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Document archives feeding a lit retrieval index, the core of RAG development services

RAG Development Services

A model that answers from your own documents, retrieved fresh at every question, with the source shown next to the answer. That is retrieval augmented generation, and Hygge's RAG development services cover the retrieval layer, the chunking strategy, and the evaluation set that proves the answers are right before anyone trusts them.

What Custom RAG Development Services Cover

What decides whether retrieval returns the right passage or a plausible-looking wrong one.

Document Processing and Chunking

How your PDFs, wikis, tickets, and contracts get split before indexing. Chunk size and boundary rules decide more of the final answer quality than the model choice does, and they get tuned against your own documents.

Embedding and Vector Storage

Turning text into vectors and storing them where search stays fast as the corpus grows. Model choice here is a cost and accuracy trade-off measured at your volume.

Retrieval and Reranking

Hybrid search combining keyword and semantic matching, then a reranking pass that puts the passage answering the question first. This step is where retrieval quality is won.

Grounded Generation and Citations

The prompt architecture that keeps the model inside the retrieved passages, plus a citation next to every claim so a reader can check the source. RAG as a service without citations is a system nobody audits.

Evaluation and Accuracy Testing

A test set of real questions with known correct answers, run on every change, so retrieval accuracy comes back as a number. Most teams skip this and find out in production.

Freshness and Reindexing

How the index stays current as documents change, and what happens to an answer built on a policy that was updated yesterday.

What Gets Decided Before RAG Development Starts

Hygge opens with an audit of your document set. RAG projects fail on the corpus far more often than on the model.

The audit settles each of these:

  • Corpus scope, which documents are in, which are stale, and which contradict each other.
  • Chunking strategy, tuned against your real document structure before indexing starts.
  • Accuracy bar, the retrieval score the system has to hit before it goes in front of anyone.
  • Access control, whether an answer can quote a document the person asking is allowed to read.
  • Fixed scope and price for the build, agreed before development starts.
What Gets Decided Before RAG Development Starts

What Sends a Team to RAG

The situations behind most enquiries Hygge scopes.

All five come down to one thing: the model answers from what it remembers, and nobody can check it. RAG development changes where the answer comes from. The audit reads your document set first, measures what retrieval returns on real questions, and prices the build against that.

The Model Invents Answers

A general model answers confidently about your pricing, your policy, or your product, and gets it wrong. Grounding every answer in a retrieved passage is the fix, and it is the whole reason RAG exists.

Knowledge Buried in Documents Nobody Reads

Answers exist in a wiki, a contract set, or five years of support tickets, and finding them takes longer than asking a colleague. Knowledge base ai turns that archive into something answerable in a sentence.

Search That Matches Words, Not Meaning

Your existing search returns documents containing the query terms, so a question phrased differently from the document returns nothing. Enterprise search ai matches on meaning, which is what people expect now.

A Pilot Run Against Your Real Questions

The demo answered the questions it was built against. Then it met a question phrased sideways, a document with a table in it, and a policy that changed last month.

Answers That Cannot Be Checked

Without citations, nobody can verify an answer, so nobody in a regulated or high-stakes context is allowed to rely on it.

How We Build a RAG System

From a document audit to a system with a measured accuracy score, running on your own infrastructure.

  1. Document Audit

    Two weeks reviewing the corpus: formats, structure, contradictions, and how often it changes. Ends in a fixed scope and price.

  2. Evaluation Set First

    Before building, we write the test questions with your team and agree the correct answers. Every change after this gets measured against that set.

  3. Retrieval Build and Tuning

    Chunking, embedding, hybrid search, and reranking, tuned in cycles against the evaluation set until the score clears the bar agreed in the audit.

  4. Generation and Citations

    The answer layer, with a source link on every claim and a defined response for when retrieval finds nothing relevant.

  5. Deploy and Monitor

    Live in your infrastructure, with logging on low-confidence answers and a reindexing schedule matched to how fast your documents change.

What Changes Once Answers Come From Your Own Data

People get an answer with the source sitting next to it, in seconds. The archive nobody opened becomes something the whole company can ask questions of. A policy update reaches every answer the same day it is published, and a wrong reply is findable, because the passage behind it is on screen. Custom RAG development services start with the documents you already hold and the questions people already ask. What separates a build that survives from one that gets switched off is measurement: LLM evaluation against a set of real questions, with RAG evaluation metrics tracked release to release so a change that helps one answer and breaks four is caught before it ships.

What Changes Once Answers Come From Your Own Data

What RAG Development Gives Back

The standards this work is held to, set against your current baseline and checked after delivery.

90 %
Of answers grounded in your own documents with the source passage attached
5 x
Faster than a person searching the same document store
2 weeks
From your document store to grounded answers running in staging
100 %
Of your documents stay inside the environment they already sit in

Where Retrieval Beats a Bigger Model

Sectors where Hygge has grounded answers in a client's own documents.

All Hygge Industries
LegalTech

LegalTech

Answers grounded in the firm's own files, each one pointing back at the paragraph it came from so a reviewer can verify it.

See the work
EdTech

EdTech

Retrieval kept inside the course material, with a citation on every response so a learner can check what the platform told them.

See the work
Retail & E-Commerce

Retail & E-Commerce

Product and policy questions answered from your own catalogue and help centre, refreshed as SKUs and terms change.

See the work
Healthcare & Staffing

Healthcare & Staffing

Retrieval over clinical and policy documents inside your perimeter, with access rules applied to every lookup.

