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DreamzTech designs and develops production-ready Retrieval-Augmented Generation systems that securely connect large language models with enterprise documents, databases, knowledge bases, APIs and internal systems. We build custom RAG pipelines, vector search architecture, retrieval evaluation, permission-aware access, grounding, monitoring and production deployment. Answer quality in a retrieval system is decided by retrieval, not by the model — which is why most disappointing RAG projects are retrieval problems wearing a model costume.
RAG development — retrieval-augmented generation — builds the pipeline that finds relevant content from your own sources at request time and supplies it to a language model as context, so the answer reflects your approved information and can cite where it came from. The work covers ingestion, chunking, embeddings, hybrid search, reranking, permission-aware filtering, context assembly, citation and retrieval evaluation.
It is the grounding layer, not the whole application. Where the product around it also needs building — interface, conversation, workflow — that is LLM development. Where the question is whether a retrieval approach fits at all, start with generative AI consulting.
Connectors to SharePoint, document management systems, wikis, databases and file shares, with parsing that preserves structure: tables as tables, headings as hierarchy, and scanned material through OCR before it enters the index.
Chunk boundaries designed per document type, with metadata captured at index time — source, owner, effective date, version, entitlement tags — because most retrieval improvements come from filtering on metadata rather than from better embeddings.
Vector similarity combined with keyword search, because pure semantic search misses exact identifiers, part numbers and policy codes. A reranking pass then orders candidates by actual relevance to the question rather than by embedding distance.
Entitlements applied inside the query rather than as a post-filter, with the identity model mapped from your source systems through AI integration services. Post-filtering leaks through ranking and snippets.
Deciding what actually goes into the request: deduplication, ordering, budget management and conflict handling when two documents disagree. Citations resolve to a specific passage a user can open and check, not just a filename.
A labelled question set with known correct sources, scored for recall, precision and groundedness, run on every change. Without this, RAG tuning is guesswork and every change is an argument about anecdotes.
The rest of what a retrieval build covers. Most of the engineering effort sits before the model call, not after it.
Choosing and configuring the store — pgvector, Pinecone, Weaviate, OpenSearch or Elasticsearch — with index type, dimensionality, filtering strategy and sharding chosen against corpus size, query volume and how often content changes.
Change detection, incremental reindexing, deletion propagation and version handling, so a withdrawn document stops being cited. Built on data engineering pipelines where sources are numerous.
Rewriting vague questions, expanding acronyms, decomposing multi-part queries and routing to the right corpus. Users ask short ambiguous questions; the retrieval layer has to make them answerable.
Checking that an answer is supported by what was retrieved, and designing the system to say it does not know rather than filling a gap. A confident answer with no supporting passage is the failure mode that destroys user trust fastest.
Enterprise content is not plain prose. Tables, diagrams, scanned forms and slide decks need extraction and representation strategies of their own, often with generative AI development for the interpretation layer.
Query patterns, retrieval hit rates, unanswered questions, citation click-through and evaluation scores over time. Retrieval quality degrades as content grows and changes, and nobody notices until users quietly stop asking.
Six stages. The evaluation set is built before the pipeline, which is the opposite of how most RAG projects run and the main reason ours can be tuned on evidence.
What users actually ask, and what condition the source content is in: duplicates, outdated versions, contradictions, ownership and access rules. Both sides have to be understood before any pipeline is designed.
A labelled set of questions with known correct source passages. Built before the pipeline so every later decision is measured rather than debated.
Parsing that keeps tables, headings and clause structure intact, chunking designed per document type, and metadata captured at index time for later filtering.
Hybrid search, metadata filtering, reranking and context assembly, tuned against the evaluation set. Generation is only addressed once retrieval hits its targets.
Prompting for grounded answers with citations that resolve to a specific passage, plus refusal behaviour when retrieval returns nothing adequate.
Query patterns, unanswered questions, retrieval scores and citation usage tracked over time, with reindexing and re-evaluation as the corpus changes.
The failures are consistent and they are almost never about the model. These four decide whether a retrieval system produces answers people trust.
RAG work usually arrives from one of these four situations, and the second is by far the most common.
The assistant sounds confident and nobody can tell where an answer came from, so people stopped trusting it.
Quality collapsed at ten thousand. Naive chunking and single-vector search do not survive real corpus size.
Retrieval works but cannot respect who is allowed to see what, so it cannot go beyond a pilot group.
Duplicates, outdated versions and contradictory documents, with no clear owner. This is normal, and it has to be handled in the retrieval design rather than fixed first.
Grouped by the kind of knowledge being retrieved. These are patterns we build, not outcome claims.
The most common enterprise RAG use case and the one where entitlements matter most. HR, IT and policy content is exactly the material people are not uniformly allowed to see, so access design leads the build.
Agent assist and self-service grounded in product documentation and past resolutions. Retrieval must distinguish between product versions, which fixed chunking routinely destroys.
