RAG & Knowledge Systems
Retrieval-augmented generation connects a language model to your own documents so it answers from your content rather than its training data, with citations back to the source. We build production RAG systems over knowledge bases, policies, contracts, tickets and technical documentation — with the retrieval quality work, access controls and evaluation that separate a system people trust from a demo they abandon.
The problem
The painful work behind a real answer
Three answers to one question
Someone asks whether the refund window is fourteen days or thirty. The policy lives in a PDF on SharePoint that was superseded by a Confluence page, which is contradicted by a helpdesk macro nobody has updated since the last pricing change.
Your best people are the search index
Your longest-serving support lead is who everyone actually asks, which works until they are in a meeting, on leave, or gone.
The same twenty questions, forever
Every new hire spends their first month learning who to ask rather than where to look, and re-answering documented questions is where senior time quietly goes.
What a real fix takes
A search box with a language model behind it is not a knowledge system. Real retrieval needs layout-aware document processing, hybrid search that can find an exact error code as readily as a concept, permission filtering applied before generation rather than after, and an evaluation set that proves the answers are right — none of which demo well, and all of which decide whether anyone still trusts the system in month three.
Outcomes
What changes once it is in place
Answers instead of search results
People ask a question in their own words and get a direct answer with a link to the paragraph it came from. The citation matters as much as the answer, because it lets someone verify in five seconds rather than trust blindly.
Institutional knowledge stops walking out the door
Documentation nobody reads becomes documentation the system reads. The knowledge that lived with three long-tenured people becomes accessible to everyone.
Support and internal query load drops
A meaningful share of internal questions are re-asks of things already documented. Deflecting them frees the people who currently answer them.
Accuracy you can measure
We build an evaluation set of real questions with known correct answers, and report retrieval and answer quality as numbers. Systems without this are trusted on vibes until the first bad answer destroys that trust permanently.
What we build
The parts that actually decide it
Ingestion pipeline
Connectors to where your content actually lives — Confluence, SharePoint, Google Drive, Notion, Zendesk, S3, your database, PDFs on a file share. Incremental sync so answers reflect current content, not a snapshot from launch day.
Document processing
The unglamorous work that determines whether the system succeeds: parsing PDFs that are really scans, preserving table structure, keeping headings attached to their content, handling multi-column layouts. Most RAG failures are document processing failures wearing a retrieval costume.
Chunking and indexing
Structure-aware chunking rather than fixed character counts, with metadata for filtering. Hybrid search combining semantic and keyword retrieval, because pure vector search reliably fails on exact terms — product codes, error numbers, policy references.
Reranking and query handling
Retrieve broadly, rerank precisely. Query rewriting for vague or conversational questions. Handling for questions that span multiple documents, which naive implementations answer confidently and wrongly.
Answer generation with citations
Every claim traceable to a source passage. Explicit refusal when the corpus does not contain the answer — a system that says "this is not covered in the documentation" is more valuable than one that improvises.
Permission-aware retrieval
Users see answers only from documents they are entitled to read. Enforced at retrieval, not filtered afterwards. This is a hard requirement in most enterprises and a common reason pilots fail their security review.
Evaluation harness
A question set with known answers, measuring retrieval recall, answer accuracy, citation correctness and refusal behaviour. Run on every change so improvements are demonstrable and regressions are caught.
What you receive
Two artifacts, described honestly
Both are yours. The ingestion configuration, the chunking and retrieval settings, the evaluation set and the questions in it stay with you — the evaluation set in particular is the asset, because it is what lets anyone, including a different vendor, prove the system still works.
The answer surface
Wherever your team already works — a web app, a Slack or Teams bot, or a panel inside your existing helpdesk. Someone asks in plain language and gets a direct answer where every claim carries a citation that opens the exact passage it came from, alongside the source document's title, owner and last-updated date, so the reader can judge for themselves whether it is current. When the corpus does not contain the answer, it says so rather than improvising.
The evaluation report
The artifact that makes the rest defensible: a table of your real questions, re-run on every change, with a row per question showing
- the question, in the words someone actually used
- the passages retrieved, in the order the system ranked them
- the answer produced
- the source it cited, and whether that source genuinely supports the claim
- pass or fail against the verified answer
- what regressed since the previous run, and which change caused it
How it works
A sequence, not a big-bang launch
- Week 1
Corpus assessment
We examine what you actually have. Volume, formats, quality, duplication, contradictions, how current it is. Contradictory documentation is extremely common and the system will surface it, so we plan for that rather than being surprised by it.
- Week 1–2
Evaluation set
Fifty to two hundred real questions with verified correct answers, drawn from your support tickets, internal chat, or subject-matter experts. Built before the system, because it defines what "working" means.
- Weeks 2–4
Pipeline build
Ingestion, processing, chunking, indexing, retrieval. Measured against the evaluation set from the first working version.
