--- title: "Enterprise Knowledge Graph: Stop AI Hallucinations" description: Ground generative AI in a trusted knowledge graph. Unify SharePoint, CRM, and SQL silos with no rip-and-replace. Get a free assessment. image: https://itraven.net/hubfs/itraven-site/2026-10/solution-data-governance-kg-night-1200x630.webp --- [![IT Raven](https://itraven.net/hs-fs/hubfs/itraven-site/brand2/itraven-lockup-inverse-2x.png?width=180&height=36&name=itraven-lockup-inverse-2x.png)](https://itraven.net/?hsLang=en) ![menu](https://f.hubspotusercontent40.net/hubfs/7479797/menu.png) ![close\_24px](https://7479797.fs1.hubspotusercontent-na1.net/hubfs/7479797/close_24px.svg) - [Home](https://itraven.net) - [Solutions](https://itraven.net/solutions) Show submenu for Solutions - Secure & Govern Show submenu for Secure & Govern - [Cybersecurity](https://itraven.net/solutions/cybersecurity) - [Compliance & Risk](https://itraven.net/solutions/compliance-risk) - [Data Governance](https://itraven.net/solutions/data-governance-kg) - [Physical Security](https://itraven.net/solutions/physical-security) - Connect & Communicate Show submenu for Connect & Communicate - [Connectivity](https://itraven.net/solutions/connectivity-wan) - [SASE / SD-WAN](https://itraven.net/solutions/sase-sdwan) - [UCaaS & Voice](https://itraven.net/solutions/ucaas-voice) - [Contact Center](https://itraven.net/solutions/ccaas) - [Mobility](https://itraven.net/solutions/mobility) - Cloud & Infrastructure Show submenu for Cloud & Infrastructure - [Cloud & IaaS](https://itraven.net/solutions/cloud-iaas) - [Data Center](https://itraven.net/solutions/datacenter-colo) - [Backup & DR](https://itraven.net/solutions/backup-dr) - [Managed IT](https://itraven.net/solutions/managed-it) - [Integration (iPaaS)](https://itraven.net/solutions/ipaas) - Grow & Specialize Show submenu for Grow & Specialize - [CX & AI](https://itraven.net/solutions/cx-ai) - [Legal AI](https://itraven.net/solutions/legal-ai) - [Workforce Training](https://itraven.net/solutions/workforce-training) - [Demand Gen](https://itraven.net/solutions/demand-gen) - [Public Sector](https://itraven.net/solutions/public-sector) - [All Solutions →](https://itraven.net/solutions) - [Assessments](https://itraven.net/assessments) - [Store](https://store.itraven.net/) - [Industries](https://itraven.net/industries) - [Trust](https://itraven.net/trust-center) - [About](https://itraven.net/about-us) - [Contact](https://itraven.net/contact-us) - [Blog](https://itraven.net/blogs) - [Home](https://itraven.net) - [Solutions](https://itraven.net/solutions) Show submenu for Solutions - Secure & Govern Show submenu for Secure & Govern - [Cybersecurity](https://itraven.net/solutions/cybersecurity) - [Compliance & Risk](https://itraven.net/solutions/compliance-risk) - [Data Governance](https://itraven.net/solutions/data-governance-kg) - [Physical Security](https://itraven.net/solutions/physical-security) - Connect & Communicate Show submenu for Connect & Communicate - [Connectivity](https://itraven.net/solutions/connectivity-wan) - [SASE / SD-WAN](https://itraven.net/solutions/sase-sdwan) - [UCaaS & Voice](https://itraven.net/solutions/ucaas-voice) - [Contact Center](https://itraven.net/solutions/ccaas) - [Mobility](https://itraven.net/solutions/mobility) - Cloud & Infrastructure Show submenu for Cloud & Infrastructure - [Cloud & IaaS](https://itraven.net/solutions/cloud-iaas) - [Data Center](https://itraven.net/solutions/datacenter-colo) - [Backup & DR](https://itraven.net/solutions/backup-dr) - [Managed IT](https://itraven.net/solutions/managed-it) - [Integration (iPaaS)](https://itraven.net/solutions/ipaas) - Grow & Specialize Show submenu for Grow & Specialize - [CX & AI](https://itraven.net/solutions/cx-ai) - [Legal AI](https://itraven.net/solutions/legal-ai) - [Workforce Training](https://itraven.net/solutions/workforce-training) - [Demand Gen](https://itraven.net/solutions/demand-gen) - [Public Sector](https://itraven.net/solutions/public-sector) - [All Solutions →](https://itraven.net/solutions) - [Assessments](https://itraven.net/assessments) - [Store](https://store.itraven.net/) - [Industries](https://itraven.net/industries) - [Trust](https://itraven.net/trust-center) - [About](https://itraven.net/about-us) - [Contact](https://itraven.net/contact-us) - [Blog](https://itraven.net/blogs) [GET STARTED](https://itraven.net/contact-us?hsLang=en) ![](https://itraven.net/hubfs/itraven-site/2026-10/solution-data-governance-kg-night-1600x900.webp) Data Governance & Knowledge Graph # Your AI hallucinates and your data is scattered. Fix the foundation, not the chatbot. We unify SharePoint, file shares, CRMs, and SQL databases into one governed knowledge graph, so search returns trusted answers and your generative AI is grounded in source you can trace back. No rip-and-replace. [Get your free IT and security evaluation](https://itraven.net/assessments?hsLang=en)[Book a 15-minute call](https://meetings.hubspot.com/john-grazioli/round-robin-discovery-call-scheduler) 500+ partner networkExperience managing Fortune 1000 accountsVendor-neutralSecurity-first The problem ## Scattered data, hallucinating AI, and a search box nobody trusts Your data lives across SharePoint, file shares, CRMs, and legacy systems, and nobody can get a single unified view of it. Employees burn hours every day hunting for the right document, the right expert, or the right answer, while the AI assistants you deployed give confident but wrong answers you can't bet a real decision on. You keep funding AI, but the underlying data isn't ready, so the ROI you were promised never shows up. - 26%of an employee's time (and salary) is spent on searching for information.knowledge-management research / provider white paper - 40%The 'Bad Data Tax' adds up to between 40%-60% of an enterprise's technology spend.provider conference deck How we solve it ## A governed semantic layer on top of the systems you already run We connect the data you already have, make its meaning explicit on open standards, and ground your AI in traceable source. It's delivered as a security-first managed service, and we stay vendor-neutral, so you get the best-fit blend of graph, search, and models instead of a locked-in stack. 