Technical Problem
Standard large language models cannot reach a company’s own data, go stale over time, and answer questions with reasoning that is not grounded in fact (hallucination).
ServicesI — AI Architecture and Model Engineering
EnterpriseRAGArchitecture
Vector Database (Vector DB) Integration
Architectural solution
We build a hybrid RAG (Retrieval-Augmented Generation) layer between your storage estate — SQL, NoSQL, cloud object storage, on-premise servers — and the language models. We run the pipeline end to end: splitting documents into semantic chunks, representing them as embeddings, and indexing them into high-performance vector databases (Qdrant, Milvus, Pgvector). For text retrieval we integrate hybrid search that combines semantic (dense) and keyword (sparse) matching, plus reranking.
Operational outcome
A knowledge layer where every answer arrives together with the document it rests on: where no source can be shown, the system stays silent instead of producing an answer. When the content is updated the answers follow, because the knowledge lives in the source rather than inside the model.
Starting Conditions
This service applies when internal knowledge cannot be found by searching, when the answer to the same question differs by department, or when nobody can show which document a model's answer came from. Two preconditions have to be on your side: a reachable document source, and a definition of who may see what. If that definition does not exist, we start there — an answer whose source is unclear is riskier than no answer at all.
How We Work
We begin with an inventory: which source, which format, which permission. Then a pilot on a limited corpus, because the method has to be seen working on your data before the whole archive is indexed. Measurement is set up alongside the pilot, not after it — measurement added later cannot say what improved: every answer is recorded together with the document it came from. Going live is gradual, one department first, then source by source. Every step has a stopping point you can return from.
Out of Scope
This service does not train a model; if behaviour specific to your organisation is needed, Language Model Training is separate work. Nor does it take on the whole of data cleaning: we make broken scans and missing metadata visible, while the decision and the effort to fix them stay with you. We index no document you have not granted access to — the RAG layer carries your existing permissions, it does not widen them. It should also not be expected to answer a question it cannot cite a source for; staying silent is the intended behaviour, not inventing one.