AI Integrations
EA GraphLink’s MCP Server connects your Sparx EA repository to Google Gemini: the same way it connects to Microsoft Copilot, just in a different ecosystem.
For organizations standardized on Google Workspace rather than Microsoft 365 or Salesforce, Gemini is the natural AI assistant path. The narrative around connecting EA repositories to AI tools has been dominated by Microsoft Copilot. This guide is for organizations where that narrative doesn’t apply. If your stakeholders spend their time in Google Docs, Google Slides, Gmail, and Google Meet: not Teams and Outlook: then Gemini is where architecture data needs to surface.
EA GraphLink’s MCP server connects to Gemini using the open Model Context Protocol standard. Same protocol, different ecosystem. Your stakeholders ask architecture questions inside the Google tools they already use, and Gemini queries the repository directly. No separate portal. No EA access required.
The architecture data reaches the places where Google Workspace organizations make decisions.
Architecture data inside Google Workspace: without changing how your organization works.
Google Workspace organizations have historically had fewer options when it comes to connecting EA repositories to AI tools. The dominant integration narratives: Microsoft Copilot, Salesforce Agentforce: don’t apply to them. The result has been a gap: architecture intelligence that reaches Microsoft organizations through Copilot, but doesn’t reach Google Workspace organizations through their equivalent tools.
EA GraphLink’s MCP server closes that gap. Because MCP is an open standard: not a Microsoft-specific or Salesforce-specific protocol: EA GraphLink can expose the repository to any MCP-compatible AI tool. Gemini implements MCP. The connection is direct and uses the same technical path as every other MCP integration in this stack.
For Google Workspace organizations, this means:
You don’t change how you work. Gemini is already available in your Google Workspace tools. Architecture data surfaces inside Gemini the same way it would for any other connected data source. Ask a question in plain English. Get an answer from your authoritative architecture repository.
The same architecture intelligence benefits that Microsoft-centric organizations get from Copilot are now available in your environment. Business decisions made inside Google Meet, Docs, and Slides can be informed by live architecture data: not by whatever an architect managed to include in a presentation two weeks ago.
Your team’s work reaches further without additional effort. Architecture data is available to stakeholders on demand through the tools they already use, which means fewer ad-hoc requests interrupting architect work.
Architecture Q&A Inside Google Workspace Tools
A business analyst is preparing a briefing in Google Docs on the current state of a major application domain. Rather than emailing an architect for a summary, she opens Gemini in Docs and asks: “What applications support our customer-facing capabilities, and what’s their current lifecycle status?” Gemini queries the repository and returns a current summary. The briefing is accurate without an architect being interrupted.
Portfolio Queries During Google Slides Preparation
A CTO is preparing a board presentation on digital transformation. She’s building slides in Google Slides and needs to characterize the current application portfolio. She asks Gemini: “How many of our applications are currently in the legacy category, and what are the top technology risks?” Gemini returns current portfolio data. The board presentation reflects the actual repository, not a manually compiled snapshot.
Architecture-Aware Content Generation in Google Docs
A program manager is writing a business case for a major infrastructure initiative. She needs to articulate the current architecture and the business case for change. She asks Gemini to help draft a section: “Describe our current application architecture in the customer management domain, including key dependencies and known risks.” Gemini synthesizes repository data into narrative content. She edits and refines.
Architecture Context During Google Meet Discussions
A project team is in a Google Meet call discussing the impact of a proposed technology change. A stakeholder asks: “Which other applications depend on this platform?” Someone opens Gemini, asks the question, and the answer surfaces in seconds: from live repository data, not a deck prepared beforehand. The conversation moves forward with real information.
| Role | Primary Benefit | Secondary Benefit |
|---|---|---|
| Business Stakeholder | Architecture answers inside Google tools, no new platforms | No EA access required, no training needed |
| IT Executive | Architecture context in real-time decisions, Google Workspace native | Data-driven decisions without architect scheduling |
| Architecture Manager | Reduced ad-hoc requests, wider architecture reach | Pressure on repository quality: a positive forcing function |
| Project Manager | Real-time architecture data during Google Meet discussions | Less delay waiting for architect responses to project questions |
| Technical Lead | Architecture context surfaced in familiar Google environment | Reduced reliance on manual EA queries |
Your stakeholders already have Gemini. The repository data should be there too.
The adoption argument for Gemini is the same as for Copilot, applied to the Google Workspace context. If your organization is on Google Workspace, your stakeholders are already using Gemini. The tool is there. Connecting it to the EA repository is the step that makes it useful for architecture questions. There’s no new platform to roll out, no adoption campaign to run.
The alternative is continued information friction.
