Data Pipelines & RAG

Your AI is only as good as the data behind it. At Spundan, we engineer robust data pipelines and Retrieval-Augmented Generation systems that connect your knowledge to your AI — delivering accurate, grounded, and real-time responses at enterprise scale.

From raw data ingestion to intelligent retrieval, we build the infrastructure that makes your AI systems trustworthy, up-to-date, and deeply connected to your business context — eliminating hallucinations and unlocking the full value of your data assets.

Our Data Pipelines & RAG Offerings

End-to-End Data Pipeline Engineering

Design and build scalable ETL/ELT pipelines that ingest, transform, and deliver data from any source — databases, APIs, files, or streams — to your AI systems reliably and in real time.

RAG System Design & Development

Build production-grade Retrieval-Augmented Generation systems that ground LLM responses in your own documents, databases, and knowledge bases — eliminating hallucinations and boosting accuracy.

Vector Database Integration

Implement and manage vector databases — Pinecone, Weaviate, Chroma, Qdrant, or pgvector — optimized for semantic search and fast similarity retrieval at any scale.

Document Ingestion & Chunking

Intelligent document processing pipelines that parse, chunk, and embed PDFs, Word docs, web pages, and structured data — making your entire knowledge base searchable by AI.

Real-Time Streaming Pipelines

Build event-driven, real-time data pipelines using Kafka, Flink, or Spark Streaming — ensuring your AI systems always have access to the freshest data without batch processing delays.

Hybrid Search & Re-Ranking

Combine dense vector search with keyword-based BM25 retrieval and intelligent re-ranking models — delivering the most relevant context to your LLM for every query.

Our Data Pipelines & RAG Implementation Process

01
Data Discovery & Source Mapping

We audit your existing data sources — databases, file stores, APIs, and third-party platforms — mapping data flows and identifying the highest-value sources for your AI use case.

02
Pipeline Architecture Design

We design a scalable, fault-tolerant pipeline architecture — choosing the right ingestion, transformation, and orchestration tools based on your data volume, velocity, and variety.

03
Data Ingestion & Transformation

We build connectors and ETL/ELT jobs that extract, clean, normalize, and enrich your data — ensuring it arrives at your AI systems in the right format, at the right time.

04
Embedding & Vector Store Setup

We select the best embedding model for your content, chunk documents intelligently, generate embeddings, and populate your vector database — building the retrieval backbone of your RAG system.

05
RAG Pipeline Development

We build the full RAG pipeline — query understanding, hybrid retrieval, context assembly, and LLM prompting — tuned for accuracy, relevance, and latency in your specific domain.

06
Evaluation & Quality Assurance

We evaluate retrieval quality using RAGAS and custom benchmarks — measuring faithfulness, answer relevancy, and context precision to ensure your RAG system meets production standards.

07
Deployment, Monitoring & Maintenance

We deploy your pipelines and RAG system to production with full observability — monitoring data freshness, retrieval quality, and pipeline health with automated alerts and re-indexing schedules.

Why Choose Spundan for Data Pipelines & RAG?

AI-First Data Engineering

We don't just build pipelines — we build pipelines designed specifically to feed AI systems. Every decision is made with model performance and retrieval quality in mind.

Hallucination Elimination

Our RAG systems ground every AI response in your verified data — dramatically reducing hallucinations and ensuring your AI always cites real, relevant sources.

Any Stack, Any Source

We integrate with any data source and tool — from legacy databases and SharePoint to modern data warehouses like Snowflake and BigQuery — regardless of complexity.

Production-Grade Reliability

Our pipelines are built for production — with error handling, retry logic, monitoring, and alerting that ensure your data flows never silently fail when your AI needs them most.

Scalable from Day One

Whether you're indexing thousands or billions of documents, our architectures scale horizontally — ensuring retrieval stays fast and accurate as your data grows.

Measurable Retrieval Quality

We benchmark and continuously evaluate retrieval performance using proven frameworks — so you always know exactly how well your RAG system is performing in production.

Frequently Asked Questions

RAG (Retrieval-Augmented Generation) enhances LLM responses by first retrieving relevant context from your own data sources before generating an answer. This grounds the AI in real, verified information — dramatically reducing hallucinations and making responses accurate, current, and specific to your business.

We can connect virtually any data source — PDFs, Word documents, SharePoint, Confluence, Notion, SQL databases, APIs, web pages, emails, and more. Our ingestion pipelines handle structured, semi-structured, and unstructured data across any format or storage system.

We build automated re-ingestion pipelines that detect changes in your source data and update the vector index incrementally — ensuring your RAG system always reflects the latest information without manual intervention or full re-indexing.

Ready to Connect Your Data to AI? Let's Build Your Pipeline.

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