- Weeks 4–6
Retrieval tuning
Where the quality is won. Chunking strategy, hybrid weighting, reranking, query rewriting, metadata filters. Iterated against measurements, not impressions.
- Weeks 6–8
Interface and access control
The surface people use — web, Slack, Teams, or embedded in an existing tool — plus permission enforcement and feedback capture.
- Weeks 8–10
Pilot and iteration
Real users, real questions. Failure cases collected and added to the evaluation set. This phase reliably reveals question types nobody anticipated.
Ongoing
Content sync, periodic re-evaluation, tuning as the corpus grows. RAG systems degrade quietly as content drifts; ownership is not optional.
Technology
A current, curated stack
Vector stores
pgvector where you already run Postgres and volume is moderate — often the right answer and rarely the recommended one. The dedicated stores at larger scale, or where managed operation matters.
- pgvector
- Qdrant
- Weaviate
- Pinecone
- Milvus
- Chroma
Retrieval and reranking
Hybrid retrieval — BM25 alongside dense vectors — because pure vector search fails on exact terms like product codes and error numbers.
- Elasticsearch
- OpenSearch
- Cohere Rerank
- BM25
Embeddings
Selected by benchmark against your corpus rather than by reputation; the leaderboard winner is frequently not the winner on your documents.
- OpenAI
- Cohere
- Voyage
- BGE
- Nomic
Generation
Commercial models for quality-sensitive answers, open-weight where data residency or volume economics require self-hosting.
- Claude
- GPT
- Gemini
- Llama
- Mistral
Frameworks
Used where they help, direct implementation where they add indirection without value. We are not committed to any of them.
- LlamaIndex
- LangChain
- Haystack
- DSPy
Document processing
The layer that usually needs the most custom work — layout-aware parsing, OCR for scanned material, table extraction.
- Unstructured
- LlamaParse
- Docling
- Azure Document Intelligence
Evaluation and tracing
Retrieval recall, answer accuracy, citation correctness and refusal behaviour, run on every change so regressions surface immediately.
- Ragas
- LangSmith
- Langfuse
- Phoenix
Where this applies
Strongest on a real corpus, honest about a thin one
Strongest where a large, reasonably well-maintained corpus exists and people repeatedly need answers from it.
Weakest where documentation is thin, badly out of date, or contradictory. RAG surfaces the state of your content; it does not improve it. If the corpus is poor, fixing it comes first, and we will say so rather than build a system that confidently returns wrong answers.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
Fixed scope, quoted before any work starts
Fixed scope, quoted after the week-one corpus assessment. You get a firm figure before any build work starts. Three things drive it.
Document volume
How many documents there are, and how current they need to stay — incremental sync over a large corpus is more work than a one-off load.
Processing difficulty
How much work your files actually need. A text-native PDF is cheap; a scanned one full of tables is not, and that difference dominates the estimate more often than volume does.
Access-control strictness
Permission-aware retrieval against your existing identity model is a materially larger build than an open corpus, and it is a hard requirement in most enterprises.
Build and run are quoted separately. Embedding, inference and hosting are ongoing and scale with corpus size and query volume, so a quote that names only the build is hiding the second number.
FAQ
Frequently asked questions
The system searches your documents for passages relevant to a question, then asks a language model to answer using only those passages. The model contributes language understanding; your documents contribute the facts. That is why answers can cite sources and why they stay current as your content changes.
RAG for knowledge that changes or requires citation, which is most business use cases. Fine-tuning for teaching a model a style, format or specialised task. They are not alternatives so much as different tools, and they are frequently combined. If someone recommends fine-tuning for a knowledge problem, ask how they will handle a document being updated.
Well-built systems constrain answers to retrieved content, cite sources, and refuse when the corpus lacks the answer. Fabrication risk is real but manageable, and it is measurable — our evaluation harness tests refusal behaviour explicitly. Any vendor claiming zero hallucination is overselling.
As current as your sync. Incremental ingestion typically runs hourly or daily; real-time is possible where the source supports it. Stale content is the most common cause of trust loss, so sync design gets real attention.
Yes, and it must. Retrieval is filtered by the requesting user's entitlements before generation, so a user cannot receive an answer derived from a document they cannot open. We integrate with your existing identity and permission model.
Then the system will reflect that, and the assessment in week one will tell you so directly. Sometimes the right recommendation is to fix a subset of content first. That is a cheaper conversation now than after launch.
Text formats are straightforward. PDFs vary enormously — a text-native PDF is easy, a scanned one needs OCR, and a scanned one full of tables needs real work. We assess your actual files rather than assuming.
Measured retrieval accuracy, answer accuracy, and citation correctness against the evaluation set, plus in-product feedback and query logs showing what people ask and where the system fails. You get numbers, not impressions.
Related