From Lakehouse to Intelligence
Enterprise AI delivery on Databricks — natively. Seven interconnected capability layers that transform your data platform into a production AI intelligence system. Milestone is the firm that has already built it.
Two-track Lakehouse
Delta Lake + AI Gold Tables
Structured and unstructured data converging at the AI consumption layer — one governed platform.
Knowledge retrieval
Mosaic AI Vector Search + GraphRAG
Content-type-aware chunking, hybrid search, knowledge graph enrichment — production-validated across client deployments.
Agentic AI
Agent Bricks · Lakebase · MLflow 3.0
The data foundation that makes Databricks Agent Bricks perform. Better data, dramatically better agents.
Built On
Databricks Partner · Multi-cloud delivery · AWS · Azure · GCP
200+
Enterprise clients globally
3,500+
Technology professionals worldwide
29+
Years in IT services
36
Countries served
80%
Report generation time cut — BFSI client
Why AI stalls
Two Crises. One Root Cause.
Your data estate has a compounding problem. Your AI investments are paying the price. In most cases, the root cause is not the model — it is the data foundation.
The Root Cause
Data built for analytics cannot power AI that reasons. The architecture is wrong at the foundation — not the model.
The Milestone Answer
A structured system of seven capability layers — built natively on Databricks — that closes both crises simultaneously
How We Deliver
Sequenced With You. Delivered By AI Engineering PODs.
Two things stall Databricks programs: building the wrong thing first, and taking too long to build it.
The first is a decision your team owns and we help you make. The second is ours to solve.
Sequencing & Prioritization
We do not hand you a roadmap. We build one with your team.
The order of work is a business decision before it is a technical one. Structured sessions with your data, risk, and business leads establish which use case pays back across the others, what has to be settled at the start so it is not retrofitted later, and what can wait.
Every workstream traces to a KPI owned by a named executive on your side. Domain workstreams ship on their own while platform accelerators run underneath in parallel — so each phase costs less than the one before it.
Output: A prioritized Databricks roadmap in two weeks.
AI Engineering PODs
Cross-functional squads that compress each phase, inside your controls.
A POD is a forward-deployed team with single-point accountability: an architect leading, with data, platform, and ML engineering underneath. The toolchain — including Genie Code — generates production-grade Spark and SQL from your Unity Catalog metadata, and every output passes the same review gates your own engineers work under.
Context from your repositories, standards, and business rules carries forward — so each phase starts ahead of the last rather than at a blank page. The same POD carries into run, where AI gold tables and metric definitions need continuous curation.
40–60%
faster engineering throughput
35%
shorter development cycles
80%
less effort on test creation
Measured across delivered AI-augmented engineering engagements. Platform environments varied.
What We Deliver
Six Practice Areas. Both Data Tracks. One Platform.
Every capability built and validated on Databricks. Structured and unstructured data treated equally throughout.
Two-track lakehouse architecture
Medallion architecture with dual gold outputs — BI tables and AI tables, built alongside each other
Traditional gold tables serve BI dashboards. AI systems need something fundamentally different — denormalized wide records with SCD Type 2 history, business-meaningful identifiers, AI-readable column descriptions, and event-driven refresh. We build both from the same silver layer, leaving existing workloads untouched.
Databricks native stack
Delta Lake + Iceberg (unified in Unity Catalog) · Lakeflow Connect for managed CDC · Lakebase for OLTP and online feature serving · Medallion dual gold-layer pattern
- Cloud-native lakehouse design on AWS, Azure, and GCP with unified governance
- Medallion architecture: bronze (raw), silver (cleansed, entity-resolved), gold BI tables and gold AI tables in parallel
- Delta Lake and Apache Iceberg unified under Unity Catalog — no format migration required
- Lakeflow Connect replaces custom CDC connectors — managed, governed, Databricks-native
- Migration from legacy data warehouses: Teradata, Synapse, Snowflake — with AI-ready output from day one
- Lakebase online feature store: sub-10ms feature serving to AI agents — GA on Databricks
Two-track architecture + AI governance layer
Pipelines that serve both analytics workloads and AI agents — simultaneously
Data engineering for AI differs from analytics engineering. Data contracts must include AI-readable column descriptions, staleness tolerance per use case, and minimum history depth for temporal reasoning — not just schema definitions. We build to this standard from the start.
