RAG & Custom LLM Solutions
Unlock the power of your proprietary data. We build secure RAG systems that allow your team to query thousands of internal documents instantly.
Your company sits on terabytes of valuable data (PDFs, Confluence, Jira, historical emails), but employees can't find answers quickly. Public LLMs like ChatGPT hallucinate and pose severe security risks for your confidential data.
Impact
Employees waste 2+ hours daily searching for internal information. Compliance risks from employees pasting sensitive data into public AI tools.
We build secure Retrieval-Augmented Generation (RAG) pipelines. This connects an advanced LLM directly to your secure databases, ensuring the AI only answers based on your actual data.
Technical Approach
We implement Pinecone/Milvus for vector search, LangChain/LlamaIndex for orchestration, and deploy open-source models (Llama 3) or secure Azure OpenAI instances to guarantee data privacy.
Keyword searching through hundreds of folders, asking colleagues on Slack, and reading through 50-page PDFs to find a single policy answer.
Typing a natural language question into a secure company portal and receiving an instant, cited answer directly from your internal documents.
Strict adherence to global data privacy laws. We never train public AI models on your proprietary data.
Architecture designed to meet rigorous healthcare and enterprise security compliance standards natively.
Scalable cloud-native deployments via AWS and Vercel Edge networks ensuring 99.99% uptime.
Everything you need to know about our RAG & Custom LLM Solutions process.
RAG is an AI framework that connects a Large Language Model (like GPT-4) to your private database. Instead of guessing, the AI retrieves the exact document and generates an answer based strictly on that document.
While no AI is perfect, RAG drastically reduces hallucinations by forcing the model to cite specific sources from your database. If the answer isn't in your data, it is programmed to say 'I don't know'.
Rarely. RAG is much more cost-effective than fine-tuning or training from scratch because it uses a pre-trained model and gives it access to a dynamic search engine of your data.
Let's build a secure RAG prototype.
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