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.

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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
1

Security and privacy workshops

RLS/CLS definition and enforcement mechanisms, PII strategy, data masking approach, and audit requirements - agreed with IT and security stakeholders.

2

Discovery and LLM-readiness evaluation

Structured assessment of your data platform, semantic layer, APIs, access controls, and metadata: what's already LLM-ready, what isn't, and why it matters.

3

Blueprint tailoring and target architecture

Refining our proven deployment blueprint together with your infrastructure and security teams, surfacing the considerations specific to your setup, and defining the LLM access architecture that fits your ecosystem (REST API, MCP, Python).

4

Accuracy-oriented architecture design

Designing the semantic context and access patterns for maximum probability of correct responses: flat vs. multi-table trade-offs, naming and metadata conventions, and consistency principles, documentation requirements.

5

Guided foundation configuration

Your administrators set up the base configuration for Anthropic Claude with our step-by-step guidance, including the choice between broader and narrower configuration scopes - with documented rationale for each decision.

6

Guardrails setup

Technical enforcement of forbidden actions, warning tiers for sensitive operations, and usage logging - configured by your team, designed and verified together.

7

Policy and usage guidelines

GenAI usage policy draft, covering permitted use, data handling, and escalation paths.

8

Action points and further rollout roadmap

Working sessions with IT to identify action points, leverage the already-LLM-ready parts of your ecosystem, and prioritize the organization-wide rollout.

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

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?

Contact us to schedule a discovery call and map the path from scattered AI experiments to a secure, organization-wide foundation.
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