Turning Local Knowledge Into Instant Answers — Our (DAAC) Solution.

Overview

In most organizations, knowledge lives scattered — tucked away in Google Drive folders, internal wikis, PDFs, meeting notes, and legacy systems.
Finding the right piece of information often means endless searching, pinging teammates, or digging through old email threads.

At Toraflow, we built a custom Documents as a Chat (DAAC) automation — a private, secure AI system that allows teams to converse directly with their internal documentation. Imagine having ChatGPT trained exclusively on your company’s files — that’s DAAC.

The Challenge

Our client, a mid-sized tech company, had years of accumulated internal documentation:

• Technical manuals

• Client project files

• Wiki pages

• Legal and HR policies

• API specs and training notes

Despite having a well-structured folder system, employees struggled to find the right documents quickly.
Search engines couldn’t understand context, and even the wiki’s built-in search missed relevant results hidden in PDFs or nested pages.

Key pain points included:

• ⚙️ Manual searches wasting hours per week

• 🧩 Disconnected knowledge sources (Drive, Confluence, Local NAS)

• 🔒 Sensitivity concerns around using third-party cloud LLMs

• 💬 No centralized way to “chat” with their company’s own data

Our Solution: “Documents as a Chat” (DAAC)

We built a fully private, on-premise AI automation designed to transform local documents into an intelligent, chat-based knowledge assistant.

Core Components

1. Local Document Indexing

   • All documents (PDF, DOCX, MD, TXT, HTML) are automatically parsed, chunked, and embedded locally.

   • The system updates continuously — new files appear in search within minutes.

2. Vector Search Engine

   • Using Supabase Vector and OpenAI-compatible embeddings, the system retrieves semantically related data, not just keyword matches.

3. Private LLM Integration

   • The assistant uses locally hosted or proxied models (like Ollama, vLLM, or Claude/ChatGPT via API) ensuring total data control.

4. Natural Language Chat Interface

   • Employees can ask:

     “Summarize our 2023 compliance guidelines.”
     “Find all references to ‘ISO27001’ across policies.”
     “What’s the client escalation process for Tier 2?”

   • DAAC finds and synthesizes the answer instantly — with document citations.

5. Role-based Access

   • Integrated with internal auth (e.g., Google Workspace or Supabase Auth) to enforce permissions — users see only documents they’re allowed to access.
     
     Impact

     After implementation, the company reported:

     MetricBeforeAfter DAACAverage time to find a document~7 minutes< 10 secondsInternal Slack/Email knowledge requests60+ per day↓ by 70%Employee satisfaction6.2/109.1/10Knowledge access compliance riskMediumLow

     Beyond speed and productivity, DAAC redefined how employees interacted with internal knowledge — turning “Where was that document?” into a single chat message.

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     Technical Stack

     LayerTechnologyFrontendNext.js 15 + Tailwind + Chat UIBackendNode.js + Supabase + LangChainVector DBSupabase Vector / PostgreSQLEmbeddingsOpenAI / Local Llama2-EmbeddingsModel RuntimeOllama / vLLM / Cloud ProxyStorageLocal Drive, Google Drive, Notion, ConfluenceAuthSupabase Auth + Role MappingDeploymentDocker + Nginx + Cloudflare Tunnel

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     Security and Privacy

     We prioritized data sovereignty — the AI runs entirely in the client’s environment:

     • 🔐 No document leaves the local network

     • 🧩 Sensitive data is masked or filtered before embedding

     • 🧱 Audit logs record every query for compliance

     • ☁️ Optional hybrid mode for teams needing both local + cloud LLM power

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     The Results in Action

     A few real-world use cases from their team:

     • HR Manager: “Summarize the new vacation policy and compare it to 2022.”

     • DevOps Lead: “What’s the Kubernetes configuration standard for our production cluster?”

     • Sales Team: “Find all references to ‘client onboarding checklist’.”

     All answers appear within seconds — with reference links, context summaries, and source highlights.

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     Future Plans

     We’re now extending DAAC to support:

     • 🗂️ Live synchronization with Notion and Confluence

     • 🧩 Multi-agent workflows (e.g., Research Agent + Summarizer Agent)

     • 🧠 Personalized memory per user

     • 💬 Voice interface for instant Q&A via Slack or WhatsApp

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     Conclusion

     DAAC is more than a chatbot — it’s your company’s collective memory, automated.
     It connects fragmented knowledge, saves time, and gives your team the ability to think faster.

     If your company’s data is buried across folders, wikis, and PDFs —
     🚀 Let’s build your own “Documents as a Chat” system.

     👉 Contact Toraflow to schedule a demo.