Does your AI tool have a hard time answering complex questions? Maybe you should consider GraphRAG! During their presentation “Tracing the Thread: Decoding the Decision-Making Process with GraphRAG” at Enterprise Search & Discovery 2025, Enterprise Knowledge’s Urmi Majumder and Kaleb Schultz discussed how GraphRAG extends traditional RAG by grounding retrieval in a semantic knowledge graph to create transparent and explainable AI. They shared a client success story that focused on enabling program officers at a global philanthropic organization to evaluate investment progress and performance more efficiently, specifically by unifying disparate data sources such as reports, memos, and financial data into a single, interconnected network.

1. ENTERPRISE KNOWLEDGE
Tracing the Thread: Decoding the
Decision-Making Process with
GraphRAG
A Case Study
Urmi Majumder and Kaleb Schultz
Enterprise Search & Discovery 2025

2. ENTERPRISE KNOWLEDGE
Topics Covered
⬢ What is Retrieval Augmented Generation (RAG)?
⬢ How GraphRAG Differs.
⬢ How can you use GraphRAG for Transparent and Explainable AI?
⬢ How can an organization use GraphRAG for Cross-Document,
Multi-Step Question and Answering?

3. ENTERPRISE KNOWLEDGE
⬢ 15+ years of experience in enterprise system architecture,
design, implementation and operations
⬢ Leads the development of technical solutions in support of
wide variety of knowledge and data management solutions
⬢ Principal architect in knowledge graphs, enterprise AI, and
scalable data management systems
⬢ Ph.D in Computer Science, Duke University
Urmi Majumder
Principal Data Architecture Consultant
Kaleb Schultz
Senior Technical Analyst
⬢ 5+ years of experience in data science, automation, and process
improvement.
⬢ Experience leading and supporting graph and data engineering
efforts across multiple projects.
⬢ BA, Political Science, James Madison University

4. 10AREAS OF EXPERTISE
KM STRATEGY & DESIGN TAXONOMY & ONTOLOGY DESIGN
TECHNOLOGY SOLUTIONS AGILE, DESIGN THINKING, & FACILITATION
CONTENT & BRAND STRATEGY KNOWLEDGE GRAPHS, DATA MODELING, & AI
ENTERPRISE SEARCH INTEGRATED CHANGE MANAGEMENT
ENTERPRISE LEARNING CONTENT MANAGEMENT
80+ EXPERT
CONSULTANTS
HEADQUARTERED IN WASHINGTON, DC,
USA
ESTABLISHED 2013 – OUR FOUNDERS AND PRINCIPALS HAVE BEEN PROVIDING
KNOWLEDGE MANAGEMENT CONSULTING TO GLOBAL CLIENTS FOR OVER 20 YEARS.
KMWORLD’S
100 COMPANIES THAT MATTER IN KM (2015, 2016, 2017, 2018,
2019, 2020, 2021, 2022)
TOP 50 TRAILBLAZERS IN AI (2020, 2021, 2022)
CIO REVIEW’S
20 MOST PROMISING KM SOLUTION PROVIDERS (2016)
INC MAGAZINE
#2,343 OF THE 5000 FASTEST GROWING COMPANIES (2021)
#2,574 OF THE 5000 FASTEST GROWING COMPANIES (2020)
#2,411 OF THE 5000 FASTEST GROWING COMPANIES (2019)
#1,289 OF THE 5000 FASTEST GROWING COMPANIES (2018)
INC MAGAZINE
BEST WORKPLACES (2018, 2019, 2021, 2022)
WASHINGTONIAN MAGAZINE’S
TOP 50 GREAT PLACES TO WORK (2017)
WASHINGTON BUSINESS JOURNAL’S
BEST PLACES TO WORK (2017, 2018, 2019, 2020)
ARLINGTON ECONOMIC DEVELOPMENT’S
FAST FOUR AWARD – FASTEST GROWING COMPANY (2016)
VIRGINIA CHAMBER OF COMMERCE’S
FANTASTIC 50 AWARD – FASTEST GROWING COMPANY (2019,
2020)
AWARD-WINNING
CONSULTANCY
PRESENCE IN BRUSSELS, BELGIUM
EK At A Glance
STABLE CLIENT
BASE
ENTERPRISE KNOWLEDGE

5. ENTERPRISE KNOWLEDGE
Retrieval Augmented
Generation (RAG)

6. ENTERPRISE KNOWLEDGE
RAGs and
LLMs
Using RAG, an LLM can access information beyond the original training set used.
While this can produce more accurate answers, foundational models alone cannot
reliably interpret enterprise data because they do not know domain context.
Content
Sources and
RAGs
RAGs can pull information from multiple sources (databases, search engines, APIs, etc.).
RAGs can summarize multiple pieces of content into a single source of truth.
Relationships between content could be implemented in a Knowledge Graph and
leveraged by a RAG for relevance scoring
Retrieval Augmented Generation
Retrieval Augmented
Generation (RAG) is a process
that augments the input
supplied to an LLM with
relevant information from an
organization’s knowledge
domain.