Databricks native stack
Lakeflow Designer (no-code ETL) · Databricks DLT · Autoloader · dbt · CI/CD via GitHub Actions · Lakehouse Monitoring for DQ
- Batch and streaming pipelines: Spark, DLT, Autoloader — both data tracks
- DataOps: CI/CD for data, lineage, observability — production-grade from sprint one
- Data contracts with AI-extended elements: column descriptions, staleness tolerance, minimum history depth
- Real-time change data capture for high-velocity operational data sources
- Pipeline performance tuning and cost-optimised cluster governance on Databricks Serverless
Semantic intelligence layer
When AI and BI produce the same number from the same metric definition, trust is established
AI systems and BI dashboards frequently calculate the same metrics differently — different SQL logic, different filters, different join strategies. The solution is Unity Catalog Metrics: one definition, consistent across dashboards, SQL, and AI agents. This architectural decision tends to restore user trust in AI outputs faster than most other changes.
Databricks native stack
Unity Catalog Metrics (semantic layer) · AI/BI Genie with Deep Research Mode · AI Forecasting and Top Drivers · Databricks One (business user portal)
- Unity Catalog Metrics as the semantic layer — KPIs defined once, consistent across Genie, dashboards, SQL, and AI agents
- AI/BI Genie rollout — natural language analytics for business users, grounded in certified UC Metrics
- Self-serve analytics enablement: Databricks One for non-technical users
- SQL Warehouse setup, performance optimisation, and cost management
- AI-powered forecasting and anomaly explanation — one-click in Databricks dashboards
Knowledge retrieval + AI decision traceability
Models that run in production — with the lineage, monitoring, and governance to keep them there
MLflow 3.0 changes the game for production AI. Full agent tracing, LLM judges for automated quality scoring, deployment jobs that evaluate before promoting — AI outputs are traceable from source data through retrieval to response. We build this into our ML engagements from day one.
Databricks native stack
MLflow 3.0 (agent tracing, LLM judges, deployment jobs) · Mosaic AI Feature Store · Model Serving · Serverless GPU · Lakehouse Monitoring
- ML platform setup: MLflow 3.0, Feature Store, Model Serving — governance from the start
- Custom model development, training, and fine-tuning on Databricks Serverless GPU
- Automated RAG quality scoring using MLflow LLM judges — no manual evaluation bottleneck
- Model monitoring, drift detection, and automated retraining pipelines
- Every AI output traceable in Unity Catalog system tables — SQL-queryable compliance audit trail
Knowledge retrieval + Graph reasoning + Agentic AI readiness
Agent Bricks optimizes against your data. We make that data excellent.
Databricks Agent Bricks auto-optimises agents from your Unity Catalog data. Better AI gold tables, richer UC Metrics coverage, higher-quality Vector Search retrieval, and knowledge graph routing — these consistently compound into significantly better Agent Bricks output. We don’t compete with Agent Bricks. We make it perform.
Databricks native stack
Mosaic AI Agent Bricks · Mosaic AI Vector Search (storage-optimized, 7× lower cost) · Mosaic AI Agent Framework · Lakebase online feature store · Databricks Apps (HITL)
- RAG pipelines on Mosaic AI Vector Search — storage-optimized, hybrid search (dense + BM25) enabled by default
- Knowledge graph enrichment layer: significant query deflection before the LLM is invoked, reducing inference costs
- Multi-agent orchestration with Mosaic AI Agent Framework — Supervisor + specialist Agent Bricks patterns
- HITL human approval workflows on Databricks Apps — wired to MLflow deployment jobs
- LLM ops, prompt governance, guardrails, and production deployment — full lifecycle ownership
AI governance layer + AI decision traceability
The three governance gaps we close on every Databricks engagement
Traditional governance tools were built for structured, catalogued, relational data. They typically have limited coverage for the threat surface that AI creates: unclassified content entering vector pipelines, limited lineage from AI output to source data, and vector stores lacking row-level security equivalents. We address all three gaps.