1. ### Unify your data where it lives We connect Microsoft 365, SharePoint, content systems, SQL databases, and event buses into one knowledge graph without ripping anything out. Your data stays in place, under the access rules you already enforce. 2. ### Open standards, no vendor lock-in The semantic layer is built on open W3C standards (RDF, SKOS, OWL, SHACL), so your meaning model is portable and interoperable. That is the deliberate anti-lock-in choice, not a proprietary graph you can never leave. 3. ### Automated, governed tagging Taxonomy-driven tagging runs automatically against a centrally governed source of truth, keeping metadata consistent across millions of pieces of content, so search returns relevant answers instead of noise. 4. ### Graph RAG that grounds your AI Graph RAG reasons over the connected graph before the model responds, so generative AI answers are explainable and traceable to source documents, cutting hallucinations to a level the enterprise can trust. 5. ### Security-first managed service Enterprise-grade access controls, data isolation, and governance are built into both on-prem and cloud deployments. Nothing gets centralized that shouldn't be; the semantic layer references your data under existing controls. 6. ### Start small, then expand We prove value on one high-value repository or use case, baseline your numbers, then expand toward a full enterprise semantic layer as maturity grows. No boil-the-ocean program that dies in year two. What you get ## Outcomes you can hold us to. - A single, unified view across SharePoint, file shares, CRM, SQL, and content systems, without replacing any of them - Generative AI answers that are explainable and traceable to source documents, not confident guesses - Search that returns trusted, relevant results because metadata is consistent and governed - Hours of daily search time returned to your employees - Taxonomy and ontology expertise delivered for you, with no need to staff a knowledge-engineering team - New subsidiaries and acquisitions folded into one governed hub instead of becoming the next silo - A standards-based, portable semantic layer that keeps your AI investment compounding instead of stranded ### What this looks like in the field - Three disconnected sources (internal document files, a SharePoint environment, and a learning platform) fused into one knowledge hub that gives employees tailored learning materials and connected information they never had before. - A large documentation property that manually tagged millions of content pieces, with search returning misleading results, moves to automated controlled-vocabulary tagging and a governed self-service portal. - A professional-services organization that couldn't match people to projects gains a knowledge-graph recommender that semantically profiles employees, positions, and projects, then ranks the right people to the right work on the fly. - An acquisitive business losing operational consistency to each new subsidiary unifies its legacy systems and field teams into one intelligent hub that accelerates onboarding as it grows. - Corporate reports where material financial, regulatory, and reputational risks are addressed but never named get those implicit risks surfaced by an inference engine using graph-based concept tagging. Outcome patterns from across the industry: the shape of results vendor-neutral delivery produces. Questions, answered ## Frequently asked Key facts - A knowledge graph models meaning and relationships, while keyword and vector search only retrieve documents. - Graph RAG grounds generative AI by reasoning over a knowledge graph before the model responds, making answers traceable to source. - Building an enterprise semantic layer on open W3C standards (RDF, SKOS, OWL, SHACL) keeps the meaning model portable and avoids vendor lock-in. - A semantic layer can be added on top of SharePoint, CRM, and SQL systems without rip-and-replace, keeping data where it lives. - Vendor-neutral knowledge graph services broker the best-fit blend of graph, search, and AI models instead of a single locked-in stack. We already have enterprise search and Microsoft 365 / Copilot. Why do we need a knowledge graph? Keyword and vector search retrieve documents; they don't model meaning or relationships. A semantic layer adds the explicit context generative AI needs to reason and trace answers to source, which is exactly the gap that causes Copilot-style assistants to hallucinate or return inconsistent results across your silos. We layer it on top of M365 and your existing search, not instead of it. Isn't this a multi-year rip-and-replace of our data platform? No. We keep your data where it lives (SharePoint, CRM, SQL, content systems) and add semantics on top. We start with a single high-value repository or use case, prove value, then expand toward a full enterprise semantic layer as maturity grows. You don't rip anything out. Vectors are simpler and our AI team already uses them. Why graph? It's not either/or. Vectors are great