Technical problem
Standard large language models cannot reach private in-house data, fall out of date, and produce confident answers with no basis in fact when questioned.
Enterprise RAG architecture and vector database integration
Architectural solution
We build a hybrid RAG (Retrieval-Augmented Generation) layer between your storage infrastructure — SQL, NoSQL, cloud storage, on-site servers — and the language models. We run the chunking of documents into semantic pieces, their representation in vector space as embeddings, and their indexing into high-performance vector databases (Qdrant, Milvus, pgvector). For text search we integrate hybrid retrieval that combines dense semantic and sparse keyword matching, together with reranking algorithms.
Operational outcome
A knowledge infrastructure with near-zero hallucination risk, fed by data that updates as it changes, answering queries in milliseconds and putting internal knowledge within seconds' reach.
I
AI architecture and model engineering
Fine-tuning, LoRA/QLoRA and model distillationGeneral-purpose models fall short against the jargon of a given sector — law, medicine, finance, manufacturing — or against business logic specific to one company; and running general models in production brings high API and hardware costs.Metric-driven evaluation and comparative model test infrastructureModel performance is judged by surface-level observation, and once systems are live there is no analytical way to track accuracy and safety rates.
GET STARTED
Which one shall we start with?
See it on your own data in one session, or write first and ask what we do.