Without this integration, architecture data stays locked in EA. Business stakeholders in Google Meet discussions don’t have access. Architects get interrupted with questions. Decisions get made without architecture context. The integration removes that friction specifically for Google Workspace organizations: an audience for whom the Microsoft Copilot path isn’t relevant.
Same MCP standard as every other integration. No custom development.
EA GraphLink’s MCP server uses the open MCP standard. Connecting it to Gemini requires no custom development: it’s configuration, not code. This keeps the implementation cost and timeline predictable.
MDG quality becomes visible: which is valuable.
As with every AI assistant integration, Gemini surfaces your repository quality directly to stakeholders. If your data is accurate, Gemini returns accurate answers. If it’s inconsistent or outdated, Gemini reflects that. This visibility is uncomfortable in the short term, but it’s one of the most effective drivers of repository governance improvement.
Requires Google Workspace with Gemini for Business or Enterprise license.
Not all Google Workspace plans include Gemini at the level required for this integration. Gemini for Business or Enterprise is the relevant tier. Confirm your licensing before planning deployment: this is a real cost consideration. If your Google Workspace plan doesn’t include the required Gemini tier, this integration isn’t available without an upgrade.
Same data governance considerations as other cloud AI integrations.
When a stakeholder queries Gemini, architecture data flows from your EA repository through Google’s infrastructure. For organizations with data residency requirements or restrictions on cloud data flows, this requires a governance review before deployment. This is the same conversation that applies to Microsoft Copilot, Claude, and ChatGPT Enterprise. It’s not a blocker for most organizations, but it’s a step that can’t be skipped for regulated industries.
If your organization uses Microsoft or Salesforce tools as a primary platform, those integrations may provide better reach.
Gemini integration is specifically valuable for organizations where Google Workspace is the primary productivity platform. If your stakeholders are distributed across Google Workspace and M365, you may get better coverage from Copilot (which reaches the M365 audience) combined with Gemini (for Google Workspace users). Alternatively, Kernaro AI Hub is ecosystem-neutral: it reaches any stakeholder through a browser, regardless of which productivity platform they’re on.
EA GraphLink with MCP Server enabled. The MCP Server is the component that exposes your Sparx EA repository to Gemini. EA GraphLink is the prerequisite for all MCP integrations: Gemini, Copilot, Claude, Agentforce. If EA GraphLink isn’t deployed in your environment, that’s the starting point.
MDG Technology sufficient to trust at scale. Gemini answers are only as good as your repository governance. Before deploying Gemini integration for stakeholder use, your MDG Technology definition must be assessed for quality. Discover evaluates this. Organizations with weak MDG governance should address that first: surfacing poor-quality data to stakeholders via an AI assistant creates more problems than it solves.
Google Workspace with Gemini for Business or Enterprise license. Confirm the specific license level required for MCP connectivity with Google at time of deployment, as product tiers evolve.
MCP Connector Configuration. The connector links Gemini to your EA GraphLink MCP Server endpoint. Configuration specifies your EA GraphLink endpoint and authentication credentials. Sparx Services handles this as part of the Connect engagement.
Data Governance Alignment. Establish upfront which repository data is visible to Gemini queries and who can access what. Organizational boundaries and access controls should be defined before production rollout.
The formula is parallel to the Copilot value calculation: applied to your Google Workspace user base.
Formula:
(Architecture requests handled by Gemini per week × time saved per request × architect hourly rate × 52 weeks) +
(Stakeholder time saved on architecture Q&A × stakeholder hourly rate × weekly volume × 52 weeks)
Example:
Architects freed: 6 requests/week × 45 minutes/request × $130/hour × 50 weeks = $29,250/year
Stakeholder time saved: 25 requests/week × 30 minutes saved × $90/hour × 50 weeks = $33,750/year
Total annual value: ~$63,000
Factor in Gemini licensing costs (determined by your Google Workspace tier) to calculate net ROI. For organizations where business decisions involve significant capital: where architecture input at the right moment changes the quality of a major investment: the decision-quality component often dwarfs the time savings.
Microsoft Copilot
The M365 equivalent. If your organization uses M365 rather than Google Workspace as its primary productivity platform, Copilot is the more appropriate integration. Uses the same EA GraphLink MCP server. Same technical path, Microsoft ecosystem.
Kernaro AI Hub
Sparx Systems’ purpose-built architecture intelligence platform. No ecosystem dependency: any stakeholder with a browser can access it, regardless of whether they’re on Google Workspace, M365, or Salesforce. Lower ecosystem lock-in than Gemini. Purpose-built for architecture questions rather than general-purpose AI assistance. Relevant comparison if you want architecture intelligence that isn’t tied to a specific productivity platform.
