Sovereign AI & Custom RAG Solutions

Intro

In an era of data leaks and privacy concerns, relying on third-party AI APIs constitutes an unacceptable operational risk. We champion "Sovereign AI," an approach where models, data, and inference remain entirely within your control, on your hardware. We specialize in deploying and fine-tuning Large Language Models on-premises, ensuring absolute data privacy, operational continuity, and freedom from external dependencies or censorship.

Technical Deep Dive: The Building Blocks of Sovereign AI

Model Quantization: Performance Meets Efficiency

To run powerful models on-premises, performance must be balanced with hardware constraints. We are experts in model quantization, a process that reduces the memory footprint and computational cost of LLMs, often with minimal impact on quality. This is how we deliver fast, efficient models that run on your infrastructure, not ours.

Production-Ready Retrieval-Augmented Generation (RAG)

An LLM is only as good as the data it can access. Our primary offering is the construction of custom Retrieval-Augmented Generation (RAG) applications that ground LLMs in your factual, proprietary data. Our pipelines are robust, scalable, and battle-tested.

• Data Ingestion & Chunking: We build automated pipelines to ingest data from any source—web scrapes, complex PDFs with tables, document repositories (SharePoint, Confluence), APIs, and databases. We employ intelligent chunking strategies, from simple recursive splitting to advanced semantic chunking, to ensure data is segmented into contextually rich passages for embedding.
• Vectorization & Storage: We select and fine-tune state-of-the-art embedding models from the MTEB Leaderboard based on the specific domain of your data (e.g., Nomic Embed for general purpose, or domain-specific models for finance or law). We deploy and manage high-performance vector databases like ChromaDB for rapid prototyping and scale to production-grade solutions like Weaviate or self-hosted FAISS for massive datasets.
• Retrieval & Reranking: Simple similarity search often fails with complex queries. We solve the "lost in the middle" problem by implementing multi-stage retrieval pipelines. This includes hybrid search (keyword + vector) and sophisticated rerankers (like Cohere Rerank or cross-encoders) to ensure the most relevant document chunks are placed at the top, dramatically improving response accuracy and eliminating hallucinations.

The Code 0 Advantage: Beyond Basic RAG

Many can build a simple RAG proof-of-concept. We build enterprise-grade systems designed for accuracy and scale. We incorporate advanced techniques to tackle complex information retrieval challenges:

• Self-Corrective and Adaptive RAG: We implement frameworks where the system autonomously refines queries and grades its own retrieved documents for relevance, ensuring higher quality context is passed to the LLM.
• GraphRAG: For highly interconnected data, we leverage knowledge graphs. This allows the LLM to traverse relationships and answer complex questions that a simple vector search cannot, such as "What are all the vulnerabilities reported by Vendor X that affect our production servers?"
• Agentic Workflows: We design LLM-based agents that can break down complex requests into smaller steps, decide which documents to retrieve, and even perform actions based on the information found, truly automating complex workflows.

Use Cases

• Cybersecurity: Developing a RAG system to ingest daily STIX/TAXII and MISP feeds, internal incident reports, the CISA KEV catalog, and private threat intelligence. This allows security analysts to ask: "Summarize the TTPs of the latest FIN7 campaign, cross-reference with our internal logs for indicators of compromise from the last 90 days, and list affected assets."
• Intelligence Analysis: Creating a secure RAG-powered chatbot for analysts to query a multi-terabyte classified knowledge base. The system provides cited, page-accurate answers with source links, turning a static archive into an interactive, conversational intelligence source.
• Engineering & DevOps: Building an internal engineering assistant that has ingested the entire company codebase, all technical documentation (Confluence, service architecture diagrams), and historical incident tickets from Jira. A junior developer can ask: "What is the standard procedure for deploying a hotfix to the billing-service? Include the required approvers and the link to the production checklist."

Complete Code Example: Building a Local RAG System

This complete, runnable Python script demonstrates how to build a basic RAG application locally using Ollama and LangChain.

# Step 0: Installation
# Ensure you have Ollama installed and have pulled a model, e.g., `ollama pull llama3`
# Then, install the required Python packages.
# pip install langchain langchain_community langchain_core ollama chromadb fastembed

import os
from langchain_community.vectorstores import Chroma
from langchain_community.chat_models import Ollama
from langchain_community.embeddings import FastEmbedEmbeddings
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Step 1: Load and Prepare Documents
# For a real use case, this would be a complex set of documents.
# Here, we'll create a dummy document.
doc_content = """
Project Dragonfire Security Protocol:
1. All API endpoints must be authenticated using JWT Bearer tokens.
2. The token signing key must be rotated every 90 days.
3. Access to the production database is restricted to the 'db-service' role in Kubernetes.
4. All sensitive data, including PII, must be encrypted at rest using AES-256.
5. Weekly vulnerability scans are mandatory for all container images.
"""
with open("security_protocol.txt", "w") as f:
    f.write(doc_content)

# Load the document from the file
loader = TextLoader("./security_protocol.txt")
docs = loader.load()

# Split the document into smaller chunks for better retrieval
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
documents = text_splitter.split_documents(docs)

# Step 2: Setup the local model, embeddings, and vector store
# Initialize the local LLM via Ollama
llm = Ollama(model="llama3")

# Initialize the embedding function and create the vector store
# This will download an embedding model the first time it's run
vectorstore = Chroma.from_documents(documents, FastEmbedEmbeddings())

# Create the retriever interface
retriever = vectorstore.as_retriever()

# Step 3: Create the RAG Chain
# This prompt template ensures the LLM uses *only* the retrieved documents
# to answer the question, which prevents it from making things up.
prompt = ChatPromptTemplate.from_template("""
Answer the following question based ONLY on the provided context.
If you don't know the answer, just say that you don't know. DO NOT use any other information.


{context}


Question: {input}
""")

document_chain = create_stuff_documents_chain(llm, prompt)
retrieval_chain = create_retrieval_chain(retriever, document_chain)

# Step 4: Invoke the chain with a question
question = "What are the key security protocols for Project Dragonfire?"
response = retrieval_chain.invoke({"input": question})

print("--- Question ---")
print(question)
print("\n--- Answer ---")
print(response["answer"])

# Clean up the dummy file
os.remove("security_protocol.txt")

Challenges & Our Solutions

Links

• Run LLMs Locally: Ollama
• Build LLM Applications: LangChain
• Quantization Technical Details: GGUF Format
• Embedding Model Rankings: Hugging Face MTEB Leaderboard
• Production Vector Database: Weaviate