By Craxel's Chief Knwoledge Engineer John Sobanski
March 3rd, 2026
Your customers, board, boss and direct reports will ask you about Knowledge Graphs. Understanding this topic is essential for maintaining a critical and competent reputation. At a high level, Knowledge Graphs boost your AI, they transform slow, unresponsive, and inaccurate enterprise AI solutions into the kind of magical experience you get from ChatGPT, Claude, Gemini or Grok.
Enterprise AI Solutions use Retrieval Augmented Generation (RAG) to seed Large Language Models (LLM) with unique, proprietary corporate data. A user types a query into the chatbot, then an agent uses this query to fetch (search for) relevant documents from the enterprise databases, and then passes a summary of the fetched documents into the LLM's context window.
This approach could lead to hallucinations. I've had the experience, for example, of an Enterprise RAG solution pulling the past performance of a teaming partner from my corpus of proposal submissions and presenting it as past performance from my company. Thanks to my Tribal Knowledge, I corrected the response, but a new employee may have submitted this faulty info to the government and lost our bid.
A RAG with Knowledge Graph solution will fix uncertainties and errors.
Writer's RAG benchmarking reports its graph-based approach scored 86.31% on Robust QA, while vector‑retrieval RAG approaches ranged from~30-75%.
RAG retrieves similar documents. It cannot traverse connections between entities. It cannot resolve when the same entity has different names across systems.
Most companies use RAG alone. This approach fails for relationship queries. A knowledge graph enables relationship queries.
Knowledge Graphs now have significant buy-in and pedigree among major corporations. Bloomberg, for example, runs a knowledge graph with billions of facts about companies, securities and people. Google launched theirs in 2012. The graph now tracks 5 billion entities. Meta's TAO system handles over a billion graph queries per second.
data.world found that backing LLM answers with a knowledge graph improved response accuracy 3× across 43 real enterprise business questions.
In a vanilla Enterprise AI RAG agents find similar text, stuff it into the context window and hope for the best. A Knoweldge Graph powered RAG has agents retrieve the right entities and their connected facts, and pass those to the context window. In other words, Knoweldge Graph get the best, cleanest and most focused information to the context as quick as possible.
A Knoweldge Graph reduces the classic enterprise failure mode, of sending the wrong entity, wrong attribution, or wrong version to the context window.
A Vanilla RAG sends data to the context window, a RAG + Knowledge Graph sends meaning.
Knowledge Graphs help wherever you gain intelligence from relationships. The classic "Detective Board" that shows pictures of criminals connected by strings provides value because of the relationships. Detectives get more information off the relationships of the criminals vs. any information focused on one particular criminal in solitude.
Gartner forecasts that graph technologies will be used in 80% of data &analytics “innovations” by 2026
Some use cases for Knowledge Graphs include:
Intelligence and investigations: Link analysis. Entity resolution. Connect-the-dots across cases and time.
Financial services: Fraud rings. AML exposure paths. Mule networks.
Ecommerce: Product identity and attributes. Variant compatibility graphs. Better recommendations.
Knowledge Graphs solve problems where networks matter and one record does not equal one truth.
Once you build a Knoweldge Graph, you get a reusable infrastructure, not a one-off pipeline.
To start, pick one high-stakes workflow: Proposal drafting from past performance, Fraud ring triage, Catalog cleanup and recommendations.
Then, define a minimal schema. Chose five to ten entity types and ten to twenty relationship types. Pick clear IDs.
You can solve identity problem first. This includes deduplication, same-as links, cononicalization and provenance tracking to know what system said what.
Ship your product fast. Enable a graph-backed search for users and AI agents. Provide an investigation view dashboard. Graph-grounded assistant.
Google Cloud estimates bad onsite search costs retailers $2T+ globally, and~80% of shoppers bounce after an unsuccessful search.
Look at a Key Performance Parameter, and measure what matters. You want to build for accuracy, precision and latency. Tie your metrics to the business, see how the Knoweldge Graph drives conversion or threat reduction.
You need a purpose-built Knowledge Infrastructure to deploy a proper Knowledge Graph solution. To get your (private) LLM the best information as quickly as possible, you need an infrastructure that can handle graph, time series, vector and relational data at scale.
Traditional architecture splits workloads across specialized systems. Clickstream goes to Kafka and S3. Product catalogs live in graph or relational databases. Time series demand sits in forecasting databases. Customer history spreads across CRM, POS, and loyalty platforms. Each system requires separate infrastructure. Data engineering teams spend months building pipelines to share context, timeline, and identity across these silos.
Craxel's Knowledge Infrastructure unifies graph, vector, time series, and relational storage in one database. You store product relationships, customer embeddings, demand forecasts, and transactional history in the same system. No integration pipelines between systems. No cross-database joins. Entity resolution happens once. The database handles traversals, similarity search, temporal queries, and ACID transactions natively. This eliminates the data engineering tax of maintaining separate infrastructure for each use case.
Bloomberg: Reinanda et al., "Knowledge Graphs: an Information Retrieval Perspective"(2020). SIGIR 2018 papers on contextualizing knowledge graph facts.
Google: Knowledge Graph launched 2012. Now contains 500 billion facts on 5 billion entities.
Meta: TAO (The Associations and Objects). Engineering blog 2013. ACM Queue "Industry-scale Knowledge Graphs" (2019).