for fuzzy retrieval; a knowledge graph adds explainable, multi-hop reasoning and a governed source of truth. Graph RAG combines both so answers trace to source documents, which is what makes AI trustworthy enough for real decisions. We broker the right blend of graph, search, and models for your cost and performance needs. Wouldn't we be locking ourselves into a proprietary graph vendor? The build is on open W3C standards (RDF, SKOS, OWL, SHACL), the explicit anti-lock-in choice. Your meaning model is portable and interoperable, not trapped in one stack. We stay vendor-neutral and broker the best-fit combination of graph, search, and AI rather than forcing a single locked-in product. Our data is too sensitive to centralize into one graph. Nothing gets centralized that shouldn't be. It's delivered as a managed service with a security-first posture: enterprise-grade access controls, data isolation, and governance built into both on-prem and cloud deployments. Data stays where it lives; the semantic layer references it under the same access rules you already enforce. We tried a knowledge-management project before and it died. Why is this different? Most KM projects fail because they try to boil the ocean with manual tagging and no governed source of truth. The difference here is automated taxonomy-driven tagging at scale, an open-standards semantic layer, and a start-small-then-expand path that proves value on one repository before you commit the enterprise. We also deliver the taxonomy and ontology expertise for you, so you don't have to staff a knowledge-engineering team. ## Baseline your bad-data tax before you commit to a build Start with the cost you're already paying. In a free IT and security evaluation, we scope a single high-value use case and baseline your search-time and data-readiness numbers against your actual environment, so any investment is justified by evidence, not a sales pitch. Vendor-neutral, security-first, no rip-and-replace. [Get your free IT and security evaluation](https://itraven.net/assessments?hsLang=en)[Book a 15-minute call](https://meetings.hubspot.com/john-grazioli/round-robin-discovery-call-scheduler) [Compare suppliers for this in the IT Raven Store →](https://store.itraven.net/services/data-governance/) IT Raven ## Ask for your free IT and security evaluation Updated October 7, 2026. Written by Bert Frichot. [![IT Raven](https://itraven.net/hubfs/itraven-site/brand2/itraven-lockup-inverse-2x.png)](https://itraven.net/?hsLang=en) IT Raven LLC is a security-first, vendor-neutral IT partner. We plan and run networks, cloud, voice and security for mid-market companies, sourced through a 500+ partner network. 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Unify SharePoint, CRM, and SQL silos with no rip-and-replace. 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A semantic layer adds the explicit context generative AI needs to reason and trace answers to source, which is exactly the gap that causes Copilot-style assistants to hallucinate or return inconsistent results across your silos. We layer it on top of M365 and your existing search, not instead of it." }, "name" : "We already have enterprise search and Microsoft 365 / Copilot. Why do we need a knowledge graph?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "No. We keep your data where it lives (SharePoint, CRM, SQL, content systems) and add semantics on top. We start with a single high-value repository or use case, prove value, then expand toward a full enterprise semantic layer as maturity grows. You don't rip anything out." }, "name" : "Isn't this a multi-year rip-and-replace of our data platform?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "It's not either/or. Vectors are great for fuzzy retrieval; a knowledge graph adds explainable, multi-hop reasoning and a governed source of truth. Graph RAG combines both so answers trace to source documents, which is what makes AI trustworthy enough for real decisions. We broker the right blend of graph, search, and models for your cost and performance needs." }, "name" : "Vectors are simpler and our AI team already uses them. Why graph?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "The build is on open W3C standards (RDF, SKOS, OWL, SHACL), the explicit anti-lock-in choice. Your meaning model is portable and interoperable, not trapped in one stack. We stay vendor-neutral and broker the best-fit combination of graph, search, and AI rather than forcing a single locked-in product." }, "name" : "Wouldn't we be locking ourselves into a proprietary graph vendor?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Nothing gets centralized that shouldn't be. It's delivered as a managed service with a security-first posture: enterprise-grade access controls, data isolation, and governance built into both on-prem and cloud deployments. Data stays where it lives; the semantic layer references it under the same access rules you already enforce." }, "name" : "Our data is too sensitive to centralize into one graph." }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Most KM projects fail because they try to boil the ocean with manual tagging and no governed source of truth. The difference here is automated taxonomy-driven tagging at scale, an open-standards semantic layer, and a start-small-then-expand path that proves value on one repository before you commit the enterprise. We also deliver the taxonomy and ontology expertise for you, so you don't have to staff a knowledge-engineering team." }, "name" : "We tried a knowledge-management project before and it died. Why is this different?" } ] } ```