Production-ready RAG and GraphRAG retrieval systems that power intelligent applications. Expert implementation of semantic search and knowledge graphs.
Production-ready solutions with proven results
Get from concept to production-ready RAG system in 2-4 weeks with proven architectures.
Achieve 90%+ accuracy with proper chunking, embedding selection, and retrieval tuning.
Seamlessly integrate with your existing databases, APIs, and knowledge bases.
Built to handle millions of documents with sub-second retrieval times.
Your data stays private. On-premise deployment options available.
Ongoing support for chunking strategies, embedding models, and retrieval optimization.
From concept to production in 8-12 weeks
We analyze your documents, knowledge base, and use cases to design the optimal RAG architecture. Choose embeddings, chunking strategy, and vector database.
Build a proof-of-concept with a subset of your data. Test retrieval accuracy and answer quality. Iterate on chunking and retrieval parameters.
Scale to full dataset with production-grade vector database, monitoring, and evaluation pipelines. Optimize for cost and performance.
Deploy to your infrastructure with comprehensive documentation, monitoring dashboards, and team training on maintaining the system.
Choose the right approach for your specific needs
| Feature | RAG & GraphRAG This Page | LLM Fine-tuning | AI Agents |
|---|---|---|---|
| Best For | Dynamic knowledge, Q&A | Domain-specific tasks | Complex workflows |
| Setup Time | 2-4 weeks | 4-8 weeks | 3-6 weeks |
| Cost | $$ | $$$ | $$ |
| Accuracy | High with good data | Very high | Variable |
| Maintenance | Low | Medium | High |
| Use When | Need latest information | Need consistent behavior | Need autonomy |
See how we've helped businesses achieve their goals
Needed an AI assistant for patient support that was HIPAA-compliant, accurate, and could access 50,000+ medical documents.
Learn how RAG and GraphRAG work, when to use each approach, and how to implement production-ready retrieval systems for your AI applications.
Retrieval-Augmented Generation (RAG) has become the gold standard for giving large language models access to specific, up-to-date information. Unlike fine-tuning, which bakes knowledge into the model’s weights, RAG retrieves relevant information on-demand and includes it in the prompt context.
Think of it this way:
Fine-tuning teaches the model new skills and behaviors RAG gives the model access to a dynamic knowledge base
This makes RAG ideal for:
A production RAG system typically consists of 5 key components:
The first step is ingesting and preparing your documents:
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Split documents into chunks
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", " ", ""]
)
chunks = splitter.split_documents(documents)Key considerations:
Each chunk gets converted to a vector representation:
from openai import OpenAI
client = OpenAI()
def get_embeddings(texts):
response = client.embeddings.create(
model="text-embedding-3-small",
input=texts
)
return [item.embedding for item in response.data]Popular embedding models:
text-embedding-3-small (1536 dimensions, cost-effective)text-embedding-3-large (3072 dimensions, higher accuracy)Cohere embed-v3 (excellent multilingual support)Embeddings are stored in a vector database for fast similarity search:
| Vector DB | Best For | Strengths |
|---|---|---|
| Pinecone | Production scale | Managed, high performance |
| Weaviate | Hybrid search | Native filtering, GraphQL |
| Qdrant | Self-hosted | Fast, easy to deploy |
| pgvector | Existing PostgreSQL | No new infrastructure |
When a user asks a question, we:
# Simple similarity search
results = vector_store.similarity_search(
query="How do I reset my password?",
k=5
)
# Advanced: Hybrid search (vector + keyword)
results = vector_store.search(
query="password reset",
search_type="hybrid",
k=5,
alpha=0.5 # Balance between vector and keyword
)Finally, retrieved chunks are added to the LLM prompt:
prompt = f"""Answer the question based on the following context:
Context:
{retrieved_chunks}
Question: {user_question}
Answer:"""
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)Traditional RAG works great for direct fact retrieval, but struggles with:
GraphRAG enhances traditional RAG by building a knowledge graph from your documents, capturing:
This enables:
# Traditional RAG: Returns chunks mentioning "CEO"
results = rag.search("Who is the CEO?")
# GraphRAG: Traverses relationships
results = graph_rag.search("Who reports to the CEO's direct reports?")
# Returns: CEO → Directors → Managers (multi-hop traversal)GraphRAG adds two key components:
1. Knowledge Graph Construction
from langchain.graphs import Neo4jGraph
# Extract entities and relationships
extractor = EntityRelationshipExtractor(llm=llm)
graph_data = extractor.process_documents(documents)
# Store in graph database
graph = Neo4jGraph(url="bolt://localhost:7687")
graph.add_graph_documents(graph_data)2. Community Detection
GraphRAG identifies communities (clusters) in your knowledge graph:
“By identifying communities of related entities, we can answer questions about themes and patterns that span multiple documents.”
