From enterprise knowledge bases to real-time customer support, we build RAG systems that deliver accurate, cited answers from your data.
Turn internal documentation, wikis, and SOPs into an intelligent search system that delivers precise answers with source citations.
Augment support agents with real-time retrieval from product docs, past tickets, and knowledge bases for faster, accurate resolutions.
Build RAG pipelines that synthesize information across research papers, reports, and datasets to accelerate discovery and decision-making.
Index codebases, API docs, and developer guides so engineering teams get instant, accurate answers about any part of the stack.
Our proven architecture handles the full retrieval-augmented generation pipeline from data ingestion to cited output.
PDFs, APIs, DBs, wikis
Semantic splitting
Vector encoding
Pinecone, Weaviate
Similarity search
Relevance scoring
Context-aware output
Source attribution
The tools and frameworks we use to build production-ready RAG pipelines.
We analyze your business processes, identify automation opportunities, and design an AI strategy aligned with your goals.
Our engineers design the agent architecture, select optimal models, and plan integrations with your existing systems.
We build your AI agents, train them on your data, and fine-tune performance through iterative testing.
Rigorous testing with real scenarios, edge case handling, and human-in-the-loop validation to ensure reliability.
Production deployment with monitoring, logging, and integration into your existing workflows.
Continuous monitoring, performance optimization, and dedicated support to ensure your AI agents improve over time.
Deep experience with Pinecone, Weaviate, Chroma, Qdrant, and pgvector for optimal storage and retrieval performance.
Combine dense vector search with sparse keyword matching (BM25) for superior retrieval accuracy across all query types.
Role-based access control, data encryption at rest and in transit, audit logging, and compliance-ready architecture.
Ingest from PDFs, databases, APIs, Confluence, Notion, Slack, and 50+ connectors with automatic format handling.
Incremental indexing pipelines that keep your knowledge base current without full re-ingestion or downtime.
Advanced chunking strategies, re-ranking models, and evaluation frameworks that deliver consistently accurate results.
RAG is an AI architecture that combines large language models with real-time retrieval from your proprietary data sources. Instead of relying solely on pre-trained knowledge, RAG systems query your documents, databases, and knowledge bases to generate accurate, grounded responses. Builderz builds custom RAG pipelines that connect LLMs to your enterprise data with 95%+ accuracy.
Builderz RAG systems integrate with virtually any data source: internal documents (PDFs, Word, Google Docs), knowledge bases (Confluence, Notion, SharePoint), databases (PostgreSQL, MongoDB, Snowflake), APIs and SaaS platforms, Slack/Teams conversation history, code repositories, CRM data (Salesforce, HubSpot), and custom data warehouses. We handle all data ingestion, chunking, and embedding.
RAG development costs vary by complexity: Basic RAG pipelines ($15,000-$40,000) for single-source document Q&A, Advanced RAG systems ($40,000-$100,000) with multi-source retrieval, re-ranking, and hybrid search, and Enterprise RAG platforms ($100,000-$300,000) with governance, multi-tenant access, and production monitoring. Book a free consultation for an accurate quote.
Fine-tuning modifies the model's weights with your data, which is expensive and requires retraining when data changes. RAG keeps the model unchanged and retrieves relevant context at query time, making it ideal for frequently updated data. Builderz often recommends RAG as the first step because it's faster to deploy, easier to maintain, and costs 70-80% less than fine-tuning for most use cases.
Builderz works with all major vector databases: Pinecone (managed, scalable), Weaviate (hybrid search), Qdrant (high-performance), Chroma (lightweight), pgvector (PostgreSQL-native), and Milvus (enterprise-grade). We select the optimal vector DB based on your scale, latency requirements, and existing infrastructure.
Builderz RAG systems achieve 90-98% accuracy on enterprise knowledge retrieval tasks, significantly outperforming vanilla LLM responses. We implement advanced techniques like hybrid search (semantic + keyword), re-ranking, query decomposition, and source citation to maximize accuracy. Every response includes source references so users can verify information.
RAG development timelines: basic document Q&A systems take 2-4 weeks, multi-source RAG with advanced retrieval 4-8 weeks, and enterprise RAG platforms 8-12 weeks. Builderz usually ships a validated prototype in 1-2 weeks before full-scale rollout.
Yes. Builderz builds RAG systems with real-time and near-real-time data ingestion pipelines. When documents are updated, new data is automatically chunked, embedded, and indexed within minutes. We support streaming ingestion from databases, webhooks from SaaS platforms, and file system watchers for document repositories.
Join 25+ companies who trust Builderz for RAG development. Get a free consultation and project quote today.
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