Manuals, specifications, runbooks and code documentation, where exact identifiers and part numbers matter and pure semantic search reliably fails without a lexical component.
Clause-level retrieval across agreements, where chunking must respect clause boundaries and citations must resolve precisely enough for someone to rely on them.
Where an answer must be traceable to an approved, current source and refusal is preferable to a guess. Freshness and withdrawal handling carry more weight than raw retrieval quality.
Agents need context assembled at each step rather than one retrieval per conversation, under the entitlements of the requesting user. Built alongside AI agent development.
Real DreamzTech AI engagements, chosen to show the integration, governance and production complexity behind systems that people actually use every day.
A multi-agent system automating prior-authorisation intake, payer-rule checking and submission, with human review retained where decisions require it. Relevant here because payer rules are exactly the kind of changing, authority-bearing content that has to be retrieved and cited rather than memorised by a model.
A custom enterprise CRM for a 120-rep sales organisation combining AI-enabled workflows, predictive analytics and automation. An example of retrieval operating inside existing role-based permissions rather than over a flattened copy of the data.
A multilingual AI support platform for a global courier spanning voice and text across several channels, integrated with shipment tracking and ticket workflows. Demonstrates knowledge retrieval combined with live system data in the same response.
Where the content lives, roughly how much there is, and who is allowed to see which parts.
We assess content condition, collect representative questions and build the labelled evaluation set before designing the pipeline.
Retrieval tuned against the evaluation set until it hits target, then grounded generation with citations on top.
Two pipelines, not one: indexing runs on a schedule, retrieval runs per request. Most designs that struggle have conflated them.
SharePoint, wikis, document stores, databases and file shares, with change detection built in.
Structure-preserving extraction so tables stay tables and headings stay hierarchy.
Boundaries designed per document type rather than a fixed token count.
Source, owner, date, version and access tags captured at index time for filtering.
Embeddings alongside lexical search, because exact identifiers defeat pure semantic matching.
Rewriting, expansion, decomposition and routing to the right corpus.
Reordering candidates by true relevance rather than embedding distance.
Applied inside the query, so restricted content never reaches ranking or snippets.
Deduplication, ordering, budget management and conflict handling before the model call.
References that open the exact passage, not just the document.
Labelled questions scored for recall, precision and groundedness on every change.
Incremental updates, deletion propagation and version handling as content changes.
Three ways to work with us, depending on whether you need a partner to own delivery, a managed team alongside your product organization, or specific expertise added to engineers you already have.
fix this before prompts
enforced in the query
stale answers cite confidently
The useful starting inputs are where the content sits, what people ask of it today, and who is allowed to see which parts.
Share your sources and the questions people ask. We will come back with a retrieval design, an evaluation approach and an honest view of your content condition. Free initial consultation, NDA available.
Store and framework choices follow corpus size, query volume, filtering needs and where the data is allowed to live.
| Layer | What we use |
|---|---|
| Vector stores | pgvector, Pinecone, Weaviate, Qdrant, OpenSearch and Elasticsearch |
| Keyword & hybrid | BM25, OpenSearch and Elasticsearch hybrid scoring |
| Embeddings | OpenAI, Cohere, open-weight embedding models and domain-adapted alternatives |
| Reranking | Cross-encoder rerankers and hosted reranking services |
| Orchestration | LangChain, LlamaIndex, LangGraph and custom pipelines |
| Models | OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral and open-weight models |
| Sources | SharePoint, Confluence, Google Drive, S3, document management systems, databases and web content |
| Data platforms | Snowflake, Databricks, BigQuery, Redshift, PostgreSQL and document stores |
| Deployment | Public cloud, private cloud, VPC/VNet isolation and on-premise where content cannot leave |
What changes by sector is the document type, how strict the entitlement model is, and how costly a stale or uncited answer would be.
Protocols and payer rules change and carry authority, so effective-date handling and mandatory citation lead the design.
Regulatory and product documentation must be current and traceable, with retrieval auditable per request.
Manuals and specifications are full of exact identifiers, which defeats semantic search without a lexical component.
Operational knowledge sits across procedures, carrier rules and customer agreements in different formats.
Product and policy content changes constantly, so freshness matters more than corpus size.
Technicians need asset-specific answers offline, which changes both retrieval scope and delivery.
Multi-property operations mean the same question needs different answers depending on site.
Specifications and drawings carry contractual weight, so citations must resolve precisely.
