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AI Data Readiness Assessment
A 3-week diagnostic that maps your existing data landscape, scores data quality across critical domains, identifies AI use case data dependencies, and produces a prioritized data modernization roadmap.
AI Data Foundation Services
Build the Data Layer Your AI Actually Needs
Mirketa's services unify, govern, and prepare your enterprise data across Salesforce Data Cloud, Snowflake, and your existing systems so your AI agents, models, and analytics can finally deliver trusted, production-grade results.
- Salesforce Data Cloud Certified
- Snowflake Partner
- HIPAA & GDPR Ready
Services
What Mirketa's AI Data Foundation Services Include
Salesforce Data Cloud Implementation
End-to-end Salesforce Data Cloud consulting and implementation including data ingestion, identity resolution, calculated insights, segmentation, activation, and Agentforce data grounding. Includes Zero Copy integration with Snowflake, Databricks, BigQuery, and Redshift.
Enterprise Data Integration
Salesforce Integration Services and enterprise iPaaS implementation using Boomi, MuleSoft, and native API frameworks connecting CRM, ERP, data warehouses, and SaaS systems into a single AI-ready data fabric.
RAG Implementation Services
Design and deployment of Retrieval-Augmented Generation systems that ground large language models in your enterprise data. Includes embedding strategy, vector database setup, chunking architecture, hybrid search, and evaluation frameworks.
Data Governance for AI
Implementation of data governance frameworks tailored for AI workloads covering data lineage, access controls, PII handling, consent management, model-data versioning, and audit trails for AI decision explainability.
Master Data Management (MDM)
Unified customer, product, and account master data: the foundation that makes AI personalization, agent grounding, and predictive analytics actually trustworthy at enterprise scale.
Reference Architecture
Mirketa's AI-Ready Enterprise Data Architecture
Layer 1
Source Systems
Your existing systems of record - Salesforce CRM, NetSuite ERP, Oracle Fusion, ServiceNow, Workday, marketing automation, and data lakes - connected via Salesforce Integration Services, Boomi, MuleSoft, or native APIs.
Layer 2
Unified Data Fabric
Salesforce Data Cloud as the customer data hub, integrated with Snowflake / Databricks / BigQuery as the analytical foundation. Zero Copy architecture eliminates duplicate data movement and ensures every AI workload runs on the same trusted dataset.
Layer 3
AI Data Services
Vector databases (Pinecone, Weaviate, pgvector), embedding generation pipelines, RAG retrievers, semantic search indexes, and feature stores that prepare data for both generative and predictive AI consumption.
Layer 4
Governance & Observability
Data lineage, quality monitoring, access controls, PII detection, consent management, and audit trails, all integrated with your AI Roadmap & Governance framework for end-to-end compliance.
Layer 5
AI Consumption Layer
AI agents (Agentforce, custom LLM agents), predictive analytics, Einstein AI, dashboards, and applications, all consuming from the same governed data layer for consistent, trustworthy results.
Technology Partners
Salesforce Data Cloud, Snowflake, and the AI Data Stack We Build On
Mirketa is platform-pragmatic. We recommend the right architecture for your stack, budget, and use case, not a single-vendor lock-in.
Customer Data Platform
Our primary recommendation for customer data unification, Agentforce grounding, and CRM-integrated AI. Native integration with Salesforce CRM eliminates overhead.
Analytical Foundation
For analytical data foundations and Zero Copy integration with Data Cloud. Supports both structured analytics and AI workloads from the same platform.
Vector Databases
For RAG and semantic search workloads, including Salesforce native vector storage for Agentforce-grounded AI agents.
Integration Platforms
Enterprise iPaaS and native Salesforce Integration Services for connecting source systems into a unified AI-ready data fabric.
AI Platforms
We build data foundations that work with any AI platform, grounding your chosen LLM in your enterprise data for trusted, production-grade results.
Governance Tools
Data governance, lineage, and audit-ready documentation built into every engagement, not bolted on as a separate phase.
Our Approach
How We Build Your AI Data Foundation: 4-Phase Methodology
01
Assess
Map current-state data architecture, score data quality, identify AI use case data requirements, and document gaps across all critical data domains.
Output: AI data readiness scorecard and prioritized backlog02
Design
Architect the target-state data foundation. Select platforms, define integration patterns, design data models, and plan governance controls for AI workloads.
Output: Reference architecture, technology selection, implementation roadmap03
Build
Execute data integration, Salesforce Data Cloud implementation, vector database deployment, RAG pipeline setup, and governance configuration in production.
