Data Silos & Fragmentation
Information was trapped in disparate tools (Slack, Jira, wikis, cloud drives, file servers), requiring employees to check up to five search boxes for a single query.
Customer Overview
Customer Overview
- Organization
- Global technology enterprise
- Scale
- ~12,000 employees operating across 20+ countries
- Content Estate
- ~4.8 million documents distributed across cloud drives, Slack threads, an internal wiki, Jira tickets, and legacy file servers
Executive Summary
Executive Summary
To solve pervasive data fragmentation and slow information discovery, YenDigital deployed a permission-aware Retrieval-Augmented Generation (RAG) knowledge assistant across a 12,000-employee global enterprise. Implemented in 16 weeks, the platform unifies 4.8M+ unstructured documents into a natural-language search interface. The solution reduced median time-to-information from 45 minutes to under 2 minutes, accelerated new-hire onboarding from 8 weeks to 3 weeks, and reduced “Where is X?” internal helpdesk tickets by ~40%, all while maintaining strict, role-based data entitlements.
Business Context
Business Context
Operating at scale across 20+ countries, the organization’s institutional knowledge was heavily decentralized. Information discovery relied heavily on tribal knowledge, passing location paths by word of mouth. Employees consistently reported the inability to find information as a top-three productivity bottleneck, which directly hampered decision-making speed, cross-functional collaboration, and overall operational efficiency.
The Challenge
The Challenge
Lexical Search Failure (Vocabulary Mismatch)
Legacy keyword search relied on literal string matching, failing when search terms did not match document titles (e.g., searching “Q3 revenue” returned no results for “Q3 Financials”).
High Operational Costs & Stalled Productivity
Hours were wasted per employee every week searching for files, decision-making stalled waiting on subject-matter experts, and work was frequently duplicated because prior assets remained invisible.
Complex Security Requirements
Ensuring that sensitive enterprise data (e.g., finance vs. contractor access) remained restricted without breaking search functionality.
YenDigital’s Approach
YenDigital’s Approach
YenDigital designed a dual-path RAG architecture tailored for high-volume enterprise estates, prioritizing search recency, deep semantic understanding, and security enforcement at the data layer rather than the UI. The approach focused on building an offline ingest-and-index path paired with an online natural-language synthesis path, bound by cross-cutting security, evaluation, and guardrail controls.
The Solution
The Solution
The resulting system features a multi-stage RAG pipeline:
Connectors & Incremental Sync
Change-data-capture connectors continuously pull updates, keeping the index current within minutes.
Parsing, OCR & Semantic Chunking
Converts unstructured formats (PDFs, decks, scans) into clean data. Chunks are prepended with LLM-generated summaries (“contextual retrieval”) to preserve surrounding document context.
Hybrid Indexing & Security Trimming
Pairs vector embeddings with a BM25 keyword index while mirroring source-system Access Control Lists (ACLs) onto every chunk to enforce live, role-based entitlement filtering.
Query Understanding & Reranking
Multi-part questions are decomposed and expanded, run through parallel vector/lexical retrieval, fused via Reciprocal Rank Fusion (RRF), and re-ordered using a cross-encoder reranker.
Grounded Synthesis & Guardrails
An LLM synthesizes verified answers with inline citations, protected by PII redaction, prompt-injection defenses, and continuous LLM-as-judge automated testing.
Business Outcomes
Business Outcomes
| Metric | Before Implementation | After Implementation |
|---|---|---|
| Median Time-to-Information | ~45 minutes |
Under 2 minutes |
| New-Hire Ramp to Productivity |
~8 weeks |
~3 weeks |
| Answers Delivered with Source Citations |
Not possible |
100% of answers |
| "Where is X?" Helpdesk Tickets |
Baseline | Reduced by ~40% |
Technologies Used
Technologies Used
- Architecture Pattern
- Dual-path Retrieval-Augmented Generation (RAG)
- Retrieval & Search
- Hybrid Search (Dense Vector Embeddings + Lexical BM25) with Reciprocal Rank Fusion (RRF)
- Ranking & Processing
- Layout-aware OCR/Parsing, Contextual Retrieval Chunking, Cross-Encoder Reranker
- LLM Capabilities
- Multi-document Synthesis, Query Expansion/Decomposition, LLM-as-Judge Evaluation
- Security & Governance
- ACL Security Trimming, PII Redaction, Prompt-Injection Defenses
Why YenDigital
Why YenDigital
Speed to Value
Rapid end-to-end execution, scaling from pilot to full enterprise deployment across 12,000 employees in 16 weeks.
Enterprise Security Focus
Native integration of source-system ACLs directly into the retrieval layer rather than relying on superficial UI-level filtering.
Advanced RAG Engineering
Implementation of state-of-the-art techniques such as contextual chunk enrichment, hybrid fusion, and automated LLM evaluation gates delivering accurate, grounded answers with citations.
Project Highlights
Project Highlights
- 100% Citation Verifiability
- Every answer provides inline citations linking back to exact source passages.
- Context-Aware Security
- Identical questions asked by different roles (e.g., finance analyst vs. external contractor) dynamically yield different, fully authorized responses.
- Enterprise Scale
- Successfully processes and maintains near-real-time index synchronization across 4.8 million documents and 20+ countries.
