Information lives across dozens of disconnected systems: ERP, CRM, finance, HR, marketing, IoT, custom apps, legacy platforms, and third-party APIs.
Millions of records arrive hourly: streaming events, documents, images, video, telemetry, AI interactions, and logs.
On-prem databases, data silos, manual reporting, batch integrations, and limited scalability slow every downstream initiative.
Organizations want to deploy AI, but their data is incomplete, duplicated, unstructured, inconsistent, or simply inaccessible.
Healthcare, finance, and government clients need HIPAA, PCI, SOC2, and GDPR-grade lineage, governance, and auditing.
Knowledge indexing, embeddings, vector databases, semantic search, chunking strategies, metadata enrichment.
Conversation history, long-term and short-term memory, session storage, context windows, reasoning history.
Relationships, entities, business hierarchy, documents, business rules, connected intelligence.
PDFs, emails, contracts, medical records, invoices, images, video, voice, chat transcripts.
Similarity search, embeddings, vector databases, hybrid search, metadata filtering, ranking.
Hallucination mitigation, data privacy, prompt security, role-based access, auditability, responsible AI.
LLMs have token limits. Enterprises have millions of documents.
We architect retrieval systems that deliver only the most relevant context to the model, not everything the enterprise knows.
AI systems continuously create chat histories, agent actions, feedback loops, reasoning traces, embeddings, and audit logs.
Without a scalable architecture, storage costs and retrieval latency become unsustainable, so we design for that growth up front.
Enterprise AI must unify information across SharePoint, Confluence, Salesforce, SAP, ServiceNow, M365, Google Workspace, and internal APIs.
One governed access layer across every source system, instead of a one-off integration per tool.
AI systems are only as accurate as the data they can reach; stale snapshots produce confidently wrong answers.
CDC pipelines, scheduled ingestion, and metadata-aware indexing keep models working from current information.
AI should not expose confidential information simply because it exists somewhere in the platform.
Role-based access, document-level permissions, and audit trails enforced on every single retrieval, not just at ingestion.
Repeated embedding creation, vector indexing, inference, and long-term storage add up fast at enterprise scale.
We optimize storage tiers, caching, and retrieval pipelines to control cost without sacrificing performance.
Challenge: The client ran core finance, procurement, and operations on SAP ECC across multiple regional instances. Reporting required manual exports and reconciliation that took days, with no unified view across FI, CO, MM, and SD modules.
Approach: A phased, CDC-based migration into a governed Microsoft Fabric lakehouse, with extraction running alongside live SAP operations, a medallion (bronze/silver/gold) structure inside OneLake, and Power BI + Copilot on top.
ArchitectureMulti-entity reporting reduced from days to near real-time
Single source of truth across FI, CO, MM, and SD
Eliminated manual reconciliation across regional instances
Foundation in place for AI-assisted forecasting
Challenge: The client operated on a decade-old on-premises relational database with nightly batch ETL. Reporting delays were routine and there was no realistic path to cloud-native scaling or AI adoption without replatforming the core system.
Approach: Replatformed onto a cloud-native lakehouse using CDC to migrate incrementally rather than a disruptive cutover, re-architecting batch jobs into event-driven pipelines with a governed access model layered on top.
ArchitectureEliminated nightly batch reporting delays
Reduced infrastructure costs versus on-prem maintenance
Zero-downtime migration path, no disruptive cutover
Governed access layer now ready for AI initiatives
Challenge: The client's connected devices and sensors generated continuous, high-frequency telemetry. A single relational database couldn't serve both real-time operational queries and long-term archival, and dashboards slowed as storage costs climbed.
Approach: A dual-path ingestion architecture: a time-series-optimized hot path for operational dashboards and alerting, and a raw data lake cold path for archival and ML training, converging inside a Databricks lakehouse built on Delta Lake.
ArchitectureReal-time device monitoring at scale
Reduced storage costs via lifecycle tiering
Unified foundation for predictive maintenance ML
Dashboard latency no longer degrades as data volume grows
A single, trusted source of enterprise data
AI-ready data foundations
Faster executive decision-making
Real-time operational visibility
Lower infrastructure and storage costs
Scalable, cloud-native architectures
Reduced reporting latency
Improved regulatory compliance
Better customer intelligence
Accelerated AI and analytics initiatives
Architecture-first, not code-first
Cross-cloud, vendor-agnostic expertise
Security and governance by design
Built for long-term partnership, not handoff
Timelines depend on the current state of your data estate. A data strategy and architecture assessment typically runs 4 to 8 weeks; platform build-out and migration is scoped after that assessment is complete.
Luminogics is vendor-agnostic across AWS, Azure, and Google Cloud, and designs data architecture around the platforms you already run on wherever possible, rather than requiring a migration to a new vendor.
In most cases, yes, through governed retrieval layers, metadata enrichment, and targeted integration rather than a ground-up rebuild of the data platform.
Governance, lineage, and access control are built into the data architecture from day one, not retrofitted after launch, supporting HIPAA, PCI, SOC2, and GDPR requirements for healthcare, finance, and government clients.
A data lake stores raw structured, semi-structured, and unstructured data at scale. A data warehouse stores structured, modeled data optimized for reporting. A lakehouse combines both: the flexibility of a lake with the governance and performance of a warehouse, on platforms like Databricks and Microsoft Fabric.
Yes. Luminogics designs and migrates enterprise data platforms onto Databricks, Snowflake, and Microsoft Fabric, including medallion architecture, Delta Lake, Unity Catalog governance, and integration with existing ERP and CRM systems such as SAP.
Cost depends on the scope of the data estate, number of source systems, compliance requirements, and whether the engagement includes migration, governance, and AI-readiness work. Most engagements start with a fixed-scope data strategy workshop before a platform build is priced.
Yes. Luminogics designs retrieval-augmented generation (RAG) pipelines, vector search, and AI agent memory architecture on top of existing enterprise data, so LLMs and AI agents can retrieve accurate, governed, and current information.