Output: Production AI data foundation04
Operate
Ongoing managed services: data quality monitoring, governance reviews, schema evolution, AI model-data alignment, and performance optimization.
Output: Continuously improving, audit-ready AI data foundationWhy Mirketa
Why Enterprises Trust Mirketa for AI Data Foundation Services
Cross-platform expertise
Certified specialists in Salesforce Data Cloud, Snowflake, Databricks, and vector databases. We build hybrid architectures that fit your real stack, not idealized vendor demos.
AI-first data architecture
Every foundation we design is built for both today's analytics and tomorrow's AI agents, eliminating expensive re-architecture later.
Salesforce Integration Services depth
15 years of Salesforce CRM consulting and Salesforce Implementation Services means we know how to integrate Data Cloud with the rest of your enterprise without breaking what already works.
Governance built in, not bolted on
Data governance, lineage, and audit-ready documentation are part of every engagement, not a separate phase.
Industry data models
Pre-built nonprofit, healthcare, manufacturing, and hi-tech data models cut implementation time by 30–50%.
Managed services continuity
Mirketa stays with you after go-live with ongoing data quality, integration, and AI data lifecycle management.
15
Years Salesforce & Data Experience
30–50%
Faster with Pre-built Data Models
45%
Reduction in Resolution Time (RAG)
60%
Tier-1 Case Deflection Rate
FAQ
Frequently Asked Questions About AI Data Foundation Services
No. Salesforce Data Cloud is our most-recommended customer data layer for organizations already running Salesforce CRM, because it eliminates integration overhead and natively grounds Agentforce. But we build AI data foundations on Snowflake, Databricks, BigQuery, and hybrid architectures as well. The right answer depends on your existing stack, AI use cases, and governance requirements.
A traditional data warehouse is optimized for structured analytical queries: reports, dashboards, BI. An AI data foundation extends that to support unstructured data (documents, conversations, images), vector embeddings, real-time retrieval for RAG applications, and continuous data flows for agentic AI. It also adds AI-specific governance like model-data lineage and explainability.
A focused Salesforce Data Cloud implementation connecting 3–5 source systems with identity resolution and basic segmentation typically takes 8–12 weeks. Enterprise Data Cloud rollouts with Agentforce grounding, advanced calculated insights, and Zero Copy integration with Snowflake run 16–24 weeks. Mirketa's pre-built accelerators reduce these timelines by 30–40%.
Retrieval-Augmented Generation (RAG) is the architecture pattern that grounds large language models in your enterprise data, eliminating hallucinations and making AI answers traceable to source documents. If you're deploying AI agents (Agentforce, custom chatbots, internal copilots) that need to answer questions using your company's specific knowledge, you need RAG, or you'll deploy an AI that confidently invents wrong answers.
Every AI data foundation we build includes governance controls mapped to your industry: HIPAA for healthcare, SOC 2 and GDPR for SaaS, PCI-DSS for financial services. We implement PII detection and masking, consent management, fine-grained access controls, audit logging, and AI decision explainability from day one. See our AI Roadmap & Governance services for the full governance framework.
Yes. Data warehouse modernization to AI-ready architectures is one of our most common engagements. We've migrated organizations from legacy warehouses (Oracle, Teradata, on-prem SQL Server) to modern AI-ready stacks built on Snowflake, Databricks, and Salesforce Data Cloud with minimal disruption to existing analytics consumers.
Ready to Build an AI-Ready Data Foundation?
Book a free 30-minute AI data readiness call with one of Mirketa's senior data architects. We'll map your current data landscape, identify the gaps blocking your AI initiatives, and give you a clear next step, whether that's Salesforce Data Cloud, a Snowflake-based architecture, or a hybrid approach.
- No commitment required, just a clear-eyed assessment
- Senior data architect, not a sales rep
- Platform-agnostic recommendation for your stack
- Response within 1 business day
No commitment required, just a clear-eyed assessmentGet Started
Schedule a Data Foundations Consultation
Tell us about your current data landscape and AI goals. A senior data architect - not a sales rep - will map the gaps and recommend your next step.
- No commitment required, just a clear-eyed assessment
- Senior data architect, not a sales rep
- Platform-agnostic recommendation for your stack
- Response within 1 business day
Certified PartnerSOC 2 CertifiedHIPAA ReadyEnterprise Ready
Certified Partner
SOC 2 Certified
HIPAA Ready
Enterprise Ready