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Fragmented knowledge, endless on-call - an AI agent that follows your team.

Graph N-hop Agentic RAG connects documents, Slack, S3, and Drive for instant answers. A hybrid engine routes repeated work to deterministic skills and novel requests to autonomous execution.

Overview

IT and software orgs scatter knowledge across wikis, Slack, Drive, and DBs, while on-call, incident response, and onboarding all rely on tribal knowledge. Existing search tools cannot cover multiple sources together, and most AI assistants stay stuck at simple Q&A.

AICLUDE links heterogeneous sources via Graph N-hop Agentic RAG and learns repeating org work patterns through a hybrid engine (autonomous + workflow). Teams reach the AI assistant from inside the tools they already use - Slack bot, web widget, API, MCP, and 9+ channels in total.

Key Capabilities

Graph N-hop Agentic RAG (multi-source)

3 data connectors (S3, Google Drive, DB) plus Graph N-hop Agentic RAG structure internal documents.

Hybrid execution engine

Routine work runs verified deterministic skills; new requests fall back to autonomous LLM execution. The hybrid pipeline chooses per situation.

Slack bot + Web widget + API + MCP

Reach the AI from the tools your team already lives in. MCP (tool discovery) supported too.

5 LLM Router + Auto Failover

OpenAI, Anthropic, Google, Bedrock, xAI. Circuit Breaker (auto fault isolation) fails over on vendor outages.

Today Dashboard

Auto-curated daily briefing for team and individual.

Case Stories

Self-contained Application Scenarios

Every case is shown in full: Pain, AICLUDE Apply, Scenario, Impact, and Tech: without collapse.

Case 01

Unified internal knowledge search AI

Customer Pain

  • Internal docs scattered across Drive, S3, DBs, and Slack - people switch between tools.
  • New hires burn time asking "where is this?"
  • Existing search tools do keyword matching - they miss context.

AICLUDE Apply

  1. 1Auto-link heterogeneous sources via 3 data connectors (S3, Google Drive, DB).
  2. 2Graph N-hop Agentic RAG splits documents at the proposition level.
  3. 3Vector search + 8-stage Fortress Pipeline validation.
  4. 4Ask in natural language from Slack bot or web widget for instant answers.
  5. 5Audit Log captures who accessed what data.

Scenario

Show Me
Input · Engineer (Slack)
"Where is last quarter's release deploy guide?"
  1. 01Retrieve
    Slack bot runs proposition RAG search
  2. 02Step
    Queries Drive, internal wiki, and DB in parallel
  3. 03Step
    Summarises 3 relevant docs + source links
  4. 04Validate
    Fortress Pipeline 8 stages filter hallucinations
  5. 05Audit
    Reply (logged in Audit Log)

Impact

  • Less switching between search tools.
  • Faster new-hire onboarding.
  • Higher org-knowledge utilisation.

Tech

  • Graph N-hop Agentic RAG
  • Slack Bot
  • Fortress Pipeline 8 stages
  • Audit Log
Case 02

Issue triage & prioritisation AI

Customer Pain

  • Issues, VoC, and customer inquiries are labelled and assigned manually.
  • Hard to leverage past resolution history per issue type.
  • Duplicate tickets pile up on similar issues.

AICLUDE Apply

  1. 1Learn org issue history via Graph N-hop Agentic RAG.
  2. 2On new issues, the LLM auto-classifies type, suggests an owner, and decides priority.
  3. 3Past similar issues auto-attached → duplicates surfaced.
  4. 4Hybrid execution engine handles repeating types via deterministic skills instantly.
  5. 5Function Calling integrates internal systems (issue tracker DB, etc.).

Scenario

Show Me
Input
New issue submitted
  1. 01Execute
    CLU agent triggered
  2. 02Retrieve
    Graph N-hop Agentic RAG retrieves past similar issues
  3. 03Step
    Suggests type, priority, and owner
  4. 04Step
    Decides whether it's a duplicate
  5. 05Execute
    Function Calling updates the ticket DB
  6. 06Step
    Slack notification to the owner

Impact

  • Faster issue triage.
  • Fewer duplicate tickets.
  • More accurate owner assignment.

Tech

  • Graph N-hop Agentic RAG
  • Hybrid Execution
  • Function Calling
  • Slack Bot
Case 03

Developer onboarding copilot

Customer Pain

  • New devs spend too long learning the codebase, architecture, and engineering culture.
  • Senior interruptions - the same questions come up over and over.
  • Team docs go stale and drift from the actual code.

AICLUDE Apply

  1. 1Learn internal architecture docs, coding conventions, and API specs via Graph N-hop Agentic RAG.
  2. 2New devs ask in natural language → relevant docs and example code surface instantly.
  3. 3The hybrid engine auto-promotes repeating question patterns into skills.
  4. 4Reach it from IDE and terminal via Slack bot, web widget, and MCP.

Scenario

Show Me
Input · New hire
"How does our team's auth flow work?"
  1. 01Retrieve
    CLU runs Graph N-hop Agentic RAG over internal docs and code
  2. 02Step
    Surfaces architecture diagrams and example code
  3. 03Validate
    Fortress Pipeline validates and cites sources
  4. 04Step
    Next similar question is answered instantly by a deterministic skill

Impact

  • Faster onboarding.
  • Fewer senior interruptions.
  • Org knowledge accumulates automatically.

Tech

  • Graph N-hop Agentic RAG
  • Hybrid Execution
  • Slack & MCP Omnichannel

Apply AICLUDE to this industry

We shape each PoC around your data, security requirements, and operating flow.