NeoTek Solutions designs, builds and runs retrieval-augmented generation (RAG) systems that answer your people’s questions from your own content, with citations they can check. RAG is the architecture behind that: before the model replies, the system searches your documents and passes the most relevant passages into the prompt. Our Nashville team has built this pattern for document-heavy work in healthcare, financial services, manufacturing and the public sector, and this page shows how we put it together.


When to Use This Architecture

RAG fits when people need answers from a large, changing body of content. Common examples include policies, procedures, contracts, technical manuals, knowledge base articles and product documentation. It also fits when answers must show their sources, or when different users may see different documents.

RAG is usually a better first step than fine-tuning. Fine-tuning means further training a model on your examples, which changes its style or behavior but is a poor way to add facts. Read our comparison of RAG vs. fine-tuning for more detail. To compare eight RAG designs, from hybrid search to agentic RAG, read RAG architectures explained.

A simpler option is often better in some cases:

  • If users need exact lookups, such as an order status, a database query or standard search may be enough.
  • If the content is small and stable, a well-written FAQ page may serve users better.
  • If the task is structured extraction from forms, consider intelligent document processing.

Core Components

Ingestion Pipeline

Connectors pull content from sources such as SharePoint, file shares, wikis, ticketing systems and databases. The pipeline extracts text, cleans it and records metadata like title, owner, date and permissions. It runs on a schedule or on change events so the index stays current.

Chunking

We split long documents into smaller passages that fit in a prompt. We follow the document’s own structure, such as headings, sections and table boundaries, rather than cutting at a fixed length. Each chunk keeps a link back to its source document and location.

Embedding Model

We convert each chunk and each user question into an embedding, a list of numbers that represents the meaning of the text. Passages with similar meaning end up close together, even when they use different words. We pick the embedding model to suit your content and hosting requirements.

Search Index

We design the index around your permissions, storing chunks, embeddings, metadata and permission tags together. Vector search finds passages by meaning, while keyword search finds exact terms like your product codes and names. We run both and merge the results, which is more reliable than either alone.

Re-Ranker

We add a second model that scores the top search results against the question more carefully. It keeps the most useful passages and drops weak matches before anything reaches the prompt.

Orchestration and Prompt Assembly

We build the orchestration layer that receives the question, rewrites it for search where that helps and calls the index. It then assembles a prompt with instructions, the selected passages and any conversation history. We instruct the model to answer only from the supplied sources and to say when it does not know.

Large Language Model

The LLM reads the prompt and writes the answer. Model choice weighs quality, cost, speed, privacy and where the model can be hosted. Many teams route calls through an LLM gateway for central controls.

Citations and User Interface

We attach references to the source passages, with links back to the original documents. Your people can check the evidence themselves and flag poor answers for review.

Evaluation and Monitoring

We build an evaluation set of real questions with expected answers, agreed with your subject-matter experts. Our automated tests check retrieval quality, answer accuracy and whether answers stay faithful to the sources. After launch we monitor quality, usage, cost and user feedback.


How It Works

Retrieval-augmented generation (RAG) architectureApproved content is ingested, chunked with metadata and permissions, embedded and stored in a search index. When a signed-in user asks a question, the orchestrator checks identity, runs permission-filtered hybrid search, re-ranks the passages and builds a prompt, and the LLM returns an answer with citations. Questions, sources, answers and feedback are logged for evaluation and audit.INGESTIONSourcesApproved contentIngestionConnect + extractChunkingMetadata, permissionsEmbedding modelChunks to vectorsSearch indexVector + keyword123QUERY AND ANSWERSigned-in userChat, search or appOrchestratorIdentity, prompt buildHybrid searchFiltered by accessRe-rankerKeeps the top few4567LLMGenerates answerAnswer + citationsShown in chat or app8Evaluation and monitoringQuestions, sources, answers and feedback are logged for quality checks and audit9Logging and feedback1Step in How It Works
  1. Connectors copy new and changed content from approved sources into the ingestion pipeline.
  2. The pipeline extracts text, splits it into chunks and attaches metadata and permissions.
  3. The embedding model converts each chunk into a vector, and the index stores it.
  4. A signed-in user asks a question in a chat window, search page or business application.
  5. The orchestrator checks the user’s identity and group memberships.
  6. Hybrid search retrieves candidate passages, filtered to documents that user is allowed to see.
  7. The re-ranker scores the candidates and keeps the most relevant few.
  8. The orchestrator assembles a prompt with instructions and the selected passages.
  9. The LLM generates an answer, and the system attaches citations to the sources used.
  10. The question, sources, answer and feedback are logged for evaluation and audit.

Security, Governance and Guardrails

Access control. Retrieval must respect source permissions. We carry permission tags into the index and filter results by the user’s identity before the model sees any text. This prevents the AI from summarizing documents a user could not open directly.

