Ask your data and get answers

AI that answers questions from your own documents and data, with clean pipelines, dashboards and strict control over who sees what.

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RAG & Data Management

What this is, in plain words

Your business knows a lot, but it is spread across PDFs, drives, emails and old systems. Finding the right answer means asking the one person who remembers. RAG, retrieval augmented generation, changes that: AI finds the right passages in your files and answers with sources.

Good answers need good data. So we also build the pipelines that collect, clean and organise your data, the dashboards that show it, and the permissions that keep each team to what they are allowed to see.

What you get

Built for results you can see

  • Answers in seconds

    Staff ask a question in plain words and get an answer with its source.

  • Knowledge that stays

    What your experts know is captured, even when people move on.

  • Clean, reliable data

    Duplicates and errors get fixed before they reach reports or AI.

  • Safe by design

    People only see answers from documents they are allowed to read.

What we build

Inside our RAG & Data work

  • RAG over your documents

    01

    AI search and answers across PDFs, docs, sheets and web pages, with citations.

  • Knowledge bases

    02

    An internal help centre your team and AI can both use.

  • Vector databases

    03

    Fast semantic search set up and tuned for your content.

  • Data pipelines

    04

    Scheduled imports from your apps, drives and databases.

  • Data cleaning

    05

    Dedupe, standardise and validate records before they are used.

  • Dashboards and access control

    06

    Clear reports, with role based access down to the document.

How we deliver

Five steps, no surprises

  1. 1

    Data audit

    We list your sources, their quality and who should see what.

  2. 2

    Pipeline and cleaning

    We bring the data together and clean it.

  3. 3

    Index and retrieval

    We chunk, embed and index content, then tune search on real questions.

  4. 4

    Answer quality checks

    Your team tests real questions and we fix weak answers.

  5. 5

    Launch and keep fresh

    We go live with automatic syncs so new documents are picked up.

A first RAG assistant over a defined set of documents typically ships in 3 to 6 weeks, and larger data projects in 6 to 12 weeks.

Tools and deliverables

What we use, what you keep

Tech we work with

  • Python
  • TypeScript
  • PostgreSQL
  • pgvector
  • Qdrant
  • Pinecone
  • OpenAI API
  • Claude API
  • LlamaIndex
  • LangChain
  • Airbyte
  • dbt
  • Metabase

Handed over to you

  • AI question and answer tool over your documents
  • Vector index with scheduled syncs
  • Data pipelines and cleaning jobs
  • Dashboards for key metrics
  • Role based access rules
  • Answer quality test set
  • Source code and documentation

Where it fits

Industries we build this for

FAQ

RAG & Data: common questions

Will the AI make things up?

RAG answers from your documents and shows its sources. When the answer is not in your data, we set it up to say so.

Is our data used to train public AI models?

No. We use providers and settings that do not train on your data, and can run models on your own servers if needed.

Can different teams see different documents?

Yes. Access rules follow your roles, so answers only come from documents that person is allowed to read.

Who owns the system and the data?

You do. Your data was always yours, and after final payment the code and indexes are too, unless we agree otherwise.

Where is it hosted?

On your cloud account, ours or on premise. We also offer support to keep syncs running and answers accurate.

Related services

Often paired with

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Or email sukhdev.narang@viralwave.marketing