Chat with Your Own Data
AI-powered chat on your company documents. Precise answers with source citations — secure, auditable, on Azure.
Your Company Knowledge Is There — but Nobody Finds It
Process manuals, contracts, SOP documents, customer data — everything sits somewhere in SharePoint, file shares, or databases. But when an employee has a specific question, they search for hours or ask colleagues. With 20% turnover in mid-market companies, implicit knowledge is lost every time someone leaves.
Traditional knowledge management systems (SharePoint intranet, wiki, Confluence) fail at adoption: nobody maintains them, nobody searches them systematically. The alternative: an AI chat that understands natural language questions, searches your documents, and answers with source citations.
Azure OpenAI and Azure AI Search make this possible — with data that stays in your Azure tenant. No third parties, no data leakage risks. All that is missing is structured implementation.
ACTIVITIES IN DETAIL
DELIVERABLES
Use case definition: scope a concrete application (e.g., HR handbook, SOPs, contract database)
Data preparation: document inventory, chunking strategy, metadata enrichment, OCR for scanned PDFs
Create Azure AI Search index: hybrid search (keyword + vector) with semantic ranking
Implement RAG pattern: Azure OpenAI as answer engine, AI Search as retrieval backend
Configure system prompt: enforce source citations, optimize groundedness, adjust temperature
Security: Managed Identity, RBAC, document-level permissions via Entra ID groups
Set up evaluation: measure relevance, groundedness, and completeness
Use case definition: scope a concrete application (e.g., HR handbook, SOPs, contract database)
Data preparation: document inventory, chunking strategy, metadata enrichment, OCR for scanned PDFs
Create Azure AI Search index: hybrid search (keyword + vector) with semantic ranking
Implement RAG pattern: Azure OpenAI as answer engine, AI Search as retrieval backend
Configure system prompt: enforce source citations, optimize groundedness, adjust temperature
Security: Managed Identity, RBAC, document-level permissions via Entra ID groups
Set up evaluation: measure relevance, groundedness, and completeness
Use Case Definition: Documented scope with data sources, target audience, and success criteria
RAG Implementation: Functional AI chat on your documents — with source citations
Security Configuration: Managed Identity, RBAC, document-level permissions — production-ready
Evaluation Setup: Groundedness and relevance metrics configured
Operations Handbook: Guide for data updates, monitoring, and cost management
Complete Project Documentation: Architecture, configuration, and decisions documented without gaps
3 steps. From start to finished project
How a typical Microsoft project runs with DAMALO
STEP 1
Choose a blueprint and analyze your environment
Select a proven blueprint. AI agents pull your licenses, current config, and compliance needs into the plan. No generic advice.
STEP 2
Receive your plan and start implementation
Review the plan. AI agents draft architecture, sequence tasks, and map dependencies to Microsoft best practices. Tailored to your tenant.
STEP 3
Guided implementation through to completion
Execute step by step. AI agents provide PowerShell scripts, admin center deep-links, and walkthroughs. Every change auto-documented.
The result: A completed Microsoft project in 1-2 weeks. Documented. Audit-ready. Understood by your team. Adjustable at any time. No change requests. No follow-up engagements.
Next steps after Chat with Your Own Data
A cleanly configured tenant is the foundation. These blueprints build directly on it
