From AI Pilots to a Secure, Organization-Wide GenAI Foundation
GENAI DEPLOYMENT BLUEPRINT - ENTERPRISE LLM ARCHITECTURE & FOUNDATION SETUP
Everyone in your organization wants to point an LLM at company data - but is your data & analytics ecosystem actually ready for it? Ungoverned access, inconsistent answers, and PII exposure are what separate a promising pilot project from a production rollout. Our GenAI Deployment Blueprint evaluates the LLM-readiness of your data ecosystem and identifies the gaps and the specific action points to close them. From there, we tailor our proven rollout approach to your environment and guide your team through setting up a working, guarded configuration - your administrators keep the keys, we bring the deployment experience.
Outcome: a working GenAI foundation in up to 8 weeks.
Key Features
- Structured evaluation of your Data & Analytics ecosystem's LLM-readiness - data platform, semantic layer, access interfaces, documentation, and governance.
- Proven deployment blueprint from previous organization-wide LLM rollouts – refined with your infrastructure and security teams to incorporate the considerations applicable to your setup.
- Base configuration for Anthropic Claude - set up by your team with our guidance; principles designed to extend to other LLM providers, with every configuration choice documented and justified.
- Security and privacy by design: row-level and column-level security definition and enforcement, PII handling strategy, and data masking.
- Guided, security-first delivery model: your IT team performs all hands-on configuration in your environment with our step-by-step guidance - no external administrative access required.
- LLM-friendly data access architecture definition - REST APIs, custom MCP servers, and/or Python-based access - designed to fit your existing ecosystem.
- Architecture optimized for correct answers: flat vs. multi-table query trade-offs, semantic consistency, and result reliability considerations.
- Technical guardrails and usage governance: hard-blocked actions, tiered warnings, usage logging, plus a GenAI usage policy.
PROJECT ACTIVITIES
Up to 8 weeks to a working foundation
DELIVERABLES
Data & Analytics LLM-readiness assessment
Deployment blueprint tailored to your environment, with documented design decisions
Working foundation configuration - set up by your team under our guidance
Security, privacy, and guardrails framework
Organization-wide usage policy and guidelines
Prioritized rollout roadmap and next action items
technology & Ecosystem
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Anthropic Claude
Our primary GenAI platform for enterprise deployments - strong reasoning over structured data, mature enterprise controls, MCP support[.
Model Context Protocol (MCP), REST APIs & Python
We define the access architecture that matches the needs and capabilities of your ecosystem.
Compatible data platforms
Microsoft Fabric, Databricks, Snowflake, Google BigQuery, Azure Synapse, Azure SQL, Oracle - or any platform with a queryable semantic or data model layer.
Assumptions
- Customer has an existing data & analytics platform (e.g. warehouse or lakehouse) with defined data models
- Customer provides (or procures) the required LLM subscriptions, API capacity, and cloud resources
- Hands-on configuration is performed by the customer's IT/infrastructure team, with Scandic Fusion enabling, guiding and verifying
- IT, security, and infrastructure stakeholders are available for workshops and configuration sessions
- Scope covers the readiness assessment, tailored blueprint, foundation configuration, guardrails, and usage policy - implementation of the access layer (REST APIs, MCP servers) and organization-wide rollout execution are available as add-ons
Optional Add-Ons
- Access layer implementation - REST APIs and/or custom MCP servers built against the defined architecture, exposing your data products to authorized AI tools
- Response-accuracy validation and evaluation framework - jointly defined test question set, validation runs against the implemented architecture, and automated regression testing of LLM responses
- Organization-wide rollout support - we stay alongside your team through the full deployment journey, advising as configuration and scope decisions arise
- Additional LLM provider configurations
- Data model and documentation improvements, if the readiness assessment reveals gaps
What’s Next?
