NISUVMarketing

AI & RAG systems

Answers built from your own data, not a guessing model.

Retrieval-augmented AI that actually knows your business — grounded in your documents, with answers that point back to the source.

Every answer is traceable to a source, and tested before it reaches a customer.

Your documentsYour knowledgeDocuments and dataDocsDataChunksRetrieval & modelFind, then answerEmbedAIRetrieveAnswers & appsWhere people use itChatbotEvalsAPI</>Your data → answersGroundedCitedFastSecure

What's included

Four workstreams, each with a clear scope, so you always know what is being done and why.

Support and internal-search chatbots

Assistants that answer customer or staff questions from your real policies, product docs and past tickets — and hand over to a person when they are not sure.

  • Answers cite the document they came from
  • Clear fallback and human hand-off
  • Tone and boundaries set to your brand

Document and knowledge-base retrieval

Ingestion pipelines that turn PDFs, wikis, sheets and web pages into a searchable knowledge base, kept up to date as your content changes.

  • Smart chunking and metadata for better recall
  • Vector and keyword hybrid search
  • Scheduled re-indexing of changed content

Custom prompt and evaluation pipelines

Prompts and test sets designed together, so quality is measured before launch and does not silently regress later.

  • Question sets built from your real queries
  • Automated checks for accuracy and hallucination
  • Versioned prompts you can review

Integration into your existing product or site

Delivered as an embeddable widget, an API or an internal tool — wired into the systems you already use.

  • Website, app, Slack or helpdesk integration
  • Authentication and access controls
  • Usage and cost monitoring

How we build a RAG system

  1. 1

    Scope the use case

    We identify the questions that matter, the sources that hold the answers and what a good answer looks like.

  2. 2

    Prepare your data

    Content is cleaned, structured and indexed, with permissions respected so people only see what they should.

  3. 3

    Build and evaluate

    We build the retrieval and generation pipeline and test it against a question set until quality meets the bar we agreed.

  4. 4

    Launch and monitor

    Roll-out is staged, with logging and feedback loops so the system keeps improving after go-live.

What you get

A working system plus the evidence that it works — not a demo that falls over on real questions.

  • Use-case and data-source plan
  • Ingestion and indexing pipeline
  • Chat interface or API, ready to embed
  • Evaluation set and quality report
  • Access controls and logging
  • Hand-over documentation

Is this the right fit?

We would rather tell you early than take on work we cannot do well.

●A good fit if

  • You have documents, FAQs or data people keep asking about
  • Accuracy and traceability matter to you
  • You want AI that fits into an existing workflow

●Probably not yet if

  • You have little written knowledge to draw on yet
  • You want a generic chatbot with no data behind it
  • You cannot share any internal content with an AI system

Common questions

What is RAG?

Retrieval-augmented generation. The system first retrieves relevant passages from your content, then asks the AI model to answer using only that material — which cuts down on made-up answers.

Is our data safe?

We design around your requirements: access controls, scoped data and choice of model provider. We will explain exactly where data goes before you commit.

How do you stop wrong answers?

Grounding, citations, an evaluation set and a clear 'I don't know' behaviour. No system is perfect, so we also monitor real usage after launch.

Can it work with our current tools?

Usually yes. We integrate with websites, apps, helpdesks and internal tools through widgets or APIs.

Ready to talk about aI & RAG systems?

Tell us what you are trying to achieve and we will reply with honest next steps and a clear quote.

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