Connect enterprise knowledge so teams stop hunting, AI stops guessing, and decisions move faster. Unify entities, context, and relationships to improve retrieval, reasoning, recommendations, and decision quality at scale.
Knowledge graphs underperform when the objectives are fuzzy, ownership is fragmented, and adoption never reaches the workflows where AI value is actually won or lost. That’s when leaders start confronting questions like:
Are we...
…modeling shared business entities, relationships, and semantics, rather than just linking sources and calling disconnected metadata a graph?
…building ingestion, lineage, access control, and performance that can survive enterprise scale?
…giving the graph a clear mission, ownership model, and governance, rather than funding a platform no one is accountable to use or sustain?
…using the graph to improve RAG grounding, recommendations, reasoning, and analytics?
…driving adoption through high-value workflows with measurable impact?
Our Solution - Turn fragmented knowledge into connected AI advantage
Our Enterprise Knowledge Graphs Playbook helps leaders turn fragmented knowledge into connected context that improves retrieval quality, reasoning, recommendations, and decision support—so GenAI performs with more precision, trust, and business value.
Your Knowledge Graph Playbook @ a Glance
- Structured 1:1 discovery sessions to pinpoint where disconnected knowledge is slowing AI performance, decisions, and execution
- A targeted readiness scan to identify the highest-impact gaps across graph strategy, data modeling, governance, platform foundations, and adoption
- An executive brief covering enterprise knowledge graph best practices, watch-outs, and priority actions
- Clarifying where connected knowledge can create the most lift across retrieval, reasoning, recommendations, and analytics
- Exploring applied Use Cases, adoption best practices, and key “Watch Outs”
- Aligning on an actionable scaling plan
- Identifying and prioritizing the gaps most likely to limit graph value, AI performance, and adoption
- Exploring our 15 Enterprise Knowledge Graphs Acceleration Guides for targeted recommendations and resources
- Leveraging a GenAI Strategist-led planning session to define your action plan
- Graph Modeling and Neo4j Fundamentals
- Data Ingestion and Integration
- Querying and Analyzing the Knowledge Graph
- Graph Data Science and AI Integration
- Production Deployment and Scaling Strategies
- Co-deliver quick wins to “make it stick” and accelerate your target state delivery goals
- Configuring and customizing your Knowledge Graphs scaling playbook
- Operationalizing your Knowledge Graphs Target Operating Model (TOM) across ownership, workflows, and governance
- Optimizing and evolving your TOM as business priorities, data sources, and AI use cases expand
- Configuring and customizing your Knowledge Graphs metrics and insights plan
- Operationalizing your Knowledge Graphs Insights Plan and supporting operational processes
- Optimizing and evolving your insights to improve adoption, graph quality, and GenAI impact
- < 30 Days Wins: Lightly configurable resources and solutions
- 30 – 60 Day Wins: Lightly customizable Quick Wins
- 60 – 90 Day Wins: Increasingly high value Quick Win deliverables
- Baseline your knowledge graph foundations, adoption gaps, and supporting resources
- Tailor the plan to the graph use cases, ownership gaps, and modeling priorities that most affect AI value
- Deliver Quick Wins, build capability, and scale priority solutions through one integrated plan
- Identify your priority stakeholders, communication needs, and connected knowledge gaps
- Configure and deliver a tailored Knowledge Graphs communications plan, custom Comms Hub, and role-specific enablement assets
- Build and sustain momentum with explainers, demos, videos, and proof points.
- Define your quarterly Knowledge Graphs review, optimization, and adaptation process
- Enable quarterly strategy and scaling plan updates, with rapid response to major market, innovation, data, and competitor shifts
- Keep your Knowledge Graphs approach evergreen by continuously improving how connected knowledge is modeled, governed, and scaled
- Identify where your teams need targeted coaching to overcome knowledge modeling, ontology, platform, or execution gaps
- Deliver tailored expert support, working sessions, and practical guidance
- Help your teams strengthen connected knowledge foundations, improve GenAI context and insight quality, and keep your Knowledge Graphs efforts moving forward
Choose Your On-Ramp...
