Liquid Analytics Decisions: A GenAI Analytics Layer for SAP ERP and CRM Data

Liquid Analytics sells a single analytics platform, Decisions, through three configurable applications that read from ERP and CRM systems and layer generative AI on top of that data. The platform reaches enterprise buyers through AWS Marketplace, and its three applications, KPI and Goals, Intelligent Sales, and Financial Strategy, share one engine rather than operating as separate products.

DuckDB and Kubernetes Carry the Query and Scaling Work

The platform runs on DuckDB, an in-process SQL OLAP database with columnar storage and vectorized query execution, which handles analytical queries against large datasets and embeds analytics inside a host application. Kubernetes orchestrates deployment, scaling, and management of the containerized services, and a monitoring component the company calls Workspace Explorer provides full-stack visibility, real-time monitoring, and troubleshooting across issues such as high data volume, fragmented tooling, and role-based access control.

A generative AI layer accepts natural language queries against structured and unstructured data, letting business users retrieve results without writing SQL. The design places computation in an embeddable, cloud-native tier, an approach comparable to embedded analytics patterns that pair a query engine with a managed container runtime. That architecture explains the integration posture: rather than extending the ERP, Decisions ingests from it and returns analysis to the same users who work in sales, finance, and operations.

Three Applications Use One Goal Engine Across Sales and Finance

The three marketplace applications apply the shared engine to distinct operational problems. KPI and Goals distributes targets automatically across organizational structures using variables such as territory, product coverage, and market potential, then updates progress in real time through connections to sales, finance, and ERP tools. Intelligent Sales analyzes buying patterns to anticipate demand, generates AI-drafted proposals, and adjusts pricing and promotions as conditions change, drawing on both structured and unstructured data. Financial Strategy aligns budgets to dynamic goals and tracks execution through a module the company calls Perform, which consolidates data views, automates workflows, and records comments in one place.

A data-versioning capability the company calls Git for Data lets teams work on budgets and financial goals in parallel, then merge their analyses into a single plan. Version control applied to data and decisions gives finance and planning teams a mechanism to track changes, revert, and reconcile concurrent edits, which addresses a common governance gap when planning happens across spreadsheets and email. The recurring premise across all three applications is data unification: each pulls fragmented ERP and CRM records into one view so that goal setting, pricing, and budgeting run against a common dataset.

What This Means for SAPinsiders

• Integration governance sits with the customer. Decisions connects to ERP and CRM systems generically, so SAP teams own the connector design, data refresh cadence, and access controls that determine whether real-time claims hold in an S/4HANA or hybrid landscape.
• Data version control is an audit surface, not only a convenience. Git for Data and change tracking create a record of who altered a budget or target and when, which finance and compliance teams can treat as evidence in planning reviews rather than as a collaboration feature alone.
• SAP specificity is a due-diligence question. The platform is described in generic ERP terms, so buyers should confirm SAP connector support, BTP or RISE compatibility, and data-residency handling before scoping a deployment against SAP systems of record.