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LLM & RAG Systems

Your Data. Your Model.
Answers You Can Trust.

We build enterprise-grade RAG systems that connect LLMs to your proprietary data — with semantic search, citation, hallucination mitigation, and real-time retrieval. Production architecture from day one.

LangChain LlamaIndex Pinecone Weaviate pgvector Qdrant OpenAI Llama 3 Claude
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80%
Hallucination reduction vs vanilla LLM
<200ms
Retrieval latency at p95
10M+
Documents indexed in pipelines
15+
RAG systems delivered
Architecture

How a Production RAG System Works

A production RAG system is more than a vector database lookup. It requires careful data ingestion, chunking strategy, embedding model selection, hybrid retrieval, reranking, and LLM prompt engineering — all with observability.

RAG vs Fine-Tuning

Use RAG for dynamic data that changes frequently. Use fine-tuning for fixed reasoning patterns and tone. Combine both for maximum accuracy.

1. Data Sources
PDFs · Confluence · SharePoint · Notion · SQL databases · APIs · Emails
Ingestion Pipeline
2. Chunking & Preprocessing
Semantic chunking · Recursive text splitter · Metadata extraction · Document parsing
Embedding
3. Embedding & Vector Index
text-embedding-3-large · Cohere · BGE-M3 → Pinecone / Qdrant / pgvector
Query time
4. Hybrid Retrieval & Reranking
Semantic + BM25 · Cohere Rerank · Cross-encoder · MMR diversity sampling
Generation
5. LLM + Prompt Engineering
Context window management · Citation injection · Guardrails · Streaming output
Monitoring
6. Observability & Evaluation
LangSmith · Arize Phoenix · RAGAS evals · Cost tracking · Drift detection
Use Cases

LLM & RAG Systems We Build

Enterprise Knowledge Base
Internal chatbot that answers questions from your policies, procedures, HR docs, and technical manuals with source citations and access control.
Intelligent Customer Support
AI support agent trained on your product docs, FAQs, and past tickets. Reduces Tier-1 ticket volume by 40–60% with accurate, cited responses.
Legal & Compliance Assistant
Search across contracts, regulatory documents, and compliance policies. Extract clauses, compare versions, and flag compliance risks with full source traceability.
Medical Knowledge Engine
Clinical decision support over medical literature, treatment protocols, and patient records. HIPAA-compliant, on-premise deployments available.
Codebase Q&A Assistant
Developers query your codebase, architecture docs, and ADRs in natural language. Powered by code-aware embeddings and repository indexing pipelines.
Financial Research Platform
Real-time retrieval over earnings reports, SEC filings, and market data. Analyst-level RAG for investment research with temporal awareness and source ranking.
Vector Database Selection

Choosing the Right Vector Database

Pinecone
Managed

Fully managed, serverless vector DB. Easiest to get started, scales automatically. Best for teams without dedicated infra.

Best for: Fast MVP, no infra overhead
Weaviate / Qdrant
Hybrid Search

Supports both vector and BM25 keyword search in one query. Excellent for mixed retrieval needs and self-hosted deployments.

Best for: Hybrid search, self-hosting
pgvector
PostgreSQL

Vector extension for PostgreSQL. Zero new infrastructure, full SQL JOIN support, transactional consistency. Perfect if you already run Postgres.

Best for: Existing Postgres infra, <10M vectors
Get Started

Book a RAG Architecture Review

Tell us about your data and use case. We'll recommend the right RAG architecture, embedding model, vector database, and LLM for your requirements — in a free 45-minute call.

Architecture Recommendation
Stack, chunking strategy, and embedding model selection
Accuracy Estimate
Benchmark targets for your specific use case and data types
Cost & Timeline
Token cost estimates, infrastructure cost, and delivery schedule
What Happens Next
01
Data Audit — We review your document types, data volume, metadata structure, and retrieval requirements
02
RAG Architecture Plan — Chunking strategy, embedding model, vector database, and LLM selection defined and documented
03
Pipeline Live in 24h — First working RAG pipeline ingesting your data and returning accurate answers within 24 hours
Our Guarantee

Every RAG system ships with a 90-day warranty on retrieval accuracy and pipeline stability. If it breaks due to our code, we fix it at no cost.

Chat with our engineers now
Talk to a RAG Engineer
// free 45-min call · architecture advice
FAQ

Common RAG & LLM System Questions

Everything you need to know. Can't find what you're looking for? Talk to us

RAG is an AI architecture that enhances LLM responses by retrieving relevant context from your data before generating answers. Rather than relying on the model's training data alone, RAG pulls current, domain-specific information — dramatically reducing hallucinations for factual queries.
Use RAG when you need the AI to answer questions from your own documents, especially when data changes frequently. Use fine-tuning when you need the model to adopt a specific tone, skill, or reasoning pattern that doesn't change often. The two approaches can be combined for best results.
Well-implemented RAG reduces hallucinations by 60–80% for domain-specific queries compared to vanilla LLM prompting. Accuracy depends on chunking strategy, embedding model quality, retrieval parameters, and reranking. We run RAGAS evaluations to measure accuracy before every production deployment.
It depends on your scale, infrastructure, and latency needs. Pinecone is managed and great for quick starts. Weaviate and Qdrant support hybrid search (vector + keyword). pgvector integrates directly with PostgreSQL with no new infrastructure. Milvus scales to billions of vectors. We help you choose the right fit.
Yes. We build fully private RAG systems using open-source models (Llama 3, Mistral, Phi-3) running via Ollama or vLLM on your own infrastructure. Nothing leaves your environment — ideal for healthcare, legal, finance, and compliance-sensitive data.
Stop Letting Your Data Go Unused

You have years of institutional knowledge locked in documents, databases, and conversations. Let's build a RAG system that makes it instantly queryable — accurately.

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