Case Study Summary
Representative AI implementation examples
Each case study below describes the business problem, solution approach, tech stack and expected outcomes.
Enterprise RAG Knowledge Assistant
AI assistant for policy, SOP, contract, project and support document search with source-grounded answers.
View case study →Vector-less RAG Search Assistant
Search and answer system using structured metadata, SQL, BM25/full-text search and reranking without vector DB dependency.
View case study →MCP Tool Integration Layer
Secure MCP server for connecting AI assistants to APIs, files, databases and internal business tools.
View case study →Agentic AI Workflow Automation
AI agent that plans tasks, calls tools, validates outputs and routes approval requests to human users.
View case study →Multi-Agent Orchestrator
Coordinator-agent pattern with specialist agents for analysis, search, validation, generation and execution.
View case study →AIOps Incident Intelligence
AI-driven incident summarisation, alert enrichment, root-cause hints and runbook recommendations.
View case study →Legacy Monolith Modernisation
Modernising a tightly coupled legacy application into modular services with APIs and improved deployment practices.
View case study →Desktop to Web Migration
Converting a Windows/desktop-based internal application into a browser-based system with role-based access.
View case study →Cloud Re-platforming
Migrating legacy workloads to cloud hosting with CI/CD, monitoring, backup and security improvements.
View case study →API Enablement Layer
Exposing legacy business functions through secure APIs to enable integrations, dashboards and AI automation.
View case study →Case Study 01
Enterprise RAG Knowledge Assistant
Business Scenario
A business team needed a secure AI assistant to answer questions from policies, SOPs, project documents, contracts, support notes and internal knowledge files without manually searching multiple folders.
Solution Approach
- Ingested documents from file storage and structured repositories.
- Chunked and indexed content with metadata such as document type, department, date and access category.
- Used retrieval-augmented generation to produce grounded answers with source references.
- Added guardrails to avoid unsupported answers and ask for clarification when context is insufficient.
- Integrated human review for sensitive or high-impact responses.
Tech Stack
Expected Outcomes
- Faster knowledge discovery
- Reduced dependency on manual document search
- Improved answer consistency
- Better onboarding and support productivity
Case Study 02
Vector-less RAG Search Assistant
Business Scenario
A team wanted AI-powered search and question answering but did not want to introduce a vector database during the first phase because the content was highly structured and already available in relational tables.
Solution Approach
- Used SQL filtering, full-text search, keyword search and metadata ranking to retrieve candidate records.
- Applied reranking logic based on exact match, recency, category and business priority.
- Passed top-ranked context to the LLM for summarisation and response generation.
- Added query classification to decide whether to search tickets, records, documents or reports.
- Designed later migration path to hybrid RAG if semantic search becomes necessary.
Tech Stack
Expected Outcomes
- Lower infrastructure complexity
- Fast implementation for structured data
- Improved explainability of retrieved records
- Option to evolve toward hybrid RAG later
Case Study 03
MCP Server for Enterprise Tool Integration
Business Scenario
A business wanted AI assistants to safely access internal tools, databases, documents and operational APIs while keeping tool access controlled, auditable and reusable across multiple assistants.
Solution Approach
- Built a custom MCP server exposing selected tools such as search, database lookup and API actions.
- Implemented role-based tool access and input validation for sensitive operations.
- Added audit logging for tool calls, parameters, response status and user context.
- Designed MCP connectors for document search, CRM/ERP lookup, ticket creation and reporting.
- Integrated human approval for write/update actions.
Tech Stack
Expected Outcomes
- Reusable AI tool integration layer
- Controlled access to enterprise systems
- Improved security and auditability
- Faster AI assistant implementation
Case Study 04
Agentic AI Workflow Automation
Business Scenario
A team needed to automate repetitive review, follow-up and reporting tasks where the process required decision-making, data lookup, document reading and approval before final action.
Solution Approach
- Designed an agent that can understand the request, break it into steps and call the required tools.
- Implemented planning, tool execution, validation and response generation stages.
- Added human-in-the-loop approval before sending emails, updating records or triggering external actions.
- Used structured output schemas to keep agent responses predictable and easy to validate.
- Created fallback paths when data is missing or confidence is low.
Tech Stack
Expected Outcomes
- Reduced manual follow-up effort
- Better process consistency
- Controlled automation with approvals
- Faster turnaround for repetitive tasks
Case Study 05
Multi-Agent Orchestrator for Complex Workflows
Business Scenario
A complex process required multiple types of expertise: document understanding, data lookup, validation, risk classification, response drafting and final action. A single-agent approach became difficult to govern.
Solution Approach
- Created a supervisor/orchestrator agent to route tasks to specialist agents.
- Defined specialist agents for search, analysis, validation, summarisation and execution.
- Used state management to track workflow status, intermediate results and approvals.
- Added policy checks and confidence scoring before allowing downstream actions.
- Enabled extensibility so new agents can be added for future business functions.
Tech Stack
Expected Outcomes
- Better control over complex AI workflows
- Improved modularity and maintainability
- Clearer governance and approvals
- Reusable agent patterns across use cases
Case Study 06
AIOps Incident Intelligence Assistant
Business Scenario
Operations teams were receiving alerts from multiple monitoring systems and needed faster triage, incident summaries, root-cause hints and recommended runbook actions.
Solution Approach
- Ingested alerts, logs, metrics and traces from monitoring platforms.
- Correlated events by service, environment, time window and dependency impact.
- Generated AI summaries with probable cause, affected components and recommended next steps.
- Linked runbooks and automated low-risk diagnostic actions.
- Maintained incident history for continuous improvement and knowledge reuse.
Tech Stack
Expected Outcomes
- Reduced incident triage time
- Improved operational visibility
- Faster root-cause analysis support
- Reusable incident knowledge base
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