A global CPG leader had decades of research, compliance, formulation, product, and market knowledge hidden across millions of documents. The problem was not lack of data. It was that the right knowledge was buried too deep. DigiWagon built a production-grade Retrieval-Augmented Generation platform using Azure AI services to help internal teams search, synthesise, and generate insights from trusted enterprise knowledge in seconds.
The result was a secure AI data assistant that makes institutional knowledge easier to find, reuse, and scale.
Why it mattered
Why Knowledge Needed a Smarter Search Layer
For a global CPG enterprise, knowledge moves everything. It supports product innovation, formulation decisions, compliance checks, safety reviews, research continuity, and faster go-to-market planning. But when that knowledge lives across millions of disconnected files, teams lose time before they even start solving the real problem. The enterprise needed more than a search bar. They needed an AI-powered knowledge layer that could understand context, retrieve the right information, and generate answers users could trust.
Business Challenge
Decades of research knowledge were scattered across files, folders, and systems.
R&D teams needed faster access to formulation, testing, research, and product intelligence.
Compliance teams needed quick access to safety, regulatory, and approval-related information.
Users were spending hours searching, reading, comparing, and summarising documents manually.
New employees needed a faster way to learn from existing enterprise knowledge.
Sensitive data needed to stay secure and visible only to authorised users.
The platform had to support multiple formats, including PDFs, PPTs, images, transcripts, videos, and internal documents.
The AI assistant needed to provide source-backed answers, not black-box responses.
Market Context
Enterprise AI is moving from experimental chatbots to workflow-ready knowledge systems.
RAG platforms are becoming essential for companies that need AI answers grounded in trusted internal data.
For R&D and compliance-heavy organisations, speed means little without accuracy, security, and traceability.
Generic LLMs are not enough when users need answers from proprietary documents.
Secure AI assistants are becoming the new interface for enterprise knowledge discovery.
User Problem
Search returned files, but users needed answers.
02Important insights were hidden inside long documents and static archives.
Teams had to open, compare, and summarise multiple files before getting useful context.
Users needed citations to trust AI-generated responses.
R&D and compliance teams needed editable outputs they could reuse in reports, briefs, and workflows.
Institutional knowledge was difficult to transfer across teams.
Users needed an assistant that could work with company-approved knowledge, not generic internet answers.
How we delivered
How We Built the Intelligence Layer
Ingest
Brought scattered knowledge into one pipeline
PDFs
PPTs
Images
Video transcripts
Internal documents
Scientific and compliance data
Index
Made enterprise knowledge searchable at depth
Azure AI Search
Dense semantic retrieval
Metadata mapping
Page and slide references
Document structure understanding
Ground
Made AI answers source-backed and trustworthy
Retrieved passages
Prompt grounding
Company-owned knowledge base
Page-level citations
Context-aware answer generation
Secure
Kept enterprise knowledge protected
SSO integration
Role-based filtering
Authorised retrieval
Secure internal access
Data confidentiality controls
What we built
What the AI Assistant Enabled
DigiWagon engineered a production-grade RAG platform that lets users ask natural language questions and get grounded, citation-backed answers from enterprise documents. Instead of sending users on a document hunt, the assistant retrieves the right context, generates a clear answer, and links it back to the original source.
A secure internal AI data assistant for enterprise knowledge discovery.
Production-grade RAG architecture powered by Azure AI services.
Semantic retrieval across millions of internal documents.
Natural language query experience for R&D, compliance, and product teams.
Grounded answer generation using company-owned data.
Page-level and slide-level citations for answer traceability.
Content cart functionality for creating editable outputs.
SSO and role-based filtering for secure access control.
Support for PDFs, PPTs, images, video transcripts, and internal knowledge formats.
Scalable foundation for future enterprise AI workflows.
The filmWatch how it works.
Key features
Seven capabilities, one per constraint.
Ask, Search, Answer
Let users ask questions in natural language instead of hunting through folders.
Retrieved the most relevant passages from millions of enterprise documents.
Turned scattered information into clear, usable answers.
Helped teams move from search fatigue to answer-first discovery.
Context-Aware Retrieval
Used semantic retrieval to understand user intent beyond exact keywords.
Surfaced relevant content from PDFs, PPTs, images, and transcripts.
Connected insights hidden across different knowledge formats.
Helped users discover relationships between research, compliance, and product data.
Source-Backed AI Responses
Grounded every answer in retrieved internal knowledge.
Reduced hallucination risk by using company-approved sources.
Generated precise summaries, comparisons, and explanations.
Made AI responses more trustworthy for business-critical use cases.
Secure Role-Based Access
Integrated SSO for secure internal access.
Applied role-based filtering during retrieval.
Ensured users only accessed information they were authorised to view.
Protected sensitive research, compliance, and enterprise data.
Page-Level Citations
Added citations so users could verify every AI-generated answer.
Linked responses back to source documents, pages, or slides.
Improved confidence for R&D and compliance teams.
Supported traceability for regulated and knowledge-heavy workflows.
Content Cart and Editable Outputs
Allowed users to save relevant snippets and insights.
Helped teams create editable summaries, reports, and reference material.
Turned research discovery into reusable work outputs.
Reduced the effort needed to manually compile information.
Multi-Format Knowledge Processing
Processed enterprise knowledge across PDFs, PPTs, images, and transcripts.
Converted static files into searchable intelligence.
Enabled discovery across structured and unstructured content.
Created a stronger foundation for future AI-powered knowledge workflows.
Technology
The stack behind this build.
Backend
Python
FastAPI
Data
PostgreSQL
MongoDB
Kafka
Celery
Cloud & DevOps
Azure
Azure VM
In closing
Where this leaves the product.
The global CPG leader now has a secure AI data assistant that turns scattered enterprise knowledge into fast, source-backed intelligence. By combining Azure AI services, semantic retrieval, grounded answer generation, citations, and role-based access, DigiWagon helped transform millions of documents into an active knowledge engine for R&D, compliance, and product teams. The result is a scalable RAG platform that helps teams find answers faster, preserve institutional knowledge, and make smarter decisions with trusted internal data.
Still Searching Through Enterprise Knowledge the Hard Way?
DigiWagon helps businesses turn scattered documents, research archives, and internal knowledge into secure AI assistants that retrieve, reason, and respond with source-backed confidence.
FinTech
Manufacturing
Manufacturing
