RAG Development Services for Enterprise Knowledge Assistants
Retrieval-augmented generation (RAG) makes a language model answer from your documents instead of from its training data. The model retrieves the relevant passages from your own content first, then writes an answer grounded in them and cites what it used. Mixcore Studio builds these systems for teams that need reliable answers over contracts, policies, product documentation, support history or research archives.
RAG is the most common first production AI system inside a company, and also the most commonly under-engineered. A demo over fifty documents is straightforward. A system that stays accurate over half a million documents, respects who is allowed to see what, and does not quietly degrade as content changes is an engineering project.
What we build
- Internal knowledge assistants — staff ask a question in plain language and get an answer with links to the source passages.
- Customer-facing support AI — grounded answers drawn from your real documentation, with escalation to a human when confidence is low.
- Document intelligence pipelines — extraction, classification and summarisation across contracts, invoices, claims and reports.
- Search modernisation — semantic and hybrid retrieval layered onto an existing search product that only does keyword matching today.
Why naive RAG fails, and what we do differently
Most disappointing RAG systems share the same handful of causes, and each has a known engineering answer.
- Bad chunking — splitting on a fixed character count severs tables and clauses from their context. We chunk on document structure and keep parent context attached.
- Vector-only retrieval — embeddings alone miss exact identifiers, part numbers and names. We combine dense vectors with keyword search and apply a reranking pass over the merged candidates.
- No permission model — a single shared index will happily quote a document the asker was never allowed to read. We filter by identity at retrieval time, not after generation.
- No grounding check — we verify that the generated answer is actually supported by the retrieved passages, and return an honest "not found" rather than a fluent guess.
- No evaluation — we build a labelled question set from your real queries and score retrieval and answer quality on every change.
The stack we work in
Vector storage is usually pgvector when you already run PostgreSQL and want one less system to operate, or a dedicated engine such as Qdrant when scale and filtering complexity justify it. Generation runs on Claude, GPT or Gemini, or on a self-hosted open-weight model served through vLLM when data must not leave your infrastructure. We deploy into your own cloud account when regulatory or contractual constraints require it.
How an engagement runs
We start by testing retrieval quality against a sample of your real content and real questions, because that is where these systems succeed or fail — not in the prompt. You get a measured baseline before committing to a full build, then a production system with permissions, evaluation and monitoring in place.
Our expertise
- Vector database engineering
- Hybrid and semantic search
- Grounded answers with citations
- Permission-aware retrieval
- Retrieval evaluation
- Private and on-premise deployment
Frequently asked questions
What is retrieval-augmented generation?
Retrieval-augmented generation is a technique where a language model first retrieves relevant passages from a chosen body of content, then generates its answer using those passages as source material. It lets a model answer accurately about private or recent information it was never trained on, and it allows the answer to cite exactly which documents it came from.
Is RAG better than fine-tuning a model on our documents?
For factual question answering over changing content, RAG is almost always the right choice. Updating a RAG system means re-indexing a document, which is immediate and cheap, whereas fine-tuning bakes information into weights and must be redone as content changes. Fine-tuning is the better tool for teaching a model a format, tone or specialised task rather than a body of facts. The two are frequently combined.
Can a RAG system respect our existing access permissions?
Yes, and it must. We apply your identity and access rules at retrieval time so a user can only ever receive answers drawn from documents they are already authorised to read. Filtering after generation is unsafe, because by then the restricted content has already influenced the answer.
Can the system run entirely inside our own infrastructure?
Yes. When data residency, regulatory or contractual constraints require it, we deploy the full pipeline into your own cloud account or on-premise environment and serve an open-weight model through vLLM, so no document or query leaves your network.
How do you stop a RAG system from making things up?
We verify that generated answers are supported by the retrieved passages, require the system to cite its sources, and design it to return an explicit "not found" when retrieval turns up nothing relevant. A system that admits ignorance is far more valuable in an enterprise setting than one that is always fluent.
Contacts
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