Thinkiyo

RAG Knowledge & Search Systems

Retrieval-augmented generation over your private documents, cited answers, access control, and auto-refreshing indexes.

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Cited
Sources on every answer
Access-controlled
Per-user document visibility
Auto-indexed
Documents refresh automatically
Any source
Drive, Notion, SharePoint, email

Overview

Retrieval-augmented generation systems that let AI work over your private documents, contracts, SOPs, knowledge bases, and operational records. Turns years of accumulated institutional knowledge into something your team can actually query and trust.

What we build

Inside the RAG Knowledge Systems build

  • Multi-Source Document IngestionGoogle Drive, SharePoint, Notion, Confluence, email archives, PDFs, spreadsheets, and custom databases, all ingested into a unified index.
  • Content-Type-Tuned Chunking & EmbeddingChunking and embedding strategies tuned to your content type, legal contracts and product docs need different approaches, and we engineer that difference.
  • Cited-Source AnswersEvery response links back to the underlying document and page, preserving trust and audit-ability.
  • Access Control LayerUsers only see answers drawn from documents they are authorised to read, permissions enforced at query time, not just ingestion.
  • Automatic Index RefreshDocuments stay current as they change, with versioning and change tracking where required.
  • Existing Tool IntegrationDeployed into Slack, email, internal portals, and custom applications, wherever your team already works.

Sample workflow

How it runs in practice

01

Internal Knowledge Bot

Employee asks "what is our refund policy for enterprise customers?" in Slack. RAG system searches contract library, policy documents, and email archive. Returns cited answer in under 3 seconds, linking to the specific clause in the relevant document.

Stack

Tech we work with

PineconeClaudeOpenAILangChainPythonGoogle Drive APINotion APISharePoint API

Frequently asked about this build

How do you prevent hallucinations?

Retrieval-first architecture, the model only answers from retrieved context, not from general training. We also implement confidence thresholds and citation requirements that force grounded responses. If the answer isn't in the documents, the system says so.

What document formats are supported?

PDF, Word, Excel, PowerPoint, plain text, Markdown, Google Docs, Notion pages, Confluence pages, and structured data sources. We handle OCR for scanned documents.

How long does it take to set up?

A basic RAG system over a defined document corpus is typically a 2 to 3 week build. Complex access control, multi-source integration, and custom UI add time.

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Every build is shipped from scratch around your exact workflows and tools, and the source code is yours to keep.

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