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BigTree108

AI knowledge search and RAG development services

AI knowledge search and RAG development services: retrieval-augmented generation that answers questions from your documents, wikis, tickets, contracts and databases with citations, enterprise AI search with each user’s permissions applied, knowledge assistants for support, sales and operations teams, and fixes for RAG systems that give wrong answers, on Azure AI Search, PostgreSQL with pgvector, Elasticsearch, OpenSearch, Qdrant, Pinecone or Weaviate, with dedicated RAG developers for your team or a project delivered end to end.

RAG systems we build

  • Knowledge assistants

    Answers for employees or customers drawn from your help centre, policies, product documentation and past tickets, with the source of every statement linked and a plain statement when the sources do not answer.

  • Enterprise AI search

    One search across SharePoint, Confluence, Google Drive, Notion, Jira and your databases, with hybrid keyword and vector retrieval, re-ranking, and each user’s access rights checked at query time.

  • Document ingestion

    PDFs, scans, slides, spreadsheets and web pages parsed with their layout, tables and images kept, split into sections a reader would recognise, and re-indexed automatically when a source changes.

  • Retrieval tuning

    Hybrid search, re-ranking, query rewriting, metadata filters and contextual chunking tuned on your own questions, and graph-based retrieval where answers depend on relationships between records.

  • Answers from structured data

    Questions answered from SQL databases, spreadsheets and APIs through read-only queries the model writes and code checks, combined with text retrieval where an answer needs both.

  • Agentic RAG

    Assistants that plan several searches, compare sources and ask a follow-up question when the first answer falls short, for research, due diligence and support cases that span many documents.

  • Fixing RAG that gives wrong answers

    An existing RAG system measured on a set of real questions, failures traced to parsing, chunking, retrieval or the prompt, and each fix proven by the score rather than by a demo.

  • Evaluation and monitoring

    Retrieval and answer quality scored with Ragas or DeepEval on every change, user feedback captured, and unanswered questions reported so your content team knows what to write next.

  • Permissions and data protection

    Document permissions enforced at retrieval rather than in the prompt, personal data redacted where the model does not need it, and indexes kept in your own cloud account.

Hire RAG developers

  • Dedicated RAG developers

    RAG developers who join your team full time, work in your tools and process, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.

  • AI knowledge search projects

    A defined piece of AI knowledge search with a scope, a fixed plan and a named lead on our side who owns the result and reports progress in your channels.

  • Ongoing AI knowledge search

    AI knowledge search as a continuing service: the same people every month, a backlog you prioritise, and hours you can see in our portal and on the invoice.

The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.

Who works on your AI knowledge search

  • RAG and AI developers

    Retrieval, prompts and evaluation

  • Data engineers

    Ingestion pipelines and connectors

  • Search engineers

    Elasticsearch, OpenSearch and vector databases

  • .NET, Python and front-end engineers

    The application and the interface

  • QA engineers

    Evaluation sets and regression tests

Answers you can check

A RAG system is only as good as its retrieval. We start from real questions your users ask and the answers your experts expect, measure retrieval and answers separately, and change parsing, chunking, search or prompts only when the score shows the change helps.

Every answer cites the passages it came from, so a user can check it in one click, and the assistant says so when your sources do not cover a question instead of filling the gap. Those questions become a list of content to write.

Other AI development services we provide

AI development overview

Questions about AI knowledge search and RAG

How do you stop it from making things up?

By grounding answers in retrieved sources and showing the citations, instructing the model to say when the sources do not answer the question, and keeping unanswerable questions in the evaluation set. That reduces invented answers and measures what is left; no current model removes them entirely.

Which vector database do you use?

Often one you already run: PostgreSQL with pgvector, Elasticsearch, OpenSearch or Azure AI Search handle most document collections. Qdrant, Pinecone or Weaviate suit larger scale or a managed vector service. We choose by your volumes, latency and where the data may live.

Can users get answers only from documents they are allowed to read?

Yes. Permissions from SharePoint, Google Drive, Confluence or your own system are stored with each indexed passage and checked at retrieval for the person asking, so a document someone cannot open never reaches the model on their behalf.

Our RAG chatbot gives wrong answers. Can you fix it?

Yes. We build an evaluation set from real questions, measure where the answers fail and trace each failure to parsing, chunking, retrieval or the prompt. Fixes go in order of impact, and each one is proven by the score.

How quickly can RAG developers start?

When the right RAG developer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.

How do we hire RAG developers through BigTree108?

Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.

Who owns the work they produce?

You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.

Want answers from your own documents?

Tell us where the documents live, who will ask and what they ask about. You get an answer within one business day: a plan, candidate profiles, or a review of the system you already have.

Email hello@bigtree108.com How we respond