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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 evaluationBook a 15-minute call
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
The 'Bad Data Tax' adds up to between 40%–60% of an enterprise's technology spend.
provider conference deck
By the numbers

The case, in numbers

7x
ROI over 2-3 years on data/AI investment
provider use-case brief
40-60%
of tech spend lost to the bad-data tax
industry research
26%
of employee time spent searching for information
knowledge-management research
50%
reduction in learning/onboarding cycle
provider use-case brief
$2M
savings from better maintenance and fewer defects
provider use-case brief
Investment vs. Return on a Knowledge Graph Build
3-yr TCO (investment)
500000 USD
Documented savings (return)
2e+06 USD
provider use-case brief
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.

01

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.

02

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.

03

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.

04

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.

05

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.

06

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.

Where you stand

From ad-hoc to optimized

The free evaluation places you on this maturity curve and maps the climb.

L1
L2
L3
L4
L5
  1. L1 · Ad-hoc / Siloed (NIST CSF: Partial) — Data scattered across SharePoint, file shares, CRMs, and legacy systems with no unified view. Tagging is manual and inconsistent; employees waste a large share of the day searching. No taxonomy, no governance, AI efforts hallucinate. Maps to NIST CSF Tier 1 — informal, reactive.
  2. L2 · Aware / Cataloged (NIST CSF: Risk Informed) — Sources inventoried and a basic data catalog exists, but metadata still varies by repository. Some access controls in place; governance is documented but not consistently applied. AI grounding is experimental. NIST CSF Tier 2 — risk-aware but not enterprise-wide.
  3. L3 · Governed Semantic Layer (NIST CSF: Repeatable) — A knowledge graph and semantic layer built on open W3C standards (RDF/SKOS/OWL/SHACL) connects key systems without ripping them out. Controlled-vocabulary tagging is automated; a centrally governed source of truth keeps metadata consistent. Access controls and data isolation are formalized. NIST CSF Tier 3 — repeatable, organization-wide policy.
  4. L4 · AI-Grounded & Reasoning (NIST CSF: Repeatable→Adaptive) — Graph RAG grounds generative AI so answers are explainable and traceable to source, cutting hallucinations to a trusted level. Inference and reasoning surface indirect, multi-hop relationships and hidden risks. Onboarding accelerates via connected learning content. Governance and security are continuously monitored.
  5. L5 · Optimized Enterprise Semantic Fabric (NIST CSF: Adaptive) — A full enterprise semantic layer / data fabric spans every silo, including newly acquired subsidiaries, with predictable performance at scale. Vendor-neutral, standards-based, and measured against ROI baselines. The bad-data tax is structurally reduced and AI investment compounds. NIST CSF Tier 4 — adaptive, continuously improving.
What you get

Outcomes, not vendor brochures

  • 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
Proven in the field

What this looks like in the field

Outcome patterns from across the industry — the shape of results vendor-neutral delivery produces.

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.
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.
Questions, answered

Frequently asked

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 evaluationBook a 15-minute call