7. ENTERPRISE KNOWLEDGE
Example RAG Call:
LLM without RAG
Organizational
knowledge is
brought in from a
data source.
“How many
pharmaceutical
clients does EK
have?”
The RAG model uses embeddings
(vector representations of text) to
identify the most contextually
similar documents in the
knowledge base to the prompt,
passing them to the LLM.
User Input
Knowledge
Base
RAG Model
LLM refers to the
data it was passed
from the knowledge
base. If the data is
up-to-date, precise,
and interpreted
correctly, the answer
should be right.
LLM produces an
output that should
be right.
LLM
LLM Output
Right answer
based on
public
information!
“Who is the CEO
of Enterprise
Knowledge?”
“Enterprise
Knowledge’s
CEO, Zach
Wahl, will be
speaking…”
“The CEO of
Enterprise
Knowledge is Zach
Wahl.”
User Input
Without retrieval, the LLM never sees
domain context and must guess from
pre-training only.
LLM with RAG

8. ENTERPRISE KNOWLEDGE
Problem Statement
At this philanthropic organization, program officers and
leadership rely on a variety of documentation, ranging from
investment reports to memos, across multiple repositories and
formats to make impactful business decisions.
They need to have a way to:
▪ Identify information related to specific investments, their
outcomes, and changes over time across documents and
resources
So that they can…
Quickly assess current investment progress and status
Avoid “manual scavenger” hunts for supporting files
Ensure decisions are based on the most current information

9. ENTERPRISE KNOWLEDGE
How Common Tools Solve the Problem
A lot of traditional RAG-style tools would address
this by retrieving chunks of text related to the
user’s question based on surface level matching,
including:
⬢ Using vector similarity to pull passages that
sound similar to the query.
⬢ Identifying sentences that mention key
investment terms (project or grant names,
country, partner, etc.)
These “naive-RAG” solutions don’t explain why certain passages were returned
and lack a deeper semantic understanding of how information connects across
files. Because of this, traditional RAG cannot trace its decision path or reconcile
information about the same investment that is scattered across multiple
documents, resulting in untraceable and incorrect answers.

10. ENTERPRISE KNOWLEDGE
The Solution
GraphRAG based Chatbot
ORIGINAL STATE THE NEED SOLUTION
● Investment information lives
across reports, memos,
spreadsheets, emails, and
disparate repos with
inconsistent terminology.
● Manual reviews to answer
questions (progress, status,
changes) are slow and error
prone.
● No single current view of the
investment lifecycle and
decisions often rely on the most
visible documents.
● Natural-language access to find
relevant passages across many
files quickly.
● Treat information as an
interconnected network of
relationships, not isolated text
fragments.
● Provide traceable, auditable
reasoning paths showing how
the answer was assembled.
● Reduce time to insight for
program officers and leadership.
● Construct a standards-based
graph / ontology representing
relevant concepts and
relationships.
● Link structured and
unstructured data to unify
terminology via the graph.
● Use the graph to drive retrieval:
semantically-anchored prompt
assembly into LLM-generated
answers.
● Provide answers with
provenance.

11. ENTERPRISE KNOWLEDGE
Transparent & Explainable AI
with GraphRAG

12. ENTERPRISE KNOWLEDGE
What is GraphRAG
GraphRAG extends traditional RAG by grounding retrieval and reasoning in a semantic
knowledge graph, allowing the model to understand how information is connected, not
just textually similar.
Retrieves relationships, entities, and
facts anchored to a shared ontology.
Understands how chunks relate
across documents.
Produces traceable reasoning paths
with provenance.
Built for multi-step, cross-document
questions.
Transparent and auditable answer
assembly tied to defined graph
structure.
GraphRAG
Retrieves information based on vector
similarity.
Treats each chunk independently.
Hard to explain why a passage was
returned.
Good for quick semantic search.
Black-box behavior when answers are
assembled in the prompt.
Traditional RAG

13. ENTERPRISE KNOWLEDGE
When to use a GraphRAG Solution?
Precision Intuition
Adaptability Composition
Delivers highly accurate
results by leveraging
structured relationships in
graph databases to resolve
ambiguities.
Enhances decision-making by
identifying patterns and
relationships that mimic
human reasoning and tool use.
Dynamically adjusts to new
information by integrating
updates via the graph and
reconfiguring minimal
components.
Synthesizes insights by
combining data from multiple
sources into a unified,
context-aware response.
Explanation
Parallel Retrieval
Provides transparent,
interpretable reasoning by
grounding responses in
explicit semantic links and
definitions.
Optimizes efficiency through
distributed query handling
across agents working on
interrelated tasks.

