Retrieval-Augmented Generation (RAG)
RAG connects a large language model to your own knowledge base. Instead of relying on generic training data, the system retrieves relevant documents, passages or records and uses them to generate accurate, source-grounded answers with citations.
Document ingestion & chunking
We build pipelines that ingest documents in many formats (PDF, Word, web pages, databases), then clean, split and structure them so the retrieval layer finds the right context every time.
Embeddings & vector search
Using embedding models and vector stores such as Qdrant, we turn your content into searchable semantic vectors so users can ask in natural language and get results based on meaning, not just keywords.
Private data & LLMs
Secure, private LLM deployments using Ollama and Qdrant vector databases, so enterprises can leverage AI while keeping full control of their sensitive data.
Experience with leading AI models
Hands-on expertise with closed models such as Anthropic Claude, ChatGPT, Gemini and Groq, plus open models such as DeepSeek, Llama, Mistral and Qwen, so we integrate state-of-the-art AI into real business solutions.
Evaluation & grounding
We measure retrieval quality, answer faithfulness and hallucination rates, then add guardrails, re-ranking and human feedback loops so answers stay accurate, citable and safe.
Intelligent automation
Natural language processing, document understanding and agentic workflows that remove manual steps from high-volume processes.
Predictive analytics
Forecasting, scoring and anomaly detection models built on your operational data and monitored in production.