From enterprise knowledge search to multi-model conversational agents to full orchestration — AI integration at every scale, running on infrastructure you control.
A unified AI agent platform that combines semantic knowledge search, multi-model conversational agents, and self-hosted orchestration. Built on OpenClaw, Copilot Studio, and Azure AI Search — accessible via Slack, Telegram, or any custom interface.
Claude, GPT-4, Grok, Gemini, local models
Slack, Telegram, web chat, HTTP API
Full stack on a single Linux server
Most AI deployments solve one problem: a chatbot here, a search tool there, a workflow automation somewhere else. The team was juggling multiple disconnected AI tools — each with its own account, context window, and limitation. Knowledge was scattered, agents were siloed, and there was no unified platform to tie everything together. The AI Agent Platform consolidates knowledge search, conversational agents, and orchestration into one self-hosted system the team actually controls.
This page is built to read like a guided walkthrough: each block shows the business reason, the system move, and the operational implication. Future case pages should follow the same spine.
Operationally, this is a Microsoft-native decision pipeline. Each stage is small enough to inspect, yet together they turn a mailbox into a governed queue.
SharePoint libraries, file shares, and internal wikis are crawled on a schedule. Documents are chunked, embedded, and stored in Azure AI Search with metadata preserved. New and updated files are picked up automatically.
Incoming messages from Slack, Telegram, or web chat hit the OpenClaw gateway. The gateway loads session context, selects the appropriate model, and builds the request — including relevant knowledge from the search index when applicable.
The selected model processes the full context: conversation history, knowledge retrieval results, and the user's message. For knowledge queries, the response includes source citations. For conversational tasks, the response reflects the ongoing session context.
The formatted response is delivered back through the originating channel — Slack message, Telegram reply, or web chat bubble. Session memory is updated. Cron-triggered results are delivered to configured channels on schedule.
The point is not tool worship. The point is to use what the business already has, then make it behave like a coherent system instead of a collection of tabs.
The orchestration layer: session management, model routing, cron scheduling, memory, and the skill plugin system. Runs as a systemd service on any Linux VPS.
Semantic document index for the knowledge layer. Handles chunking, embedding, and relevance-ranked retrieval across all connected document sources.
Optional generation layer for knowledge-grounded responses within the Microsoft ecosystem. Connects to Azure AI Search for retrieval-augmented generation.
First-class messaging channels. The same agent is available in both, with separate session context per channel. No separate bot deployment required.
Claude, GPT-4, Grok, Gemini, and local models all available. Model selection per agent or per request. Session history transfers across model switches.
This showcase represents the full breadth of SoKKoS AI capabilities: knowledge search, multi-model agents, and self-hosted orchestration working as a single integrated system rather than separate products.
Support inboxes, sales qualification, service requests, approvals, or mixed internal mailboxes — the same design principle applies: classify intent, route with confidence, and keep exceptions visible.