Databricks Partnership

The Lakehouse Is Not the Destination. Production AI Is

Most lakehouse programs are sold as a data platform project and judged as one. Then the AI use case arrives and the hard question surfaces: can anyone explain where this number came from. We build lakehouses on Databricks with that question already answered, because we know the source systems the data came out of.
Start with the Data Estate Diagnostic
See how we deliver...
Why Infolob

Anyone can land data in a Lakehouse. Few can tell you what it means.

The difficult part of a Databricks program is almost never Databricks. It is the twenty years of ERP, MES, and Oracle schema on the other side of the pipeline, and the business logic buried in it that nobody documented.

We know the source systems

Where lakehouse programs actually break

Seventeen years inside Oracle, ERP, and plant systems means we can map a schema to a business meaning without a six-week discovery cycle. Ingestion is straightforward. Semantics are not.

Governed from day one

Not a retrofit

Catalog, lineage, access model, and quality rules are designed with the medallion architecture rather than added after an auditor asks. That is what makes an AI answer defensible later.

We stay for the run

Strategy through steady state

24x7 NOC and SOC operations and managed services mean the platform does not degrade after go-live. Pipelines break quietly. Somebody has to be watching.

One partner, strategy through steady state

Advise

Advisory

Build

Implementation

Migrate

Migration

Run

Managed Services

Extend

Custom Development

Optimize

FinOps

How we deliver

From Foundation to Autonomy, on Databricks

The four-pillar model applied to a lakehouse program. Data and Analytics leads here, but it is not the whole engagement, and it is not where the value is realized.

Foundation

Autonomy

Infrastructure

Foundations that hold under production load. Workspace and cloud landing design, network and identity, cost controls, and connectivity back to the estate the data comes from.

Data & Analytics

Data the business can act on and auditors can trace. Medallion architecture, Delta pipelines, catalog and lineage, and migration off legacy ETL that nobody wants to touch.

Agentic AI

Agents that carry real work, governed and measured. Retrieval over governed lakehouse data, vector search, evaluation harnesses, and human escalation paths.

Application Layer

Enterprise applications that act on their own intelligence. Lakehouse outputs pushed back into ERP, MES, and planning systems so the decision arrives where the work happens.

One partner, strategy through steady state

Advise

Advisory

Build

Implementation

Migrate

Migration

Run

Managed Services

Extend

Custom Development

Optimize

FinOps

Autonomy

04 Application Layer

Enterprise applications that act on their own intelligence. Lakehouse outputs pushed back into ERP, MES, and planning systems so the decision arrives where the work happens.

03 Agentic AI

Agents that carry real work, governed and measured. Retrieval over governed lakehouse data, vector search, evaluation harnesses, and human escalation paths.

02 Data & Analytics

Data the business can act on and auditors can trace. Medallion architecture, Delta pipelines, catalog and lineage, and migration off legacy ETL that nobody wants to touch.

01 Infrastructure

Foundations that hold under production load. Workspace and cloud landing design, network and identity, cost controls, and connectivity back to the estate the data comes from.

Foundation

Accelerators Engine

Applied across every pillar, in every engagement.

Capabilities

What we deliver on Databricks

01 Infrastructure

Platform foundations
  • Landing zone, networking, identity & security
  • ERP, MES, and Oracle connectivity
  • Cluster governance, sizing & cost controls
  • Multi-cloud deployment (Azure, AWS, GCP)
  • Sovereign cloud support
  • Workspace, cloud landing, and network design
  • Identity, access model, and security baseline
  • Connectivity to Oracle, ERP, MES, and plant systems
  • Cluster policy, sizing, and cost guardrails
  • Deployment across Azure, AWS, and GCP estates
  • Sovereign and residency-constrained deployments

02 Data & Analytics

Lakehouse architecture
  • Medallion architecture & Delta pipelines
  • ETL and warehouse modernization
  • Data catalog, lineage & quality
  • Semantic models & business metrics
  • Advanced analytics & AI-ready data
  • Medallion architecture design and build
  • Delta pipeline engineering, batch and streaming
  • Migration off legacy ETL and warehouse platforms
  • Catalog, lineage, and data quality frameworks
  • Semantic modeling and business metric definition
  • Advanced analytics and predictive modeling

03 Agentic AI

Intelligent automation
  • AI readiness assessment
  • RAG, vector search & embeddings
  • Agent design, evaluation & guardrails
  • MLOps and model lifecycle
  • Enterprise AI governance
  • AI readiness assessment against the lakehouse estate
  • Retrieval over governed lakehouse data
  • Vector search and embedding pipelines
  • Agent design, evaluation, and guardrails
  • Model lifecycle and MLOps operations
  • AI governance framework implementation

