Databricks
Operational Intelligence, Engineered on Databricks
Databricks unifies analytics, ML, streaming, and AI on a single Lakehouse platform. But most enterprises stop at dashboards and pipelines without turning the Lakehouse into an AI-native execution fabric that reduces cost-to-serve, accelerates revenue realization, and powers closed-loop operations.
iOPEX goes beyond implementation. We operationalize Databricks by aligning our agentic intelligence for ServiceOps
and RevOps with Databricks telemetry, so signals become actions, and workflows become self-improving operational systems.
15-30%
compute cost reduction
20-40%
pipeline performance improvement
30-50%
faster model-to-production cycles
MTTR ↓ | SLA ↑
pre-built agents
The Shift
From implementation to sustained execution.
Why Lakehouse value stallsCommon realities after go-live
Build-first adoption: Pipelines and models ship but operational outcomes don’t move.
Data and process debt compounds: Optimization, lineage, and governance gaps create rework, slower delivery, and audit risk.
Cost-to-serve sAI remains “insight-only”: Signals don’t translate into next actions; teams stay manual and reactive.
Cost-to-serve scales linearly: Compute inefficiency and human effort rise with volume and complexity.
The iOPEX solution: Lakehouse as an execution fabric
Operate the platform end-to-end: We run Delta, streaming, ML/GenAI, and Unity Catalog with continuous optimization, not periodic cleanups.
Embed operational intelligence: Databricks becomes the sensing layer for Command Agents triggering predictive remediation, revenue leakage detection, and automated orchestration.
Services-as-Software economics: We engineer for measurable outcomes where effort and cost per transaction decline quarter-over-quarter.
Outcomes we enable
Lower cost-to-serve through AI-led automation and workload optimization
Predictive incident detection and proactive remediation using telemetry along with ML signals
Agentic execution: command agents that act (with human-in-the-loop assurance)
Revenue acceleration via usage analytics, renewal intelligence, and leakage prevention
Closed-loop service case orchestration (Sense → Decide → Act → Learn)
Reduced data and process debt via governance, lineage, and operating discipline
How We Deliver
A delivery model built for outcomes
01
Stabilize performance and reliability
Real-time ingestion, medallion architecture, Delta optimization, streaming hardening
Outcome: faster, more reliable pipelines and reduced operational firefighting
02
Make governance audit-ready by design
Unity Catalog, lineage, RBAC/ABAC enforcement, MDM controls
Outcome: reduced rework, better trust in data, faster compliance response
03
Accelerate ML/GenAI to production
MLflow lifecycle, feature store, RAG pipelines, monitored deployments
Outcome: shorter model-to-production cycle and higher adoption with guardrails
04
Activate agentic execution on telemetry
Command Agents consume Databricks signals to trigger next-best-action and orchestration
Outcome: MTTR down, SLA up, fewer manual handoffs, closed-loop operations
05
Drive FinOps and “declining effort” economics
Cluster sizing, workload isolation, auto-termination, scheduling policies
Outcome: compute cost reduction and sustained cost-per-transaction decline
