Data architecture
Define the roles of operational stores, warehouses, lakehouses, object storage, event streams, knowledge sources, and APIs.
AI & Data Platforms
Put governed enterprise data to work for AI.
SVCG designs the data services that agents, analytics, and models depend on: governed access, reliable pipelines, retrieval, semantic context, lineage, and measurable data quality.
What makes a data platform AI-ready
Models and agents need dependable access to company data without bypassing ownership, permissions, or quality controls. An AI-ready platform provides that access through stable services for pipelines, metadata, retrieval, semantic definitions, lineage, and monitoring. It should extend useful source systems rather than duplicate them without a reason.
Platform layers
Context that agents can trust.
Pipelines and streaming
Build ingestion and transformation workflows with explicit ownership, replay behavior, failure handling, and recovery procedures.
Retrieval and knowledge
Choose vector, keyword, graph, or hybrid retrieval based on the source material and query. Test chunking, metadata, ranking, and citations against representative questions.
Quality and observability
Monitor freshness, completeness, schema changes, retrieval quality, and business-level expectations. Route failures to the team that owns the data.
Governance and access
Enforce identity, role, policy, tenancy, retention, sensitivity, and audit controls across human and agent access.
AI-ready interfaces
Expose governed services, tools, semantic definitions, and context that agents can use without bypassing enterprise controls.
Production flow
From source system to controlled action.
01
Ingest
Capture structured, unstructured, event, and operational data with lineage.
02
Govern
Apply identity, permission, quality, sensitivity, and retention controls.
03
Retrieve
Provide relevant context through search, APIs, semantic layers, and tools.
04
Act
Let approved agents and applications use context inside bounded workflows.
05
Observe
Trace data, retrieval, decisions, actions, failures, and business outcomes.
Production workloads
Data foundations for agents, analytics, and AI factories.
Enterprise agent context
Connect operational state, company knowledge, permissions, policies, and tools to multi-step agent workflows.
Company Brain context
Unify business semantics, knowledge, operational state, decision memory, permissions, and tools for enterprise agents.
Knowledge and agentic RAG
Let agents choose approved sources, retrieve evidence, evaluate the results, refine the query, and cite what they used.
AI factory data supply
Prepare governed datasets, metadata, checkpoints, artifacts, and movement paths for training and inference infrastructure.
Frequently asked questions
What makes a data platform AI-ready?
A data platform is AI-ready when models and agents can access reliable data through governed interfaces. That requires ownership, lineage, quality checks, permission enforcement, retrieval services, and operational monitoring. A vector database alone is not a data platform.
Does an AI data platform require a new lakehouse?
No. Many organizations can extend the warehouses, lakehouses, operational stores, catalogs, and APIs they already run. A new platform is justified only when the existing systems cannot meet the required access, quality, latency, governance, or scale.
How do data platforms support enterprise AI agents?
The platform gives agents permission-aware access to company data, semantic definitions, retrieval, workflow state, and quality signals. It also provides stable interfaces so agents do not connect directly to every source system.
Start with the decisions the data must support.
Email info@svc.group with the target workload, source systems, data owners, latency requirements, and access constraints. We will help identify the platform work that is actually required.