MERUVION

Your SAP data, ready for AI.

Meruvion designs, builds and runs data platforms on SAP Datasphere, Snowflake, Databricks, BigQuery, AWS and Azure. On top of them we build AI products made for your schemas, not generic SaaS.

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Platforms we implement SAP Datasphere SAP Analytics Cloud Snowflake Databricks Google BigQuery AWS data lakes Azure Synapse

SAP landscapes are too complex for generic tools

Off-the-shelf AI guesses at what your data means. We harvest the metadata your system already holds, so every answer can be traced back to its source.

The usual approach

Recordings, screen scraping and guesswork

Brittle recordings and screen dependencies break with every upgrade. Maintenance costs climb, data lineage gets lost, and AI models fill the gaps with hallucinations.

The Meruvion approach

Metadata-first, deterministic data fabric

We read metadata directly, normalise data automatically and export models into Datasphere, AWS, Azure Synapse and Snowflake, with no duplication and no invented answers.

452,000ABAP tables
80,000CDS views
7.3 millionfields, most of them undocumented

What we do

From the first architecture workshop to running the platform day to day, one team owns the outcome.

Data platform implementations

Modern cloud data platforms on SAP Datasphere, Snowflake, Databricks, BigQuery, AWS and Azure, designed around how your business reports, plans and forecasts.

See how we deliver them

AI-powered SAP solutions

Custom AI products that run natively on Datasphere and your cloud platform, reading live data without copying it elsewhere.

IT consulting

Enterprise data architecture and SAP strategy, including BOBJ and BW modernisation, platform selection and migration planning.

Cloud solutions

Multi-cloud integrations for AWS, Azure and Snowflake, including feeds into planning tools such as Anaplan.

Managed services

Continuous optimisation and oversight after go-live, so models, loads and alerts keep running as the business changes.

Cloud data platforms that move the business forward

Digitalisation starts with data people can trust. We move SAP and non-SAP data off manual exports, spreadsheets and static reports onto one governed cloud platform, then put it to work in dashboards, planning, alerts and AI.

Decide on today's numbers

Data refreshes continuously from SAP, so leaders stop waiting for month-end reports.

Cut manual effort

Automated loads replace exports, CSV uploads and copy-paste between systems.

One version of the truth

Finance, supply chain and leadership work from the same governed models.

Secure by design

People see only the company codes, regions or cost centres they're authorised for.

Change without rewrites

Business logic stays platform-agnostic with CDS Canvas, so upgrades and migrations are faster.

Ready for AI and growth

A foundation that scales with data volumes and supports forecasting, alerts and assistants.

Proven on our own projects

Our team has designed, built and run these platforms end to end for manufacturers in the automotive and food-ingredients sectors.

Read the success stories
  • Automotive parts manufacturerLegacy SAP BOBJ reporting replaced with Datasphere and SAC, giving leadership live views of production and OEM delivery commitments.
  • Plant-based ingredients manufacturerManual CSV loads into Anaplan replaced by an automated S/4HANA to Datasphere to Azure pipeline.
  • Manufacturing operationsProduction WhatsApp alerts and scheduled Excel and PDF reports running directly on Datasphere data.
  • Hands-on platform deliverySAP Datasphere, SAP Analytics Cloud, Snowflake, Google BigQuery, Databricks, AWS, Azure Synapse and Anaplan integration.

AI products we have built

Each one started with a client problem and now runs in production or pre-release. All of them work directly on your cloud systems.

Meridian

An NLP assistant and metadata catalog engine running on SAP Datasphere. Business users ask questions in plain language and get answers built on catalogued, lineage-checked data.

Meridian console showing a plain-language question, a generated chart and live metadata telemetry

What it does

  • Harvests metadata from every Datasphere tenant space
  • Resolves the CSN query graph and data lineage automatically
  • Describes each model with a grain statement

For business users

  • Plain-language "Ask" assistant
  • Guarded SQL: queries are checked before they run
  • Charts and answers without waiting on IT

Success stories

Two recent SAP Datasphere programmes, delivered end to end.

Automotive parts manufacturer supplying top global OEMs

From legacy BOBJ reporting to an AI-ready platform on Datasphere and SAC

The leadership team was running the business on static WebI reports. We replaced them with SAP Datasphere for enterprise data modelling and SAP Analytics Cloud for executive dashboards, then connected WhatsApp alerts for the numbers that can't wait.

