• The learning data quality layer for enterprise systems

    The learning data quality layer for enterprise systems

    SherpAI develops an AI-based software layer that integrates as an add-on into ERP, CRM and comparable systems. The platform detects faulty data structures, corrects them contextually and enriches missing information.
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  • From a working prototype to a scalable product

    From a working prototype to a scalable product

    Previous work and an existing prototype validate both demand and technical feasibility. With the EXIST start-up grant, SherpAI is being advanced from a domain-specific demonstrator into a generalisable enterprise product.
    About the Project
  • White-label add-on for ERP vendors

    White-label add-on for ERP vendors

    Market entry takes place through ERP vendors as integration and sales partners. SherpAI combines customer-specific fine-tuning, process integration and a scalable partner sales model based on revenue share.
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    Visuelle Darstellung von Datenqualitätsproblemen in ERP-nahen Stammdaten

    Starting Point

    Poor master data quality leads to wrong decisions, process costs and high maintenance effort.

    Enterprise software makes operational decisions every day based on master data. In real ERP systems, however, this data emerges over years through manual inputs, migrations and workarounds.

    The result is inconsistent, duplicate or incomplete records. Manual maintenance and purely rule-based checks are maintenance-heavy, not scalable and often fix only simple errors. This is exactly where SherpAI comes in as a learning data quality layer.

    Learn more about the project

    Letters of Intent

    validated data domains

    team members

    months in the EXIST phase

    Innovation

    Orchestrated AI modules instead of purely rule-based data maintenance

    SherpAI combines several AI paradigms in a modular, orchestrable architecture: structure identification, semantic interpretation, deterministic correction and self-adaptive learning.

    The foundation consists of containerised AI modules, AI-CPS building blocks and customer-specific fine-tuning per installation. This creates not a rigid rule engine, but a learning infrastructure component that can adapt to different data contexts and organisation-specific error patterns.

    Technology & Team
    Plattformansicht von SherpAI mit Ergebnis- und Statuslogik

    Value Proposition

    Data Analysis, Data Cleaning and Data Enrichment in one solution

    Data Analysis

    Faulty data structures, duplicates, inconsistencies and incomplete information are detected automatically and interpreted in their specific data context.

    Data Cleaning

    Simple and complex data errors are corrected contextually. Deterministic system logic ensures reproducible changes in the source systems.

    Data Enrichment

    Missing information can be enriched and organisation-specific error profiles can evolve continuously. Each installation improves the system's capabilities.

    Current Stage

    From pre-seed through EXIST to partner sales

    SherpAI is being developed out of the University of Potsdam as a white-label-ready infrastructure component for data quality.

    Pre-seed / Prototype

    01
    Current state
    A working prototype has been demonstrated on real enterprise data, but it is still strongly domain-specific and not yet transferable without manual adaptation.
    • deduplication and error detection
    • model-based data correction
    • demonstration with Gebauer GmbH
    • not yet generalisable without adaptation
    About

    EXIST funding phase

    02
    EXIST
    12 months
    The funding phase closes the gap between demonstrator and scalable product, with a focus on generalisation, learning and safe integration.
    • systematisation via AI-CPS
    • pre-release integration into TimelineERP
    • beta release after feedback loop
    • pilot integration with additional users
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    Market entry & partners

    03
    Go-to-market
    Rollout takes place through ERP vendors as integration and sales partners in a white-label and revenue-share model.
    • focus on SME ERP vendors
    • 3 Letters of Intent
    • planned ERP integration
    • later expansion into further software systems
    Contact

    Think of SherpAI as standard infrastructure for data quality.

    We speak with ERP vendors, pilot partners and organisations that want to build data quality as a strategic infrastructure capability.

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    SherpAI

    SherpAI is an EXIST startup project from Potsdam focused on learning data quality infrastructure for ERP-related enterprise systems.

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