AI-ready data in 11 weeks
Your AI is only as good as your data foundation. We build production-grade semantic layers, knowledge graphs, and lakehouse architectures that make petabyte-scale data queryable by AI agents.
Why most AI projects fail at the data layer
A data intelligence platform is a unified architecture that combines lakehouse storage, semantic layers, knowledge graphs, and governance frameworks to make enterprise data AI-ready. It bridges fragmented warehouses, lakes, SaaS tools, and legacy systems into a single queryable foundation that AI models can reliably consume.
Our Data Intelligence Platform creates a unified semantic layer across your entire data estate. Knowledge graphs capture business relationships, data products enable self-service consumption, and governance ensures trust at scale.
Built on Databricks, Snowflake, or Microsoft Fabric — we accelerate platform deployment from 6-12 months down to 11 weeks using pre-built ingestion patterns, transformation templates, and quality frameworks.
Every layer of the data intelligence stack
From raw ingestion to AI-ready data products — accelerated with production-tested patterns.
Semantic Layer
Unified business definitions across all data sources. Natural language query interfaces that let AI agents and business users access consistent metrics without SQL knowledge.
Knowledge Graphs
Entity relationships, lineage tracking, and business context captured in queryable graph structures. Powers AI reasoning, impact analysis, and automated discovery.
Lakehouse Accelerators
Pre-built ingestion, transformation, and quality patterns for Databricks, Snowflake, and Fabric. Delta Live Tables, Snowpark, and Data Factory templates ship in days.
Data Products
Self-contained, discoverable, and contractually guaranteed data assets. Domain teams own their products while platform teams provide infrastructure and governance.
Data Governance
Automated classification, access control, lineage tracking, and quality monitoring. Unity Catalog, Snowflake governance, or Purview integration depending on your platform.
Self-Service Analytics
Business users query data through natural language interfaces, curated dashboards, and governed notebooks. Democratize access without compromising security or quality.
AI-ready in 11 weeks
A proven methodology that compresses 12-month data programs into sprint-based delivery.
Data Estate Assessment
Catalog existing sources, identify critical data domains, and map current-state architecture. Deliverable: prioritized domain roadmap with quick-win identification.
Platform Foundation
Deploy lakehouse infrastructure (Databricks/Snowflake/Fabric), configure governance frameworks, and establish CI/CD pipelines for data assets. Weeks 2-4.
Domain Onboarding
Priority data domains are modeled, ingested, and transformed using accelerator templates. Data quality rules and contracts are implemented. Weeks 5-8.
Semantic & AI Layer
Semantic definitions, knowledge graphs, and AI-ready feature stores are built on top of the governed data foundation. Natural language interfaces activated. Weeks 8-10.
Production & Handover
Platform is hardened for production workloads with monitoring, alerting, and operational runbooks. Team training and capability transfer ensure self-sufficiency. Week 11.
Platform Impact
Frequently asked questions
A data intelligence platform is a unified architecture that combines lakehouse storage, semantic layers, knowledge graphs, and governance frameworks to make enterprise data AI-ready. It creates a single source of truth across fragmented data estates, enabling AI agents and business users to query petabyte-scale data through natural language interfaces.
Using pre-built ingestion patterns, transformation templates, and quality frameworks, enterprises can make their data AI-ready in 11 weeks instead of the typical 6–12 months. The accelerated approach uses sprint-based delivery across platform foundation, domain onboarding, and semantic layer deployment.
A semantic layer provides unified business definitions across all data sources, creating natural language query interfaces that let AI agents and business users access consistent metrics without SQL knowledge. It bridges the gap between raw data and AI consumption by standardizing definitions, relationships, and access patterns.
Maharadha supports three major data platforms: Databricks (Delta Live Tables, Unity Catalog), Snowflake (Snowpark, Snowflake governance), and Microsoft Fabric (Data Factory, OneLake). Platform selection is based on your existing ecosystem, workload characteristics, and governance requirements.
Data mesh architecture is a decentralized approach where domain teams own their data products while platform teams provide shared infrastructure and governance. Self-contained, discoverable, and contractually guaranteed data assets enable self-service consumption at scale without creating bottlenecks around a central data team.
Ready to make your data AI-ready?
Share your data landscape. We'll assess readiness, recommend the right platform, and deliver a production foundation in 11 weeks.