LLM and RAG

RAG Development Services and Custom LLM Applications

RAG development services build AI assistants that answer from your own documents and systems. Retrieval-augmented generation (RAG) finds the relevant content first, then has a language model write the answer from it, with sources. NextCTL builds RAG systems and custom LLM applications, including enterprise AI search, with access controls and answer-quality testing.

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  • Delaware registered
  • Dhaka delivery hub
  • Scope agreed in writing
  • Weekly demos during build
  • A named lead on every project
  • Human handoff on every AI agent
At a glance

At a glance

  • For: CTOs, product leaders and technology directors with large bodies of internal knowledge.

  • You get: an AI search or assistant application working over your real data, with sources shown.

  • Work done by: NextCTL engineers; evaluation of answer quality is part of every build.

  • Part of: AI development.

RAG

What RAG development is

Compare

RAG vs custom LLM development

Custom LLM development means adapting a language model to your needs. There are three common routes:

  • Prompting: write clear instructions and examples. The simplest route.

  • RAG: add your knowledge at question time. Best when answers depend on facts that change or must be traceable.

  • Fine-tuning: train a model on examples to change its style, format or task behaviour. Best for consistent behaviour, not for storing facts.

Most business cases start with RAG. Fine-tuning is considered only when testing shows it is worth the extra effort. We test the options on your examples before recommending one.

Who it is for

Who this is for

  • Companies whose staff lose time searching wikis, drives, tickets and email for answers.

  • Support and operations teams who need consistent answers from policy documents.

  • Product teams adding an "ask your data" feature to a product.

  • Legal, finance and technical teams working with large document sets.

Not a fit if the content is thin, out of date or has no owner. RAG shows the quality of your content. We will tell you what needs fixing first.

What we build

LLM applications and RAG systems we build

Quality and access

How we make RAG answers reliable and secure

  • Access control: the system only retrieves content the person asking is allowed to see, based on your existing permissions.

  • Source citations: answers link to the passages they came from.

  • Evaluation: a test set of real questions measures whether the right passages are found and whether the answer sticks to them.

  • Refusals: when the content does not contain the answer, the system says so and does not guess.

  • Freshness: new and changed documents are re-indexed on a schedule or on change.

  • Logging: questions, retrieved passages and answers are recorded for review.

These are engineering practices we build. They are not certifications, and we do not promise that errors never occur.

Deliverables

What you receive

  • A working RAG or LLM application connected to your content sources.

  • A content pipeline: ingestion, cleaning, indexing and refresh.

  • An evaluation set and results, which can be rerun after any change.

  • Access-control design and documentation of how permissions are applied.

  • Monitoring and logs for usage, quality and model-call volume.

  • A runbook for your team, including how to add new sources.

How it works

How a RAG project runs

  1. Step 1

    Choose the first use case and the content that answers it.

  2. Step 2

    Review the content: gaps, duplicates, formats and permissions.

  3. Step 3

    Build a prototype on a slice of the content and test it with real questions.

  4. Step 4

    Tune chunking, search and prompts using the evaluation results.

  5. Step 5

    Add connectors, access control, logging and an approval or fallback path.

  6. Step 6

    Launch to a pilot group, review the questions asked, and widen access.

How a RAG project runs

Technology

Technology we work with

  • Embedding models and vector databases for semantic search, combined with keyword search for exact terms.

  • Document parsing for PDFs, office files, web pages, tickets and email.

  • Re-ranking to put the best passages first.

  • Language models from hosted providers.

  • Evaluation tools for retrieval quality and answer groundedness.

  • Observability, tracing and cost tracking.

  • Connectors to common sources through APIs and MCP servers (see MCP server development).

What are RAG development services?

RAG development services design and build systems that answer questions from your own content. Retrieval-augmented generation searches your documents for relevant passages and has a language model write an answer based on them, with sources. The service covers content ingestion, indexing, retrieval, answer generation, access control and testing, so the result is accurate enough for daily use.

What is the difference between RAG and fine-tuning a custom LLM?

RAG adds your knowledge at question time, which suits facts that change or need citations. Fine-tuning trains a model on examples to change its behaviour, style or format, and is not a good way to store facts. Most business cases start with RAG, and we test both on your examples before advising.

What does LLM application development involve?

It involves choosing a model, designing prompts and workflows, connecting your data and tools, building the backend and interface, adding guardrails, and testing against real cases. After launch it includes monitoring, cost control and updates as models and content change. The aim is a reliable application, not a one-off demo.

How do you measure answer quality and reduce hallucinations?

We build a test set of real questions with known good answers, then measure whether the right passages are retrieved and whether the answer stays within them. We require citations, let the system decline when evidence is missing, and rerun the tests after every change. We also review live questions after launch.

What is enterprise AI search and how is it different from regular search?

Enterprise AI search understands the meaning of a question, not only its keywords, and searches across many internal sources at once. It returns a cited answer or ranked results, and respects each person's permissions. It works well when knowledge is scattered across tools, and it depends on the quality of the underlying content.

Who owns what you build?

Ownership of the code, prompts, configuration and documentation is confirmed in the project agreement. We document what we build, including the architecture, data flows and settings, so your team, or another team, can run and extend it.

RAG and LLM development questions

Independent check

Decide with evidence, not a pitch.

Open a neutral research prompt in the AI you already use. It asks where NextCTL fits, where a freelancer or an in house hire may be enough, and what to verify before you choose.

AI answers can be incomplete. Check nextctl.com/methodology before deciding.

Show us the content
people search for

Tell us where answers live today and who needs them. We will propose a first use case and how we would test it.

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