Databricks native stack
Unity Catalog (ABAC, row filters, column masking, lineage) · Lakehouse Monitoring (12 DQ dimensions) · MLflow 3.0 system tables · Unity Catalog Metrics · AI-aware DLP at Lakeflow ingestion
- Classification gap closed: UC auto-classification at Lakeflow ingestion — every document tagged before embedding
- Lineage gap closed: MLflow 3.0 system tables in Unity Catalog — source through transformation to AI output, SQL-queryable
- Vector access control gap closed: Mosaic AI Vector Search indexes governed by Unity Catalog fine-grained permissions
- GDPR, HIPAA, SOC 2, EU AI Act compliance alignment — both structured and unstructured data tracks
- Databricks cost modeling, FinOps dashboards, and Serverless budget governance
Our methodology
LakeMind™ — Seven Capability Layers. One Intelligence System.
A structured system where each layer maps to a Databricks capability stack — validated through client deployments and our active innovation lab. We deliver LakeMind™ through our AI Engineering POD model — cross-functional squads of data engineers, ML engineers, and Databricks specialists embedded in your delivery cycle. Genie Code accelerates development by generating production-grade Spark and SQL directly from your Unity Catalog metadata, compressing timelines while keeping governance in place from day one.
STRUCTURED + UNSTRUCTURED
Two-track lakehouse architecture
Structured and unstructured data converging at the AI consumption layer
- Delta Lake · Lakebase · Lakeflow
GOVERNANCE + COMPLIANCE
AI governance and trust layer
Unity Catalog as the unified governance plane for both data tracks
- UC · Classification · Metrics · Lineage
RAG + VECTOR SEARCH
Enterprise knowledge retrieval
Chunking, embedding, hybrid search, and RAG architecture selection
- Mosaic AI Vector Search · Hybrid search
LINEAGE + RESPONSIBLE AI
AI decision traceability
Every AI output traceable from source data to response — in Unity Catalog
- MLflow 3.0 · System tables · HITL
SEMANTIC INTELLIGENCE
Semantic intelligence layer
Meaningful query deflection via graph and UC Metrics — reducing LLM inference costs
- UC Metrics · Genie · Entity graph
GRAPH + RELATIONSHIP AI
Knowledge graph reasoning
Relationship intelligence over structured and unstructured data combined
- Knowledge graph database + Mosaic AI Vector Search
AGENTIC AI READINESS — THE CULMINATION
Agentic AI data readiness
The data foundation that makes autonomous AI agents actually perform — each other layer feeds into this one
Databricks Native
Agent Bricks · Lakebase Online Feature Store · Mosaic AI Agent Framework · Serverless GPU · Unity Catalog permissions
Proven results
Real Outcomes. Real Clients. Delivered on Databricks.
These are not projections or benchmarks. They are results from delivered engagements.
The foundation comes first. Here’s what it made possible on Databricks.
Global Financial Services
BUILT
Delta Lake medallion with dual gold outputs. Lakeflow Connect replaced 6 custom CDC pipelines. Unity Catalog ABAC closed PCI/PII classification gap from day one. Lakebase enabled sub-10ms feature serving to fraud detection agents.
Unlocked
90% lower data latency
2TB+ processed monthly via Databricks Serverless · Fraud model features served in real time for the first time
Global Events Co. · 75+ countries
BUILT
Unity Catalog Metrics activated as the semantic layer across 20+ business units that had never shared a common KPI definition. Single governed Delta Lake with 40+ certified Data Products. AI/BI Genie consumed UC Metrics for consistent cross-BU analytics.
Unlocked
20+ BUs on one model
90% of data quality issues eliminated via Lakehouse Monitoring · 30% faster partner onboarding with UC-governed data sharing
Leading Vocational Education Provider
BUILT
Predictive dropout models built and versioned in MLflow, trained on unified admissions, academics, LMS, and student support data in Databricks. AI risk scores generated continuously across the enrolled student population — at-risk flags surfacing weeks before disengagement. Databricks Workflows automated personalised intervention routing: the right advisor, the right resource, triggered without manual triage.
Unlocked
10% reduction in student dropout rate
At-risk students flagged weeks before dropout · Personalized interventions routed automatically · Retention improvements measured across campuses and programs
Global Industrial Manufacturer
BUILT
Metadata-driven data integration across multiple geographies and legacy systems via Lakeflow Connect. Sensitive data classified and separated at Lakeflow ingestion using UC auto-classification. Knowledge graph connected asset entities across structured Delta tables and unstructured maintenance documents.
Unlocked
60–70% less integration effort
Every subsequent Databricks workload benefited from the shared UC catalog · Enterprise security without a platform rebuild