Connect Generative AI & LLMs to Your Enterprise Data
Build context-aware LLM products using Retrieval-Augmented Generation (RAG), vector databases, and model fine-tuning.
Design My RAG PipelineChallenges We Help You Solve
LLM Hallucinations
Public AI models make up facts. We use RAG to pin AI replies to your verified PDF files.
Data Security Fears
Uploading private PDF agreements to public AI systems leaks data. We set up private cloud LLMs.
Lack of Real-time Data
Public LLMs have training cutoff dates. We connect LLMs directly to your SQL databases.
Tailored Solutions Built for Business Performance
RAG Pipelines Setup
Connecting company PDFs, docs, and databases to vector stores (Pinecone, PGVector) for accurate AI searches.
OpenAI & Claude Integrations
Custom prompting, API keys setup, and rate-limit managers to integrate GPT/Claude models.
Local/Open-Source LLMs
Deploying Llama 3 or Mistral models on your private servers so data never leaves your network.
Custom LLM Fine-Tuning
Training models on your company’s brand voice, document templates, or specific coding syntax.
Our Technical Framework & Stack
Technologies and Platforms We Use
From modern frameworks to industry-standard platforms, we harness technology to empower businesses. Our approach ensures innovation, reliability, and solutions that are ready for the future.
Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is a technology that searches your private documents first, retrieves the relevant text, and feeds it to the LLM. This ensures the AI answers using only your verified data, preventing hallucinations.
Why would we fine-tune a model instead of just prompting it?
Fine-tuning is best when you need the model to learn a highly specific formatting style, medical/legal vocabulary, or custom programming codes that general LLMs do not know.
Can you build a private alternative to ChatGPT?
Yes, we build custom chat interfaces deployed on your private servers using open-source models, allowing secure document chats with zero monthly per-user licensing fees.
How does vector search work?
Vector search converts text sentences into math numbers (embeddings) that represent meaning. This allows the AI to search by concept rather than just matching exact keywords.
What are the token running costs of LLM APIs?
API costs depend on usage. We implement strict rate-limiting, semantic caching (saving common answers), and optimized prompts to reduce API usage costs by up to 50%.