14. ENTERPRISE KNOWLEDGE
What makes up a GraphRAG solution?
GraphRAG solutions are comprised of:
Ontology
Defines the enterprise
entities and
relationships that the
system uses to
contextualize retrieved
knowledge.
Knowledge Graph
Database
A data store that
consists of nodes and
their relationships,
corresponding to
real-world entities and
interactions.
Retrieval and Ranking
Layer
Finds the relevant
graph nodes, paths,
and documents and
ranks them before
passing context to the
model.
Person
Address
Name
Phone
Email
Claim ID SSN Relative
Bank
LLM & Orchestration
Layer
Combines retrieved
graph context with the
user query and
produces a grounded
answer that can be
traced back to the
source.

15. ENTERPRISE KNOWLEDGE
Explainability via Semantics & Provenance
Ontology provides
structured
meaning which
delivers precision
and removes
ambiguity when
responding to
enterprise
questions.
Graph database
enables traceable
traversals so
responses are built
from specific nodes
and hops rather
than embedding
guesses.
Provenance
enriches each
retrieval result so
context contains
source, version,
timestamp, and
text snippet for
auditability.
Ranking ties
reasoning to the
most relevant
relationships
making
composition
possible across
many distributed
sources.
The orchestration
layer produces an
answer that can
be traced so every
claim is tied back to
specific hops and
sources for
explainability.

16. ENTERPRISE KNOWLEDGE
1
Structured (Data) and unstructured
(Document) information.
2
Pipelines to extract structured data and
enriched document metadata into the graph.
3
Graph backed knowledge aggregation layer,
making data more accessible and meaningful.
4
Secure API endpoints for Large Language
Models and associated resources.
5
Application to interpret user queries, surface
relevant context, and ground LLM output.
6
Possible interfaces include: a novel frontend app
or APIs utilized by internal agents.
High Level Data Flow for a GraphRAG Solution
Static Components (Maintained Consistently) Dynamic Components (Triggered at Query)
Graph
RAG
Ontology
+
Graph

17. ENTERPRISE KNOWLEDGE
Case Study: Cross-Document,
Multi-Step Q&A with GraphRAG

18. ENTERPRISE KNOWLEDGE
A global philanthropic organization
needs to evaluate the progress and
performance of investments.
However, information about those
investments lives in disparate and
segmented systems (CMS’s,
communication applications,
unstructured documents, etc.).
This makes it difficult to answer even
basic questions about status, outcomes,
risks, and alignment because there is no
unified view across sources with a
static terminology.
The Challenge

19. ENTERPRISE KNOWLEDGE
GraphRAG for Multi-Step Q&A
User submits
natural
language
query
Ontology
atomizes the
query into
graph-aligned
hops
Each traversal retrieves its
scoped graph context and
linked evidence
LLM
processes
with enriched
context from
the graph
System
delivers
grounded
final answer

20. ENTERPRISE KNOWLEDGE
O&E FRAMEWORK
GRAPH USE for AI
Ontology
Semantic Context
Custom Graph
Construction
Pipeline
Tabular data
EK-Generated Gold
Standard Data
GraphDB
Graph
End User
Application
Ontology-Leveraged NER
Graph-Based Semantic
Resolution
GRAPH CONSTRUCTION
User Query Is
processed
using ontology,
graph, and
documents
SME Validation
of AI Output
Unstructured data
In-Memory Document
Retrieval
GraphRAG Architecture

21. ENTERPRISE KNOWLEDGE
● Disparate sources with structured,
unstructured, and multiple versions,
made answers inconsistent.
● LLMs lacked domain context and
guessed from pre-training.
● No clear provenance to show
where facts came from.
● Slow to compile project-level
insights.
● Answers varied from person and
were hard to verify.
THE
AFTER
Business Transformation
THE
BEFORE
● Unified retrieval instead of manual
hunting.
● Domain-aware interpretation of
queries.
● Source-traceable evidence for
every claim.
● Near-instant synthesis of
project-level insights.
● Consistent answers based on
shared meaning.
UNLOCKED
QUESTIONS
● What investments are part of
Project ABC?
● Is there a variance of more than
10% for Investment XYZ?
● What are the goals of Investment
XYZ?
● How has the scope changed of
Investment XYZ?
● Is Investment XYZ meeting its
goals?

22. ENTERPRISE KNOWLEDGE
Closing Activity Reflections:
● Are you solving any GraphRAG
problems in your organization?
Where are you in your GraphRAG
journey?
● Will you be able to leverage some
of the approaches we discussed to
solve your problem?
● What are some blockers/concerns
that are top of mind for you?