This enables global queries like:
Here’s when to use each approach:
Examples: Product docs, FAQ systems, simple Q&A
Examples: Research papers, organizational knowledge, legal documents
Ready to build a RAG system? Here’s our battle-tested checklist:
Document ingestion pipeline
Chunking strategy
Embedding generation
text-embedding-3-small)Vector database setup
Retrieval evaluation
k (number of results) parameterQuery enhancement
Re-ranking
Prompt engineering
Response generation
Observability
Problem: Chunks split in the middle of important information
Solution: Use semantic chunking based on document structure:
# Bad: Fixed-size chunking
chunks = split_every_1000_chars(document)
# Good: Semantic chunking
chunks = split_by_sections_and_paragraphs(document)Problem: Retrieved chunks don’t actually answer the question
Solutions:
Problem: Model still makes up information even with retrieved context
Solutions:
# Add explicit instruction
prompt = f"""CRITICAL: Only use information from the context below.
If the answer is not in the context, say "I don't have enough information."
Context:
{retrieved_chunks}
Question: {question}"""
# Verify citations
response = llm.generate(prompt)
verify_citations_exist(response, retrieved_chunks)Problem: Performance degrades as document count grows
Solutions:
Once you have a basic RAG system working, consider these enhancements:
Add a summary or context to each chunk:
for chunk in chunks:
context = f"""Document: {chunk.document_title}
Section: {chunk.section}
Previous content: {previous_chunk_summary}"""
chunk.embedding = embed(f"{context}\n\n{chunk.content}")This helps the model understand chunk context even when retrieved in isolation.
Route different query types to specialized retrievers:
def route_query(query):
if is_factual(query):
return vector_search(query)
elif requires_computation(query):
return sql_search(query)
elif needs_multi_hop(query):
return graph_search(query)Retrieve, analyze, then retrieve again if needed:
# First retrieval
initial_results = retrieve(query)
# Check if more info needed
if needs_more_context(initial_results, query):
followup_query = generate_followup_query(initial_results, query)
additional_results = retrieve(followup_query)
results = merge(initial_results, additional_results)RAG systems can get expensive at scale. Here’s how to optimize:
text-embedding-3-small is 4x cheaper than largeCost comparison (1M tokens):
text-embedding-3-small: $0.02text-embedding-3-large: $0.13Cohere embed-v3: $0.10Client: B2B SaaS company with 10,000+ help articles Challenge: Support team spent 60% of time finding information Solution: RAG-powered internal knowledge assistant
Results:
Key success factors:
Ready to implement RAG or GraphRAG for your organization?
We offer three engagement models:
All engagements include:
Want to dive deeper? Check out these resources:
Have questions about implementing RAG for your use case? Book a consultation and we’ll help you design the right solution.
Learn more from our expert insights and implementation guides.
Learn how to implement Retrieval Augmented Generation (RAG) systems that power intelligent applications with your own data.
RAG is a technique that combines information retrieval with LLM generation. Instead of relying solely on the LLM's training data, RAG retrieves relevant information from your knowledge base and provides it as context to the LLM. This results in more accurate, up-to-date, and factual responses.
Use RAG when you need to answer questions from a large, dynamic knowledge base (documents, FAQs, wikis). Use fine-tuning when you need to change the LLM's behavior, tone, or teach it specialized domain knowledge. RAG is faster to implement and easier to update.
You need structured or unstructured documents: PDFs, Word docs, markdown files, databases, APIs, or any text-based knowledge. The quality of your data directly impacts the quality of answers. We help you prepare and clean your data for optimal results.
A typical POC takes 1-2 weeks. Production implementation takes 2-4 weeks depending on data volume and complexity. Total timeline is usually 4-8 weeks from kickoff to production deployment.
It depends on your scale and requirements. For small/medium datasets (<1M docs), Pinecone or Weaviate work well. For large scale, consider Qdrant or Milvus. We help you choose based on your specific needs, budget, and technical constraints.
GraphRAG combines knowledge graphs with traditional RAG. Use it when your data has complex relationships (entities, hierarchies, connections) that need to be preserved. Great for research papers, legal documents, and interconnected knowledge bases.
We implement evaluation pipelines that measure retrieval precision, answer accuracy, and relevance. Common metrics include NDCG for retrieval and human evaluation for answer quality. We provide dashboards to track performance over time.
Yes! RAG systems can be updated in real-time as new documents are added. We implement incremental indexing pipelines that keep your knowledge base current without full re-indexing.
Still have questions? We're here to help. Contact us for more information.
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