SAP S/4HANASAP DatasphereSAP Analytics CloudAzure Blob StoragePython WhatsApp API

Impact

  • Live dashboards for production schedules, supply chain and OEM delivery commitments
  • New applications on the same data: WhatsApp alerts and formatted Excel mailers
  • Automated, validated loads with no manual intervention
  • Business teams answer their own questions instead of queuing for IT
  • A foundation ready for predictive models and conversational AI
Architecture: SAP S/4HANA to SAP Analytics Cloud through SAP Cloud Connector and SAP Datasphere, with Azure Blob Storage and WhatsApp alerts
End-to-end integration, SAP S/4HANA to SAC via Datasphere
Read the full story

The challenge

  • Reports couldn't track production schedules, bottlenecks or OEM commitments dynamically
  • Static history only, with no AI forecasting for demand and material planning
  • Every WebI report over SAP OData depended on central IT, creating long backlogs
  • The architecture couldn't support conversational AI, predictive models or LLM metadata queries
  • No mobile-responsive reports, drill-throughs or modern executive visuals

What we built

CDC-enabled CDS views, both SAP-delivered and custom, track incremental changes in S/4HANA and replicate into Datasphere through replication flows. Delta and change-log tables capture deletes automatically.

Ingestion layertoQuality layer: facts, dimensions, hierarchiestoConsumption layer: analytic models for SAC
Datasphere layer architecture: ingestion, quality and consumption layers
Datasphere layer architecture

Two-tier security

SAC: objects and foldersTeams such as Finance, SCM and IT see only the dashboards and stories in their folders.
Datasphere: row-level dataData access controls on analytic models filter rows by company code, region or cost centre, using the user's SAML identity over Live Connection.

Manufacturer of plant-based flavours, nutraceuticals, natural colours and fragrances

Automating SAP-to-Anaplan data for demand and forecast planning

Planning data reached Anaplan as CSV files loaded by hand each month. We implemented SAP Datasphere and built an automated pipeline from S/4HANA to Anaplan through Azure Blob Storage.

SAP S/4HANASAP DatasphereAzure Blob StorageAnaplanSAP Analytics Cloud

Impact

  • Manual data loads eliminated; data movement is fully automated
  • Anaplan receives updated SAP data without delay for planning and forecasting
  • Month-end no longer depends on specific individuals
  • Clean, validated data improves planning accuracy and audit trails
  • Scales with growth and higher volumes without extra effort
Architecture: SAP S/4HANA through SAP Cloud Connector and Datasphere to Azure Blob staging and Anaplan
End-to-end integration, SAP S/4HANA to Anaplan via Datasphere
Read the full story

The challenge

  • Manual feeds into Anaplan raised the risk of errors and incomplete data
  • The same data sat in complicated, overlapping model layers
  • The manual process couldn't scale with the business
  • Manual steps weakened traceability for audit and compliance

What we built

CDC-enabled CDS views replicate S/4HANA changes into Datasphere. A dedicated propagation layer stages outbound data and exports it to Azure Blob Storage for downstream integration with Anaplan.

IngestiontoQualitytoReporting (SAC)andPropagation to Azure and Anaplan
Datasphere layer architecture: ingestion, quality, reporting and propagation layers
Datasphere layer architecture with propagation layer

A four-phase delivery framework

Structured so you see working results in weeks, not quarters.

  1. Assess

    Map your landscape, data sources and reporting pain points, and agree the outcomes that matter.

  2. Architect

    Design the platform, layers, security model and integrations in a hands-on workshop.

  3. Build

    Replicate, model and migrate, then add the AI tools and alerts your teams will use daily.

  4. Run

    Hand over with managed services that keep the platform optimised as you grow.

Platform depth

Architecture expertise across SAP and non-SAP ecosystems.

Bespoke engineering

AI tools designed for each client's own schemas.

Our own IP

CDS Canvas and Meridian speed up deployment, migration and documentation.

End-to-end ownership

One team from design to continuous managed services.

See Meridian on your own data

Book a live demo and a custom architecture workshop. We'll look at your landscape and show you where a platform and AI tools would pay off first.

  • A walkthrough of Meridian, CDS Canvas or Relay Console
  • A first view of your target architecture
  • Honest advice on which platform fits

Send us your details and our team will follow up.