See the work

Retrieval Systems Hygge Has Shipped

Products where the answer had to come from the client's own documents, built as rag as a service projects Hygge still supports.

All Hygge Case Studies
Country Navigator
EdTech

Country Navigator

A decade-old monolith serving enterprise customers, rebuilt into services and given a production AI assistant, with the user base migrated without downtime.

  • 0Downtime moving a decade-old platform
  • 1 configReplaces the SSO setup only one person knew
  • 10 yearsOf monolith rebuilt into services
Read the case
Smarter Humans
EdTech

Smarter Humans

Seven years building an AI-powered learning platform, from the original spaced-repetition web app to production AI content generation and retrieval-augmented chat.

  • 93 %Cut off a twenty-second document load
  • 1 uploadReplaces the card writing people quit over
  • 7 yearsOn one product without a handover
Read the case

What You Get From the Document Audit

A corpus assessment with the problem documents named, a chunking recommendation tested on your files, a realistic accuracy expectation, and a fixed scope and price. The starting point for enterprise search ai or a grounded assistant.

The Stack Behind a RAG Build

Chosen per project against corpus size, freshness requirement, and where your data is allowed to live.

Where your content is indexed and how it is searched, sized for the corpus you hold and the query load a real user base creates.

PgvectorPgvector
QdrantQdrant
PineconePinecone
WeaviateWeaviate
ElasticsearchElasticsearch

Frequently Asked Questions

How retrieval is grounded, kept current, and measured against rag evaluation metrics before launch.

Question mark iconWhat is retrieval augmented generation?
Retrieval augmented generation is a pattern where the system fetches relevant documents at the moment of the question and hands them to the model as context, so the answer is grounded in that material and can cite it. The model itself stays untrained on your content. The documents live in your own store, which is why updating an answer means updating a document.
Question mark iconHow does RAG work?
Source documents are split into chunks and indexed, usually as embeddings in a vector store. A question is embedded the same way, the closest chunks are retrieved, and those chunks go to the model alongside the question. The model composes its answer from them. Quality is decided at retrieval: when the right chunk fails to come back, no model can recover the answer.
Question mark iconWhat is a RAG model?
The phrase is loose. There is no separate RAG model, only a standard language model used inside a retrieval pipeline. What varies between systems is the retrieval half: how documents are chunked, which embedding model indexes them, whether a reranker sorts the candidates, and how much context the model receives. Teams treating RAG as a model choice usually end up debugging retrieval.
Question mark iconWhat is RAG in AI?
RAG is how an AI system answers from material it was never trained on: your policies, your contracts, your product documentation. The material stays in your store, and access rules apply at retrieval time so a user only sees answers built from documents they are allowed to read. On Country Navigator each enterprise gets its own isolated knowledge base behind the same platform.
Question mark iconHow does retrieval augmented generation work in production?
Beyond the pipeline, production RAG needs three things a prototype skips: evaluation against a fixed question set so any change can be measured, freshness handling so an updated document reaches the index, and citation so a reader can check the source. Systems without evaluation drift quietly, since every change to chunking or prompting shifts answers nobody is measuring.
Question mark iconWhat is RAG and when do I need it?
Retrieval augmented generation means the model reads the relevant passages from your own documents at the moment of the question, then answers from them. You need it when answers have to come from content the model was never trained on, or from content that changes: pricing, policies, contracts, product documentation.
Question mark iconDo I need RAG or fine-tuning?
RAG fits when the answer must come from documents that change, and when a citation matters. Fine-tuning fits when you need a consistent tone, format, or a narrow task the model handles badly through prompting. Many production systems use both, and Hygge decides that in the audit against your own examples.
Question mark iconHow much do RAG development services cost?
Price follows corpus complexity more than corpus size. Clean, structured documents in one format cost a fraction of mixed PDFs with tables, scanned pages, and five years of contradictory versions. Hygge fixes the price after a two-week document audit.
Question mark iconHow accurate is RAG?
That depends on retrieval, and it is measurable. We build an evaluation set with your team before development, then report a retrieval score you can hold us to. Any team quoting an accuracy figure before seeing your documents is guessing.
Question mark iconCan the system show where an answer came from?
Yes, and it should. Every claim gets a citation to the source passage, so a reader can check it. In regulated work this is the difference between a system people are allowed to use and one they are not.
Question mark iconWhat happens when my documents change?
Reindexing runs on a schedule matched to how fast your content moves, from real-time for pricing to weekly for documentation. The audit sets that cadence, since it affects both cost and how stale an answer can get.
Question mark iconCan a RAG system respect who is allowed to see what?
Yes. Access control applies at retrieval, so the system only searches documents the person asking has permission to read. This has to be designed in from the start, because bolting it on later means reindexing everything.
Question mark iconWhat is a RAG service?
A RAG service connects a model to your own documents so answers come back with the source passage attached.

From a Document Archive to Answers With Sources

Tell us what your documents hold and what people keep asking. You get a corpus assessment, a scope, and a price, the same starting point behind every rag development services project at Hygge.

Tell Us Which Documents Hold the Answers

Tell Us Which Documents Hold the Answers

Share where your knowledge lives, who searches it, and the questions people give up on finding.

Get a First Consultation

Get a First Consultation

We review your document sources and access rules for anything that would change scope, cost, or timeline.

Receive a Detailed Proposal

Receive a Detailed Proposal

A scoped plan with the approach, timeline, and cost, built around your actual content and retrieval needs.

Scope this service

Tell us what the system has to do. You get the scope and the price before any code is written.