Three ways to get your information in front of a model. They solve different problems and are routinely confused during scoping.
| RAG this page | Fine-tuning | Long context | |
|---|---|---|---|
| Best for | Information that changes, and answers that must cite a source | Teaching a repeatable format, style or task behaviour | Small, stable corpora that fit in one request |
| Updating content | Reindex the changed documents | Retrain | Resend everything each time |
| Citations | Native to the design | Not available | Possible but imprecise |
| Permissions | Enforceable per user at query time | Baked in, not per user | All or nothing per request |
| Cost driver | Indexing and retrieval infrastructure | Training runs and data preparation | Tokens per request, which grows fast |
For enterprise knowledge, RAG is almost always the correct starting point because content changes and answers must be traceable. Fine-tuning addresses behaviour, not knowledge, and the two are complementary rather than alternatives. Long context is convenient for small corpora and becomes expensive quickly. Generative AI consulting can settle which applies before you commit.
What buyers ask when scoping a retrieval build: why answers go wrong, how permissions work, chunking, freshness, cost and how RAG compares with fine-tuning.
RAG development — retrieval-augmented generation — builds the pipeline that finds relevant content from your own sources at request time and supplies it to a language model as context, so answers reflect your approved information and can cite where they came from. The work covers ingestion, chunking, embeddings, hybrid search, reranking, permission-aware filtering, context assembly, citation and retrieval evaluation.
Almost always because retrieval failed, not because the model did. If the correct passage was never retrieved, no amount of prompt engineering produces a correct answer. The usual causes are chunking that destroyed document structure, vector-only search missing exact identifiers, no reranking pass, and no labelled evaluation set to detect any of it. Fix retrieval before touching prompts.
RAG supplies information at request time; fine-tuning changes model behaviour through training. RAG suits knowledge that changes and answers that must cite a source, and content is updated by reindexing rather than retraining. Fine-tuning suits repeatable formats, styles and task behaviour. They address different problems and are complementary rather than alternatives.
For most enterprise cases, yes. Long context works for small, stable corpora that fit in a single request, but cost scales with every token sent on every call, and it offers no per-user permission enforcement — it is all or nothing. RAG retrieves only what is relevant, enforces entitlements at query time and keeps costs proportional to the answer rather than the corpus.
Entitlements are applied inside the retrieval query rather than filtering results afterwards. Post-filtering leaks information through ranking positions, snippets and citation counts even when documents are hidden. Access tags are captured at index time, the identity model is mapped from your source systems, and retrieval can be audited per user and per request.
Chunking splits documents into retrievable units. Fixed-size chunking is the default in most tutorials and it destroys tables, splits clauses mid-sentence and separates headings from the content beneath them. Chunk boundaries should be designed per document type, because a policy PDF, a support article and a spreadsheet export need entirely different treatment.
It depends on corpus size, query volume, filtering requirements and where the data is allowed to live. pgvector is often sufficient and avoids a new component if you already run PostgreSQL. Pinecone, Weaviate and Qdrant suit larger or more demanding workloads. OpenSearch and Elasticsearch are strong where hybrid keyword and vector search matters. The choice is rarely the limiting factor on quality.
With a labelled question set where the correct source passages are known, scored for retrieval recall and precision and for groundedness of the final answer. Build it before the pipeline, because without it every tuning decision becomes an argument about anecdotes. Track the same measures over time, since retrieval degrades as content grows and changes.
Change detection on the sources, incremental reindexing rather than full rebuilds, deletion propagation so withdrawn documents stop being retrievable, and version handling where several editions coexist. Stale retrieval is more damaging than missing retrieval, because the system cites an outdated document with full confidence.
Yes, with extra work. Tables need extraction that preserves structure rather than flattening into text. Scanned material needs OCR before indexing. Diagrams and slides often need a description pass so they are retrievable at all. Enterprise content is rarely plain prose, and pipelines that assume it is tend to underperform on exactly the documents that matter most.
The variables are the number and condition of the sources, the complexity of the permission model, and how messy the content is. Duplicates, outdated versions and contradictory documents are normal and are handled in retrieval design rather than fixed first. The content assessment stage exists to answer this, because estimates made before seeing the corpus are usually optimistic.
Cost comes from embedding and reindexing as content changes, vector store infrastructure, retrieval queries, and tokens for the context sent with each request. Context size is usually the largest variable and the easiest to reduce with better retrieval — precise retrieval sends less and costs less, so quality and cost improve together.
Yes. Vector stores, embedding models and language models can all run in your own cloud environment, an isolated VPC or on-premise. This is common where content cannot leave a controlled boundary. Open-weight embedding and language models make it practical, at the cost of higher infrastructure and operational effort.
This is normal in enterprise content and has to be designed for. Options include preferring the most recent effective date, preferring an authoritative source hierarchy, surfacing both passages with their dates and letting the user judge, or refusing and escalating. The right behaviour depends on the domain, and it is a decision to make deliberately rather than leave to the model.
Verified client feedback consistently highlights responsiveness, practical problem solving, communication and delivery quality.
If answers are wrong, unverifiable or quietly stale, the fix is almost always in retrieval rather than the model. Tell us where your content lives. NDA available • US-led project management • Private and on-premise options.
