# Answers Grounded in Your Own Knowledge. Retrieval-augmented generation that answers from your documentation, your data and your policies — with citations, and with the same permissions the person asking already has. ## What it is A language model knows what it was trained on. It does not know your pricing, your policies, or what your team decided last quarter. RAG closes that gap by retrieving the relevant material at query time and requiring the model to answer from it. Done properly, it changes the failure mode. Instead of a confident invention, you get a cited answer or an admission that the answer is not in the corpus. That distinction is the entire value. The engineering is mostly in retrieval, not generation. How documents are chunked, how queries are expanded, how results are re-ranked, and how permissions are applied determine whether the system is useful or merely impressive in a demo. ## Capabilities - **Ingestion pipelines** — PDFs, Office documents, wikis, ticket histories, databases and code, with incremental re-indexing - **Structure-aware chunking** — Splitting that respects headings, tables and clause boundaries rather than fixed character counts - **Hybrid retrieval** — Vector similarity combined with keyword search and re-ranking, because pure embedding search misses exact terms - **Permission-aware access** — Retrieval filtered by the asker’s existing entitlements — the system cannot surface what they could not already open - **Citation and grounding** — Every answer traceable to its source passages; below-threshold retrieval returns an honest refusal - **Evaluation** — A held-out question set scored on retrieval accuracy and answer faithfulness, run on every index change ## How it works Documents → ingestion → chunking → embeddings → vector store → hybrid retrieval → re-ranking → grounded response with citations. ## What actually decides whether retrieval works Almost none of it is the model. Most of it is what goes into the index, and what the system does when nothing good comes back out. - **Chunking is a content decision, not a parameter** — A fixed window that splits a table from its header, or a clause from the exception that qualifies it, produces passages that retrieve well and answer wrongly. Splitting follows the structure of the document, which means ingestion is written per source type rather than configured once. - **Embedding search alone misses the exact term** — Vector similarity is weak on part numbers, error codes, policy references and names — which is most of what people actually search for. Keyword search runs alongside it and a reranker decides the final order, because recall and precision are different problems solved at different stages. - **Permissions belong at retrieval, not at the answer** — Filtering the response is a leak waiting to be summarised. The asker’s entitlements become part of the query, so a passage they could not open is never a candidate — the tenant and role boundary is enforced by the index rather than requested in a prompt. - **Freshness is a property of the pipeline** — An answer is only as current as the last successful re-index. Incremental updates run on the source system’s webhook or your release cadence, and deletions have to propagate: a document removed from the wiki that survives in the index is the failure nobody thinks to test. - **Refusal is a feature, and it has a threshold** — Below a retrieval confidence bar the system says the answer is not in the corpus instead of composing a plausible one. Where that bar sits is a business decision — how wrong an answer may be, against how often the system declines — so it is set with you and then measured, not left at a default. - **Evaluation is what makes a change safe to ship** — Retrieval quality and answer faithfulness are separate scores and they fail separately: a system can find the right passage and still misread it, or answer correctly from the wrong source and get away with it until the source is wrong. Measuring them apart is what tells you which half to fix. ## Use cases ### Internal knowledge search - **Trigger** — An employee asks a policy or process question - **Reasoning** — Retrieves across wikis, documents and past decisions within their permissions - **Action** — Answers with citations to the source - **Result** — Answers stop depending on who is online ### Support agent assist - **Trigger** — An agent opens a ticket - **Reasoning** — Retrieves the relevant policy and similar resolved cases - **Action** — Surfaces a drafted response with sources for the agent to verify - **Result** — New agents work from institutional knowledge immediately ### Product documentation Q&A - **Trigger** — A customer asks a question in-product - **Reasoning** — Retrieves from current documentation only - **Action** — Answers with links, or routes to support when unsupported - **Result** — Documentation becomes usable without being read ### Contract and policy review - **Trigger** — A document is uploaded for checking - **Reasoning** — Retrieves comparable clauses and your standard positions - **Action** — Flags deviations with references to the standard - **Result** — Review starts from a shortlist rather than page one ## Integrations - SharePoint - Google Drive - Confluence - Notion - Zendesk - Intercom - S3 - PostgreSQL - Snowflake - Internal wikis - Custom document stores ## Technologies - OpenAI - Anthropic - pgvector - Pinecone - PostgreSQL - Redis - LangChain - Python - FastAPI - OpenTelemetry ## How we work Five phases, each ending in a decision you make: Discover → Architect → Prototype → Production → Optimize. Full process: [How we work](https://www.bralakai.com/how-we-work). ## FAQs ### RAG or fine-tuning? Different problems. RAG supplies knowledge that changes; fine-tuning shapes behaviour and format that does not. If your answer changes when a document is updated, you need retrieval. Most business use cases are retrieval problems mistaken for training problems. ### Does our data go to the model provider? Only the retrieved passages needed to answer, and only if you use a hosted provider. Where that is unacceptable, we deploy open models in your environment. This is decided before any data moves. ### How do you stop it answering things people shouldn’t see? Permissions are applied at retrieval, not at the answer. If the asker cannot open the document, the passage is never retrieved, so it cannot leak through a summary. ### What if the answer isn’t in our documents? It says so. Retrieval below the confidence threshold returns a refusal rather than a generated guess, and the gap is logged — over time that log tells you what documentation is missing. ### How do we know the answers are accurate? A held-out evaluation set of real questions with known answers, scored on whether retrieval found the right source and whether the answer stayed faithful to it. It runs on every index change, so quality is measured rather than assumed. ### How often does the index update? As often as your content changes. Incremental re-indexing runs on a schedule or on a webhook from the source system. ## Related - [AI Agent Development](https://www.bralakai.com/ai-agent-development) — Agents that reason through a problem, call your systems and complete the work end to end. - [AI Copilots](https://www.bralakai.com/ai-copilots) — Copilots inside the tools your team already uses — surfacing context and drafting the next step. - [AI Integration](https://www.bralakai.com/ai-integration) — Connecting intelligent systems to your CRM, ERP, databases and internal tools, reliably. - [Multi-Agent Systems](https://www.bralakai.com/multi-agent-systems) — Specialist agents with narrow scope, working under an orchestrator that manages context and control. - [Healthcare (industry)](https://www.bralakai.com/industries/healthcare) - [SaaS & Technology (industry)](https://www.bralakai.com/industries/saas) - [What Is RAG? (article)](https://www.bralakai.com/insights/what-is-rag) — Retrieval-augmented generation closes the gap between what a model was trained on and what your organisation actually knows. The interesting part is not the generation. It is that a well-built RAG system changes what happens when it does not know. - [RAG vs Fine-Tuning (article)](https://www.bralakai.com/insights/rag-vs-fine-tuning) — These are not two ways of doing the same thing. One changes what a model can look up; the other changes how it behaves. Choosing between them is easy once you know which of those you actually need — and most business cases need the first. ## Your Next Intelligent System Starts Here. Tell us what you’re trying to improve, automate or build. We’ll help you identify the right AI strategy and engineering path. - [Book an AI Strategy Call](https://www.bralakai.com/contact) - [Start a Project](https://www.bralakai.com/contact) --- *Bralak AI — Building Intelligent Solutions · Automating the Future.* Bralak AI Pvt. Ltd. — Noida, UP, India. - Canonical page: https://www.bralakai.com/rag-development - Agent index: https://www.bralakai.com/llms.txt · full text: https://www.bralakai.com/llms-full.txt - Contact: info@bralakai.com · https://www.bralakai.com/contact