Pipelines built for reports move data once a night. AI agents need data that is fresh, tested and explained. Data Semantics connects ERP, CRM, core banking and industry systems in weeks, not quarters, with AI writing the code and our engineers owning production.
Your sources
SAP & OracleDynamics 365SalesforceCore bankingYardi & OPERAFiles & APIsAI-assisted build
Engineers review every change
Your platform
Microsoft FabricDatabricksSnowflakeData Semantics builds and runs data pipelines on Microsoft Fabric, Databricks and Snowflake, connecting SAP, Oracle, Dynamics 365, Salesforce, core banking and industry systems. AI generates the ingestion and transformation code, tests and documentation. Our engineers own the architecture, review every change and run it in production.
Most pipelines were designed to land data overnight for dashboards people check in the morning. When an AI agent acts on that data, a late, broken or unexplained load is no longer an annoyance; it is a wrong action.
Designed once, generated by agents, reviewed by engineers and tested on every run.
Data brought in from ERP, CRM, core banking, APIs, files and industry systems.
Raw, cleaned and business-ready layers, so quality and lineage are visible at every step.
Data in minutes, not days, for operations and AI agents that need it now.
Bad data stopped before it reaches a report or an agent.
Pipelines managed like software: versioned, tested and released safely.
Pipelines that run fast and don’t waste compute.
AI drafts the boilerplate that slows data engineering down. Our engineers design the architecture, review every change and own what goes to production. Mapping-driven code generation, medallion modeling and data quality checks run in DataPrep 360 today.
AI profiles source tables and APIs to find keys, volumes and quality issues before any build starts.
You get: a source profile and build plan
Checked by: the engineer who owns the source
Ingestion and transformation code (PySpark, Spark SQL, dbt or Fabric pipelines) is generated from approved mappings.
You get: pipelines in your repository
Checked by: engineers review every line
Runs in DataPrep 360Bronze, silver and gold layers and dimensional models are proposed, with documentation.
You get: a documented model
Checked by: architects approve the model
Runs in DataPrep 360Unit tests and data quality rules are generated for every pipeline and run on every load.
You get: tested pipelines
Checked by: engineers approve the rules
Runs in DataPrep 360AI reads failed loads and proposes a fix.
You get: faster recovery
Checked by: an engineer approves every fix
AI keeps documentation and lineage current as pipelines change.
You get: up-to-date lineage
Checked by: engineers review with each release
Tell us which systems you need connected and where the data should land. We’ll show you how we would build it.
Most pipelines move columns from A to B. Ours apply your ontology as they go: business keys, definitions and relationships are built into the silver and gold layers, so the data that lands is already understood by reports and AI agents.
The same customer from CRM, ERP and the bank feed lands as one entity, matched on agreed keys.
Rules such as "net revenue" or "active customer" are applied once in the pipeline, not rebuilt in every report.
Every column traces back to its source and forward to the business term it feeds.
Gold-layer data arrives with the context agents need to act on it safely.
Prove the approach on a real source, then add the rest on the same patterns.
We connect your first priority sources end to end, with tests, lineage and documentation.
A blended team of our engineers and agents that adds sources and subject areas every month.
Pipelines from more than 18 source systems brought onto one governed platform, feeding 600+ reports with system performance improved by more than half.
Email and attachment data moved out of expensive CRM storage into a governed, lower-cost Azure Data Lake, with the links back to Salesforce kept.
About data engineering
Data engineering builds and runs the pipelines that bring new sources into a data platform and keep them reliable. Data platform modernization moves an existing legacy warehouse, its pipelines and reports onto Microsoft Fabric, Databricks or Snowflake. Data Semantics often does both: modernize the platform once, then keep connecting new sources to it.
AI profiles sources, generates ingestion and transformation code, proposes data models, writes tests and documentation, and fixes failed loads. Engineers design the architecture and review every change, so delivery is faster without losing control.
A pipeline is ready for AI agents when its data is as fresh as the decisions need, tested on every load, documented with lineage and business definitions, and monitored so failures are caught before an agent acts on bad data. Data Semantics builds pipelines to that standard on Fabric, Databricks and Snowflake.
Sources and platforms
Microsoft Fabric and Azure, where we are a Microsoft Solutions Partner, plus Databricks and Snowflake, where we have strong delivery competency.
Yes. Data Semantics connects ERP and CRM sources such as SAP, Oracle Fusion and EBS, Dynamics 365 and AX, NetSuite, JD Edwards and Salesforce; core banking platforms such as Temenos T24 and Temenos Transact, Path Solutions iMAL, Infosys Finacle and Oracle FLEXCUBE Universal Banking; and industry systems such as Yardi, OPERA, LS Retail and Oracle Retail, using our library of source mappings.
Yes, where the use case needs it, such as operational alerts or AI agents acting on fresh data. Most reporting needs are met with frequent incremental loads, which cost less.
Quality and operations
Every pipeline has automated tests and quality checks, runs through CI/CD, and is monitored in production. Failures are diagnosed and fixed quickly, often automatically.
Lineage is captured as pipelines are built, in Microsoft Purview, Databricks Unity Catalog or Snowflake Horizon, so you can trace any number in a report back to its source system, transformation and owner.
Both. Code is versioned in your repositories, follows shared patterns and is documented with lineage, so your team can run it. Or Data Semantics can run and improve your pipelines as a managed service, with monitoring, failure handling and new sources added on request, through Managed Data & BI Services.
Time
A typical source, profiled, ingested, modeled and tested, takes weeks rather than months, and faster where it is already in our mapping library.
Name it. We’ll profile it, connect it and show you tested, documented data on your platform, fast.
