Our Approach to Generative AI
We separate the hype from the reality, focusing on what it takes to get LLMs working reliably in production.
RAG-Powered Chatbots
Stop generic chatbot responses. We build Retrieval-Augmented Generation (RAG) systems that answer questions based on your private data, complete with citations to prevent hallucinations.
Semantic Search over Private Data
Go beyond keyword search. We use vector embeddings and databases like Pinecone or Weaviate to enable true semantic search that understands intent and context.
LLM-Powered Workflow Automation
We build agents that connect LLMs to your existing tools and APIs, automating complex tasks like data extraction, summarization, and routing.
Why Our Approach Is Different
Building with LLMs is easy. Building reliable products with them is hard.
Private & Secure by Default
We never send your sensitive data to public APIs. We build solutions using private cloud deployments or enterprise-grade APIs to ensure your data stays yours.
Focus on Reducing Hallucinations
We use techniques like RAG, fact-checking, and output validation to build systems you can actually trust. A model that makes things up is a liability.
Cost & Performance Optimized
Running large models is expensive. We optimize every step—from prompt engineering to inference—using tools like vLLM to ensure low latency and manageable costs.
Our Generative AI Toolkit
We use the best tools for building robust, production-ready LLM applications.
Foundation Models
Expertise with OpenAI (GPT-4), Anthropic (Claude 3), Llama, and open-source models.
Fine-tuning & Adaptation
Efficient fine-tuning with LoRA and PEFT. Production-grade RAG systems.
Vector Databases & Search
Pinecone, Weaviate, Chroma, and FAISS for scalable semantic search.
Optimized Inference
Using vLLM, TensorRT-LLM, and other tools to serve models quickly and cheaply.
Safety & Governance
Implementing guardrails, content filtering, and explainability to ensure safe and responsible AI.
Evaluation & Observability
Tools like Ragas, Arize Phoenix, or LangSmith for continuous evaluation and monitoring of LLM outputs.
Frequently Asked Questions
How do you stop the model from making things up (hallucinating)?
+We primarily use Retrieval-Augmented Generation (RAG), which forces the model to base its answers on your provided documents. We also implement fact-checking against knowledge bases and can include citations in the output for full traceability.
Will you use our private data to train a model?
+Yes, but always securely. We can fine-tune a model on your data within your own private cloud environment, ensuring your proprietary information never leaves your control and is never exposed to a third-party model provider.
Is it expensive to run our own custom LLM solution?
+It can be, but we specialize in cost optimization. We choose the right-sized model for the task, apply efficient fine-tuning methods, and use optimized inference servers. Often, a smaller, fine-tuned model can outperform a larger, more expensive one.
What's a 'vector database' and why do I need one?
+A vector database stores your data (like text from documents) as numerical representations (vectors). This allows for extremely fast and accurate ‘semantic search,’ where the system finds results based on meaning and context, not just keywords. It’s the core engine behind a modern RAG system.
How quickly can we build a prototype?
+Using a RAG approach with your existing documents, we can often build a powerful and useful proof-of-concept in just a few weeks. This allows you to validate the approach and demonstrate value quickly before committing to a larger project.
Should we fine-tune a model or use RAG?
+Usually both, but they serve different purposes. RAG is best for giving models access to specific, changing information (like your latest documents). Fine-tuning is better for teaching a model a specific style, format, or highly specialized technical jargon. We help you find the right balance.