AI Data Infrastructure: Building the Foundation

AI is only as good as the data it can access. Here’s how to build infrastructure that enables AI success.

The Infrastructure Stack

┌─────────────────────────────────────┐
│         AI Applications             │
├─────────────────────────────────────┤
│      Orchestration Layer            │
├──────────┬──────────┬───────────────┤
│ Vector   │ Knowledge│ Feature       │
│ Store    │ Graph    │ Store         │
├──────────┴──────────┴───────────────┤
│        Data Processing              │
├─────────────────────────────────────┤
│        Data Sources                 │
└─────────────────────────────────────┘

Core Components

1. Data Ingestion

Sources to connect:

Databases (SQL, NoSQL)
APIs and SaaS platforms
Documents and files
Real-time streams
External data providers

Key considerations:

Incremental updates
Change data capture
Schema evolution
Error handling

2. Data Processing

Essential capabilities:

Capability	Purpose
ETL/ELT	Transform and load data
Cleaning	Ensure quality
Enrichment	Add metadata, context
Chunking	Prepare for embeddings

3. Vector Storage

For semantic search and RAG:

Embed documents and data
Enable similarity search
Support metadata filtering
Scale with data volume

Popular options:

Pinecone (managed)
Weaviate (hybrid search)
Qdrant (performance)
Chroma (local dev)
pgvector (PostgreSQL)

4. Knowledge Graphs

For relationship-rich data:

Entity relationships
Hierarchies
Ontologies
Temporal data

5. Feature Stores

For ML applications:

Feature computation
Feature serving
Version control
Monitoring

Architecture Patterns

Pattern 1: RAG-First

Best for document-heavy use cases.

Documents → Chunking → Embeddings → Vector Store
                                        ↓
User Query → Embedding → Search → Context → LLM → Response

Pattern 2: Hybrid Search

Best for structured + unstructured data.

Structured Data → Relational DB
                        ↓
Unstructured → Vector Store → Combined Search → LLM
                        ↑
User Query →────────────┘

Best for diverse data types.

Text → Text Embeddings ──┐
Images → Image Embeddings ├→ Multi-Modal Store → AI Apps
Audio → Audio Embeddings ─┘

Data Quality for AI

Critical Factors

Accuracy: Correct information
Completeness: No missing critical data
Freshness: Up-to-date content
Consistency: Uniform formats
Relevance: Aligned with use cases

Quality Pipeline

Raw Data → Validation → Cleaning → Enrichment → Quality Store
              ↓            ↓           ↓
          Log Issues  Fix Problems  Add Metadata

Embedding Strategies

Choosing Embedding Models

Factor	Consideration
Dimension	Higher = more nuance, more cost
Speed	Balance accuracy vs. latency
Domain	General vs. specialized
Cost	Per-token pricing

Chunk Size Guidelines

Small (256 tokens): Precise retrieval, more noise
Medium (512 tokens): Balanced approach
Large (1024+ tokens): More context, less precise

Metadata Strategy

Include with embeddings:

Source document
Creation date
Author/owner
Category/type
Access permissions

Scaling Considerations

Data Volume

Scale	Approach
< 100K docs	Single vector store
100K - 1M	Sharded vector store
> 1M	Distributed architecture

Query Volume

Caching for frequent queries
Read replicas for scale
Async processing for batch
Rate limiting for protection

Cost Management

Tiered storage (hot/cold)
Compression where possible
Embedding model optimization
Query result caching

Security Architecture

Access Control

Document-level permissions
User/role-based access
Query result filtering
Audit logging

Data Protection

Encryption at rest
Encryption in transit
Key management
Backup/recovery

Monitoring and Observability

Key Metrics

Ingestion success rate
Data freshness
Query latency
Retrieval quality
Storage utilization

Alerting

Pipeline failures
Quality degradation
Performance issues
Security events

Implementation Roadmap

Phase 1: Foundation

Connect primary data sources
Set up basic vector store
Implement simple RAG
Establish monitoring

Phase 2: Enhancement

Add more data sources
Improve chunking/embedding
Implement hybrid search
Add metadata enrichment

Phase 3: Scale

Distributed architecture
Advanced caching
Performance optimization
Multi-tenant support

Need help building your AI data infrastructure? Let’s architect your solution.