Data Infrastructure for AI
Build RAG pipelines, vector databases, and embeddings infrastructure. Connect your proprietary data to LLMs securely and efficiently with 10x faster data retrieval.
Your data is your competitive advantage. LLMs cannot access it.
You have years of institutional knowledge locked in documents, databases, and internal systems. ChatGPT and Claude know nothing about it. When employees ask questions about your products, policies, or processes, generic AI fails.
Simply uploading documents to a chatbot does not work at scale. Without proper chunking, embeddings, and retrieval, the AI cannot find relevant information. Responses are incomplete, inaccurate, or miss critical context.
Retrieval-Augmented Generation (RAG) solves this. We build infrastructure that connects LLMs to your data in real-time. The model retrieves relevant context before responding, grounding every answer in your actual information.
What We Build
End-to-end data infrastructure for AI applications. From raw data to production-ready retrieval systems.
RAG Pipeline Development
Build retrieval-augmented generation systems that ground LLM responses in your actual data. Reduce hallucinations and ensure factual accuracy.
- Document ingestion and chunking strategies
- Hybrid search (semantic + keyword)
- Re-ranking for improved relevance
- Source citation and attribution
Vector Database Implementation
Deploy and optimize vector databases for semantic search at scale. We help you choose the right database and configure it for your workload.
- Database selection (Pinecone, Weaviate, Qdrant, pgvector)
- Index optimization for your query patterns
- Scaling and sharding strategies
- Cost optimization for cloud deployments
Embeddings Infrastructure
Generate, store, and serve embeddings efficiently. From text and images to structured data, we build pipelines that keep your vector stores fresh.
- Embedding model selection (OpenAI, Cohere, open-source)
- Batch processing for large corpora
- Incremental updates and versioning
- Multi-modal embeddings (text, images, code)
Data Pipeline Architecture
Connect your existing data sources to AI systems. ETL pipelines that extract, transform, and load data into AI-ready formats.
- Source system integration (databases, APIs, file stores)
- Data cleaning and preprocessing
- Schema normalization
- Real-time vs batch processing strategies
How RAG Works
A typical RAG pipeline has six key stages. We optimize each stage for your specific data and use case.
Document Ingestion
Extract text from PDFs, Word docs, web pages, and databases. Handle tables, images, and complex layouts.
Chunking & Processing
Split documents into semantic chunks. Preserve context and metadata for accurate retrieval.
Embedding Generation
Convert text chunks to vector embeddings using models optimized for your domain.
Vector Storage
Store embeddings in a vector database with appropriate indexing for fast retrieval.
Query Processing
Convert user queries to embeddings and retrieve relevant chunks using semantic similarity.
Response Generation
Pass retrieved context to the LLM with proper prompting. Generate grounded, accurate responses.
Key Infrastructure Decisions
Building AI data infrastructure involves trade-offs. We help you make the right choices for your requirements.
Vector Database Selection
Managed services like Pinecone offer simplicity. Self-hosted options like Qdrant or pgvector offer control and cost savings. We help you evaluate based on scale, budget, and operational requirements.
Embedding Model Choice
OpenAI embeddings are convenient but add per-query costs. Open-source models can run locally with no API costs. Domain-specific fine-tuned models can improve retrieval accuracy by 20%+.
Security & Compliance
Sensitive data requires careful architecture. We can deploy entirely within your VPC, implement row-level security on retrievals, and ensure compliance with GDPR, HIPAA, or industry-specific regulations.
Knowledge Base for a Professional Services Firm
A 150-person consulting firm had 10+ years of project documentation, proposals, and internal memos spread across SharePoint, Confluence, and email archives. Consultants spent hours searching for relevant precedents and examples.
We built a RAG-powered knowledge assistant that:
- Indexed 50,000+ documents across all sources
- Answers questions with citations to source documents
- Respects document permissions from source systems
- Syncs nightly to stay current
Results
Technologies We Work With
We are not tied to any single vendor. We select the right tools based on your scale, budget, and existing infrastructure.
For startups, that might mean Pinecone for simplicity. For enterprises, it might be pgvector in your existing PostgreSQL cluster. We design for your constraints.
Our Data Stack
Ready to connect your data to AI?
Let's discuss your data landscape and design a retrieval architecture that makes your knowledge accessible to LLMs.
Get in touch