Data protection. Content is encrypted in transit and at rest. Sensitive fields can be masked during ingestion or redacted at the gateway. We use enterprise or private model deployments where required, and your data is not used to train public models.

Prompt injection. Prompt injection is text hidden in content that tries to override the system’s instructions. We treat retrieved text as untrusted data, separate it clearly from instructions and test against known attack patterns.

Evaluation. Every change to prompts, chunking, models or sources runs against the evaluation set before release. This catches regressions early.

Monitoring and human oversight. Dashboards show answer quality, unanswered questions and cost. Content owners fix gaps in the source material. For high-stakes topics, answers can be routed to a person for review.


Reference Stack by Cloud

Component Microsoft Azure AWS Google Cloud
Content storage Azure Blob Storage Amazon S3 Cloud Storage
Text extraction Azure AI Document Intelligence Amazon Textract Document AI
Hybrid and vector search Azure AI Search Amazon OpenSearch Service Vertex AI Search
Embeddings and LLM Azure OpenAI Amazon Bedrock Vertex AI
Orchestration Azure Functions or Azure Container Apps AWS Lambda or Amazon ECS Cloud Run
Identity and access Microsoft Entra ID AWS IAM Cloud IAM
Monitoring Azure Monitor Amazon CloudWatch Cloud Monitoring

Open-source and on-premises options also fit, such as PostgreSQL with pgvector, OpenSearch, open-weight models and Kubernetes. NeoTek Solutions is vendor-neutral and chooses components based on your requirements.


Common Pitfalls

  • Indexing everything at once, including outdated and duplicate documents.
  • Ignoring source permissions, so users can retrieve content they should not see.
  • Using fixed-size chunks that cut tables and sections in half.
  • Relying on vector search alone, which can miss exact codes and names.
  • Skipping an evaluation set and judging quality by a few demo questions.
  • Treating launch as the finish line instead of monitoring and improving content.

Where We Apply It

In healthcare, RAG supports policy lookup, clinical operations procedures and payer rule questions, designed for HIPAA requirements. In financial services and insurance, it helps staff search underwriting guidelines, product documents and compliance procedures. In manufacturing, technicians use it to query equipment manuals and maintenance procedures. Detailed guides cover healthcare contact centers, financial policy questions and maintenance and SOP assistants.

Example scenario: A service desk team keeps answering the same questions from hundreds of procedure documents. A RAG assistant answers from approved procedures, cites the source page and flags gaps for content owners.


How Can NeoTek Solutions Help You Build It?

You do not have to assemble this architecture yourself. We deliver the whole system, or we join your team for the parts you want help with.

  • Start with a planIn a short assessment we review your content, users, permissions and the questions that matter, then recommend the simplest design that will work.
  • Prove it on your contentWe build a working assistant on a focused set of documents, scored against an evaluation set your subject-matter experts agree on.
  • Run it in productionWe add permission-aware retrieval, citations, monitoring, guardrails and support, in your cloud tenant or on-premises.
  • How we buildShort cycles with AI-assisted delivery and human review, an evaluation set from day one and working software you can try early, so scope and cost stay visible.
  • Skills on the teamAI and machine learning engineers, data engineers, cloud and security specialists and QA, so retrieval, pipelines, permissions and testing are covered by one team.
  • What makes us differentWe use AI in how we build, not only in what we build, we stay vendor-neutral across Azure, AWS, Google Cloud and open source, and you own what we deliver.

Tell us what your teams search for today. Book a free AI consultation and we will walk through your content, your constraints and a realistic first step.


Frequently Asked Questions

What do you actually build when you build RAG?

We connect a large language model to a search index of your own content. When your user asks a question, our system retrieves the relevant passages and adds them to the prompt. The model then writes an answer grounded in those passages, with citations your people can check.

Will this stop the AI making things up?

It reduces inaccurate answers, and we are honest that it does not remove them entirely. We work on retrieval quality, clear instructions, citations and an evaluation set we rerun on every change. For high-stakes topics we route answers to a person and instruct the model to say when the sources do not answer the question.

How do you make sure it finds part numbers as well as concepts?

We run vector search, which matches meaning, alongside keyword search, which matches exact terms. We merge the results and re-rank them. That way your people find both related passages and exact matches like part numbers or policy IDs.

How do you handle our document permissions?

We carry permission tags from the source into the index. At query time we filter results to documents the signed-in user is allowed to open. The model never receives text that user could not read directly, so the assistant cannot summarize what they cannot see.

Can you run this on-premises?

Yes. We build RAG on-premises or in a private cloud using open-source search, such as PostgreSQL with pgvector, and self-hosted open-weight models. We do this for clients with strict data residency or privacy requirements.


Plan Your RAG System

Tell us what content your teams search and who needs answers. We will help you design a secure, well-tested RAG solution. Learn more about our generative AI solutions or talk to an AI architect.

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