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RAG Development Services
Answers drawn fromyour own documents
We build retrieval systems that let people ask questions of company documents and receive answers with sources, respecting who is allowed to see what and saying so plainly when the documents hold no answer.
A knowledge assistant earns trust when it shows its sources, respects every permission and admits what it could not find.
Answers drawn from your own documents
- 01Cited answers
- 02Permission-aware search
- 03Measured quality
Retrieval-augmented generation, usually shortened to RAG, connects a language model to your content. A question is matched to the most relevant passages, and the model writes an answer from those passages with links back to them. We build the ingestion, indexing, retrieval and answer layers, and the evaluation that shows whether the system is finding the right material.
It suits organizations whose knowledge is spread across drives, wikis, tickets and policy files, where staff or customers lose time searching. The same foundation powers enterprise knowledge search, support assistants and research tools. It does not suit questions that need calculation over structured records, which belong to a database query, and we separate the two early.
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From Kickoff to Launch
We list the sources, their owners, formats, volumes and access rules, collect real questions from the people who will use the system and agree what a correct answer looks like.
Ingestion and search are built and measured before any answer is generated, since most poor answers come from the wrong passages being found and not from the model.
The model is given the retrieved passages and firm instructions on citing, refusing and staying within the sources, and the interface is built to show the evidence beside every answer.
The system is scored on the question set, tested by a pilot group whose feedback is logged, tuned where it falls short and then opened to more users with monitoring in place.
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What buyers ask before they start
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Book a callRAG stands for retrieval-augmented generation. The system searches your content for passages relevant to a question and a language model writes the answer from them. It is the right approach when answers live in documents that change, when sources must be shown and when access differs between people. It is less suited to arithmetic over records or to tasks that need no company knowledge.
Permissions are applied at retrieval, before the model sees anything. The index stores who may open each document, taken from the source system, and every search is filtered by the identity of the person asking. A passage the user cannot open is never retrieved, so it cannot appear in an answer. We test this with accounts that hold different rights.
With a set of real questions that have agreed correct answers and known source documents. Each change is scored on whether the right passages were retrieved, whether the answer is supported by them and whether it addresses the question. People also rate answers in use, and low ratings feed back into the set. Quality is reported as figures over time.
The system should say so. We instruct and test it to decline when retrieval returns nothing relevant or the passages conflict, and to point the person to an owner or a search instead. Refusals are logged, since a cluster of unanswered questions shows exactly which content is missing. An assistant that always answers is one that sometimes invents.
It is the same foundation used in two ways. Knowledge search returns ranked documents and passages for people to read. A RAG assistant goes one step further and writes an answer from them. Both depend on good ingestion, relevance, permissions and fresh content. Many organizations start with search, prove that retrieval works and then add generated answers on top.
