Sovereign AI Platforms / Runtime foundation

    GenAI data foundation. On-prem, sovereign, Cloudberry-powered.

    Part of our Sovereign AI Platforms practice. A Kubernetes-native warehouse/lakehouse foundation that makes AI trustworthy — feeding RAG, GraphRAG, and agents with governed, real-time, high-quality data.

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    At a glance
    < 15 min
    Freshness SLA
    > 95%
    Datasets with tests
    100%
    Lineage coverage
    < 50ms
    P95 query latency
    01Architecture

    AI data foundation stack

    A complete data stack feeding the AI Factory with governed, high-quality data.

    Ingest & Change Data Capture

    DebeziumFlink CDCAirflowArgoTektonKafkaRedpandaDB ConnectorsSaaS/ECM

    Storage & Tables

    CloudberryIcebergDeltaHudiMinIOS3-compatible

    Transform & Semantic Modeling

    dbtMetrics StoreHeadless BIFeast

    Discovery, Catalog, Lineage

    DataHubAmundsenColumn-level lineageDataset healthSLAs

    Quality, Testing, Profiling

    Great ExpectationsAnomaly detectionDrift monitorsPII classifiers

    Governance, Privacy & Security

    Row/column securityOPAKyvernomTLSSPIFFEVaultData residency tags

    Observability & FinOps

    PrometheusGrafanaLokiTempoOpenTelemetrySLI/SLOCost meters

    Serve to GenAI

    MilvusQdrantWeaviatepgvectorNeo4jMemgraphvLLMTritonKServe
    02Engine

    Why Cloudberry

    An MPP warehouse that runs where your data already lives.

    On-prem, MPP-class performance

    Scale-out SQL with columnar storage and vector-friendly functions for RAG prep.

    Lakehouse-ready

    Native support for open table formats and object store tiers (hot/warm/cold) with time-travel.

    Mixed workloads

    BI + feature engineering + retrieval prep in one plane; isolation via namespaces/quotas.

    Kubernetes-native operations

    Operators, Helm, GitOps (Argo CD), rolling upgrades, autoscaling.

    Sovereign control

    Run in air-gapped or restricted networks with lineage, audit, and policy enforcement.

    Enterprise scale

    Handles petabyte-scale data with consistent performance and cost predictability.

    03Retrieval

    How it powers RAG and GraphRAG

    Production-grade data pipelines for intelligent retrieval.

    Chunking@ELT

    Deterministic chunkers with semantic boundary hints (headings, tables, code blocks).

    Reranking & Retrieval Policies

    Store candidate sets; enforce per-tenant retrieval policies (privacy, residency).

    Evaluator Harness

    RAGAS / Giskard / DeepEval integrated with datasets managed in Cloudberry tables.

    Feedback Loops

    Capture prompts/answers/ratings as first-class datasets; iterate with canary/A-B routes.

    Graph joins

    Entity/relationship extraction → Neo4j/Memgraph with back-references into Cloudberry tables.

    04Zero trust

    Data security and compliance modes

    Comprehensive audit trails, residency tags, and policy enforcement at the row and column level.

    PII Detection & Remediation

    Hashing, masking, synthetic augmentation options for sensitive data.

    Policy Prompts & Guardrails

    Propagate classification tags to downstream prompts and tool access.

    Zero-trust Patterns

    mTLS everywhere, SPIFFE IDs, per-service JWT, vault-backed secrets.

    Audit Exports

    Immutable logs, SBOM, supply-chain attestations; ready-made checklists for PDPL/DIFC/LFPDPPP/FedRAMP-aware/CJIS.

    05Operations

    Performance, private inference, and DevEx

    Co-located data and GPUs, with the controls to keep them predictable under load.

    DGX/HGX + Cloudberry

    Co-located data and GPUs for feature prep, embedding pipelines, and private inference. Handles small fine-tuned models through open-source 400B+ class with tensor parallelism.

    Throughput controls

    Queueing, quota, and SLO-aware backpressure to model gateways keep performance consistent under load.

    DevEx & Ops

    One-click Environments

    Terraform + Helm values; golden paths for new domains

    dbt + CI

    Tests as gates; schema contracts; automated docs

    Storybook Widgets

    Lineage view, dataset health cards, cost dashboards

    GitOps

    Argo CD; drift detection; policy-guarded rollouts

    06Delivery playbook

    Production-ready in 12 weeks

    Three phases, fixed outcomes, no open-ended discovery.

    1

    Discovery

    Weeks 0-2

    • Data map
    • Threat model
    • Governance baseline
    • Architecture design
    2

    Foundation

    Weeks 3-6

    • Cloudberry clusters + object store
    • Ingest/CDC pipelines
    • dbt transformations
    • Catalog/lineage
    • First RAG feed
    3

    Production

    Weeks 7-12

    • Quality/observability hardening
    • Compliance mode activation
    • Cost meters
    • Productionize retrieval + evaluations
    • Optional GraphRAG & edge
    < 6 weeks
    Time to first use case
    < 100ms
    P95 latency
    > 90%
    Hit@K accuracy
    60%
    Cost reduction
    07Questions

    Frequently asked

    Next step

    Ready to stand up your AI Factory?

    Production AI infrastructure that respects your sovereignty and accelerates your timeline.

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