Salesforce Agentforce
The Salesforce ecosystem equivalent. For organizations where Salesforce is the primary business platform rather than Google Workspace.
Claude
Not ecosystem-specific. Claude connects to EA GraphLink via MCP and is available without Google Workspace dependency. Stronger for deep analysis and documentation tasks. Less integrated into the Google Workspace workflow than Gemini for Google Workspace users.
What Google Workspace license is required for this integration?
Gemini for Business or Gemini for Enterprise tier is required. Standard Google Workspace plans do not include Gemini at the level needed for MCP connectivity. Confirm the specific licensing requirement with Google at the time of your deployment, as product tiers and capabilities evolve. This is a straightforward conversation to have with your Google account manager before planning the integration.
Is the Gemini integration technically different from the Copilot integration?
No: both use the same EA GraphLink MCP server and the same MCP protocol. The technical path is identical: EA GraphLink exposes an MCP endpoint, and the AI tool (Gemini or Copilot) connects to it via an MCP connector. The difference is the ecosystem, not the integration mechanism. The same EA GraphLink deployment can support both Gemini and Copilot connections simultaneously.
Can I run Gemini and Copilot integrations simultaneously from one EA GraphLink deployment?
Yes. EA GraphLink’s MCP server can support multiple simultaneous MCP client connections. If your organization has users on both Google Workspace and M365, you can connect Gemini for Google Workspace users and Copilot for M365 users from the same EA GraphLink instance. There’s no requirement to choose between them at the infrastructure level: the choice is typically driven by licensing budget.
Does Google Gemini store or use my architecture data for training?
Google’s data handling policies for Gemini for Business and Enterprise are more restrictive than consumer Gemini: specifically, Google does not use enterprise data for model training in the Business and Enterprise tiers. Confirm the current terms directly with Google for your specific license, as these policies are updated over time and vary by product tier.
What is the difference between Google Gemini and Kernaro AI Hub for this use case?
Gemini is a general-purpose AI assistant that integrates into Google Workspace tools. With the EA GraphLink MCP connection, it can answer architecture questions from within Docs, Slides, Gmail, and Meet. Kernaro AI Hub is a purpose-built architecture intelligence platform: a dedicated stakeholder portal specialized for architecture queries, accessible through any browser. Gemini’s advantage is seamless integration into Google Workspace workflows. Kernaro’s advantage is depth of architecture specialization and ecosystem independence. Some organizations use both.
How does Gemini compare to Claude for architecture reasoning tasks?
Both are capable AI models and both connect to EA GraphLink via MCP. The practical difference for most architecture questions is less about model capability and more about where your stakeholders prefer to work. Gemini integrates into Google Workspace tools natively: better for Google Workspace organizations where stakeholders are already in Docs and Meet. Claude is not tied to a specific productivity ecosystem: better for architects and technical users who want deep analysis capability outside of a Google Workspace context. For sophisticated architecture analysis tasks, Claude is generally regarded as strong; for stakeholder self-service within Google Workspace, Gemini’s integration advantage matters more.
What MDG quality is required for Gemini to return useful answers?
The threshold is: stereotypes applied consistently to element types, tagged values populated reliably for the attributes stakeholders will query, and relationships accurately maintained between applications, capabilities, and infrastructure. If your repository has these fundamentals in place, Gemini’s responses will be substantive. If key tagged values are incomplete (for example, lifecycle status is only populated for 40% of applications), Gemini will return incomplete answers: and will appear unreliable to stakeholders even though the limitation is a data quality issue, not an AI issue. Discover assesses MDG readiness before Connect begins.
How long does a Connect engagement take to deploy the Gemini integration?
Typically 12–16 weeks from engagement start to production, assuming EA GraphLink is already deployed and MDG governance is sufficient. Timeline drivers include: Google Workspace Gemini licensing procurement, security and data governance review, authentication configuration, and stakeholder rollout scope. Organizations with more complex security environments or governance improvement requirements should plan for the longer end. Sparx Services establishes a specific milestone plan at the start of Connect.
For Google Workspace organizations, this integration removes the gap that has existed between EA repositories and the AI tools their stakeholders actually use. Architecture data can now reach decisions as they happen: in Slides sessions, Meet calls, and Docs drafts: without requiring stakeholders to leave their environment.
The readiness question is whether your repository is in a state where surfacing it to stakeholders will be useful. If MDG governance is uncertain, start with Discover. If you’ve assessed your foundation and you’re ready to deploy:
[Start Your Connect Engagement]
Questions about whether Gemini is the right integration path for your organization?
[Let’s Discuss Your AI Strategy]
Talk to a Sparx Services architect about where your organization is on the journey and what the next stage looks like.