Choose the right on-ramp for your Knowledge Graphs journey—whether you’re looking to rapidly align and mobilize, solve targeted challenges, or scale your Knowledge Graphs holistically.
An Accelerated Alignment & Action Planning Sprint
A fast-paced leadership alignment and action planning sprint to:
- Baseline your current knowledge graphs maturity
- Explore connected knowledge best practices
- Align on top priorities
- Define your path forward
- Identify near-term Quick Wins
Build the Knowledge Graphs Systems GenAI Scale Demands
Confidently scale your Knowledge Graphs with a tailored TOM that helps you turn connected knowledge into sharper GenAI performance, better decisions, and stronger business outcomes.
Targeted Knowledge Graphs Solutions
Rapidly solve targeted Knowledge Graphs scaling challenges, including:
- Baseline your current connected knowledge and modeling gaps
- Solve a high-priority knowledge graph challenge
- Clarify your target knowledge priorities
- Align on practical actions to move forward
- Deliver focused progress in a matter of weeks
Outcomes you can expect
Turn disconnected enterprise knowledge into a more connected foundation for GenAI understanding and retrieval.
Improve how well GenAI solutions understand relationships, meaning, and business context across your knowledge landscape.
Strengthen the relevance, consistency, and usefulness of the knowledge your GenAI solutions rely on.
Reduce the time it takes to find, connect, and apply the right knowledge across systems and workflows.
Turn stronger knowledge graph foundations into better insights, smarter GenAI performance, and more meaningful business results.
Complimentary Resources
Curious About What “Great Looks Like”?
Review our “Knowledge Graphs” Whitepaper
Want to See How You Compare?
Complete our Knowledge Graphs Scan or Assessment
Want an easy way to come up to speed?
Click here to listen to our Knowledge Graphs Podcast
Want to dig deeper?
Click here to check out our library of YouTube videosFrequently Asked Questions
- Why do we need stronger Knowledge Graph capabilities now?
Because GenAI loses quality and context when enterprise knowledge stays fragmented across systems and content. - What outcomes should we expect from this work?
Richer context, stronger relevance, faster retrieval, and greater trust in GenAI responses. - What happens if we don’t strengthen Knowledge Graph capabilities?
Disconnected knowledge leads to weaker context, slower retrieval, and less useful experiences.
- What do you mean by “Knowledge Graphs” in a GenAI context?
A structured knowledge layer that gives GenAI richer business context. - What are the main deliverables from this work?
Graph priorities, knowledge design direction, and a context roadmap. - What do “Quick Wins” look like in Knowledge Graph work?
Map key entities, connect priority sources, and improve access to business context.
- Does this only apply to highly complex enterprise environments?
No—it helps wherever GenAI needs connected business knowledge to improve relevance and speed. - Can this work alongside existing search or retrieval approaches?
Yes—it strengthens existing search and retrieval by adding structure, context, and relationship awareness. - Does this cover more than data linking?
Yes—it covers concept modeling and knowledge structure—not just linking data.
- How do you decide where Knowledge Graphs will matter most?
We focus on the use cases where connected knowledge most improves context and explainability. - How do you keep Knowledge Graph work from becoming too broad or theoretical?
We focus on the knowledge links that improve real experiences, not modeling everything. - How do you connect Knowledge Graphs to business impact?
We tie graph investments to richer context, stronger trust, and more differentiated experiences.
- Who should be involved from our side?
Data, product, technology, and business leaders who own knowledge sources and information architecture. - How do you keep Knowledge Graph efforts aligned to business priorities?
We focus the graph on the concepts and relationships that improve priority use cases. - How do you sustain this after the initial work is done?
We build a knowledge foundation teams can extend as new sources and use cases emerge.