04 Application Layer

Where the work happens
  • ERP, MES & planning system integration
  • Analytics apps & decision support
  • Reporting modernization
  • Embedded AI in workflows
  • Managed platform services
  • Write-back into ERP, MES, and planning systems
  • Analytics applications and decision interfaces
  • Operational reporting consolidation
  • Integration with Oracle and packaged applications
  • Embedded intelligence in existing workflows
  • Managed services for the running platform

The differentiator

From Oracle Estate to Lakehouse

The migration most partners quote around is the one we lead with. Decades of Oracle schema, custom PL/SQL, and undocumented business logic sitting between the enterprise and its lakehouse, with nobody left who wrote it.

We do this work because we built and ran those systems. Source profiling, logic extraction, dependency mapping, and a phased migration that keeps the legacy platform serving the business until the lakehouse can take the load. The result is a Databricks estate the business trusts, because the lineage runs all the way back to the system of record.

Where this lands

  • Lakehouse programs stalled at ingestion because the source semantics are unclear
  • Legacy ETL and warehouse platforms at end of life or end of support
  • Manufacturing and energy estates with plant, ERP, and project data in separate silos
  • Organizations running Databricks in one business unit that need it enterprise wide
  • AI use cases blocked by governance rather than by capability

Entry point: the no-cost Data Estate Diagnostic

Where we go deep

Industry focus

Databricks engagements concentrate in operational, asset-heavy estates where the data is distributed across systems that were never designed to talk to each other.

Manufacturing & Supply Chain

Plant, MES, and ERP data unified for predictive maintenance, quality, and throughput analytics across distributed operations.

Energy & Utilities

Asset performance, field and grid telemetry, and operational intelligence across capital-intensive estates with long-lived infrastructure.

Construction

Project, schedule, and cost data consolidated across Primavera and field systems for portfolio-level visibility.

Banking & Financial Services

Risk and regulatory data products built on lineage that holds up in front of an examiner.

Retail & Consumer Goods

Demand signals, unified commerce data, and supply chain visibility at peak-season scale.

Healthcare & Life Sciences

Clinical and operational data modernization inside regulated environments with evidence requirements.

The delivery record

The lakehouse is new. The estate work underneath it is not

Infolob was founded in 2009 and has spent 17+ years running the systems that lakehouse programs draw from. Databricks became a live channel through the CloudServ merger effective July 1, 2026, which also added sovereign cloud, IBM capability, 24x7 NOC and SOC managed services, and a Riyadh delivery presence.

That combination is deliberate. The constraint on most lakehouse programs is not the platform. It is knowing what the source data means and having someone who will still be there when a pipeline fails at two in the morning.
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Years of enterprise trust

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Enterprise clients served

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Technology professionals

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Technology certifications

Customer successes

Delivered outcomes, not projections

We assert what we have delivered. Every claim below comes from a completed engagement.

Infrastructure
Healthcare

Healthcare firm replatforms EHR and reduces operating cost

EHR migration to Exadata, delivering approximately $10M in annual operating cost reduction.

Read The Story

Infrastructure
Financial services

Global fintech adopts Exadata to support expansion

Platform consolidation supporting rapid transaction growth without proportional infrastructure cost.

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Infrastructure
Retail

Lifestyle retailer brings cloud into on-premise infrastructure

Oracle Cloud at Customer deployment resolving public cloud security constraints without stalling modernization.

Read The Story

See All Success Stories
Start here

Find out whether your data estate can carry the AI use case you have in mind

The Data Estate Diagnostic is a no-cost, structured review of your source systems, pipelines, and governance posture. It produces a source-to-lakehouse map, a gap view against your target use cases, and a sequenced plan your executive team can act on.

Request the Data Estate Diagnostic

What you walk away with

  • Source system inventory and profiling
  • Source-to-lakehouse mapping with semantic gaps identified
  • Governance and lineage readiness assessment
  • Migration sequencing off legacy ETL and warehouse platforms
  • Use case feasibility scored against the current estate
  • Recommended first project scope and kickoff plan

[email protected] · infolob.com

Partner ecosystem

Cloud-agnostic by design. We follow the best technology for your business, not vendor allegiance.

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Databricks

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A cloud-agnostic AI and Data transformation partner. Infrastructure, Data & Analytics, Agentic AI, and Application Layer delivery for enterprises that need AI to reach production.

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