Adopting a new platform without a plan simply moves technical debt to a newer address. Real modernization means rethinking how data is ingested, replicated, harmonized, governed and consumed, and building for the AI workloads coming next, not only the reports running today.
LTM was a launch partner for the Oracle AI Data Platform and has been designing on it since the platform became available. We build an open medallion lakehouse on Apache Iceberg, real-time and batch ingestion across Oracle and non-Oracle sources, a unified catalog with end-to-end lineage, and security applied at object and row level from the first load. Accelerators convert what you already run including structures, pipelines, and policies, rather than rebuilding it by hand.
A four-week read of what you actually run: source inventory, pipeline and report profiling, data quality baseline, workload and cost analysis. You receive a target lakehouse architecture, a migration wave plan sequenced by business risk, and a TCO comparison against staying put.
Bronze, silver and gold layers on Autonomous AI Lakehouse and OCI Object Storage, in open Apache Iceberg format. Unified catalog, AI vector search for unstructured content, and semantic models that reflect how your business defines a customer, an order and a cost centre.
Batch and streaming ingestion from Fusion, EBS, JDE, SAP and third-party systems. ODI pipelines auto-generated from parameters, GoldenGate change data capture for homogeneous and heterogeneous replication, and PVO extracts replicated to ADW without hand-built jobs.
Databricks, Snowflake, Hadoop, OBIEE, OBIA or on-prem warehouses converted to AIDP-native services with TransisTOR — structures, pipelines, lineage and policies moved and reconciled automatically. Legacy data retained cost-effectively on OCI for regulatory access, then the old stack retired.
Cataloging, lineage, quality rules and residency controls applied as the platform is built, not bolted on after. Object and row-level security provisioned in bulk and kept synchronised with IDCS/IAM, so access policy survives every future change.
Created natively on the Oracle AI Data Platform as a launch partner, not retro-fitted to it. Discovers, converts and validates data structures, pipelines, lineage and governance into AIDP-native services — then amplifies value with prebuilt KPI packs and a network of AI agents.
Fast data transfer from any Oracle or non-Oracle relational source into a medallion architecture on OCI. Also keeps legacy data on OCI at low cost for regulatory retention, with a simple query interface for auditing and everyday use.
Creates ODI data pipelines automatically from input parameters, without manual intervention. It accelerates straightforward pipeline creation — staging mappings and similar patterns — and frees engineers for work that genuinely needs judgement.
As the analytics landscape grows, more pipelines pull from more sources. ODI Lineage makes end-to-end data flow visible, and turns impact assessment for any change from a week of archaeology into a query.
Bulk transition of data and object security to OAC and FDI from other systems. Create roles and groups from a single form, apply data and object-level security with ease, and grant privileges to many users in one click.
Plug-and-play enterprise data models for EBS and Fusion Finance, HCM and SCM, with ready-to-use raw-to-staging-to-curated mappings and preconfigured load plans: a modelled, working warehouse without months of hand-built groundwork.
Real-time insight and load monitoring for ODI. Failures surface early, batch windows stay predictable, and support teams spend their time on resolution instead of discovery.
| Layer | What sits here | LTM asset applied |
| Sources | Fusion, EBS, JDE, SAP, files, third-party APIs, unstructured content | SwiftStream, Auto PVO Replicator
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| Ingest & Replicate | Batch ELT, GoldenGate CDC, streaming | ODI AutoMap, SwiftStream |
| Lakehouse (Bronze/Silver/Gold) | Autonomous AI Lakehouse, Object Storage, Apache Iceberg tables | TransisTOR, MetaModel, ModelBuilder |
| Govern | Catalog, lineage, quality rules, residency, retention | ODI Lineage, Load Monitoring |
| Secure | Object and row-level policy, IDCS/IAM sync | iSee |
| Consume | OAC, FDI, ML models, AI agents, natural-language query | TransisTOR Amplify, iNavigator |
One of the first Oracle AI Data Platform deployments in production anywhere — legacy data estate converted to AIDP-native services with TransisTOR, then activated with AI agents.
Financial and inventory analytics built on OAC, ADW, ODI and object storage over JDE — with platform modernisation to cloud infrastructure and NextGen AMS.
Data lake implemented across heterogeneous Oracle and non-Oracle sources, replacing fragmented reporting with one governed foundation and a modern BI layer.
Power BI and SQL Server reporting transitioned to Fusion Data Intelligence, then extended for cross-functional reporting and automated regulatory submissions.
OCI Analytics implementation eliminated manual processes and gave the business direct access to supply order data. Powered by Oracle Data Analytics
Warehouse efficiency improved by embracing Oracle Analytics Cloud, with automated dataflow replacing manual reconciliation. Good improvement by Oracle Analytics Services
Fixed scope, fixed price, zero disruption to running operations. We profile your sources, pipelines and reports, baseline data quality and cost, and hand back a target lakehouse architecture with a migration wave plan and TCO comparison attached.
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