Question 1

How does the credit-based billing system handle model switching when different providers have different token costs?

Accepted Answer

MicrocosmWorks implemented a normalized credit system in NexumFive where each AI provider's token costs are mapped to a universal credit value maintained in the admin pricing configuration. When a user switches between OpenAI, Claude, Gemini, Grok, or Perplexity mid-conversation, the backend calculates credit consumption based on the per-model cost multiplier. The admin dashboard provides per-provider and per-model cost analytics for accurate margin tracking.

Question 2

How does the dual RAG architecture with FAISS and Milvus differ, and when does each store get used?

Accepted Answer

MicrocosmWorks designed the dual RAG system so local FAISS handles per-conversation and per-GPT knowledge with disk-backed indices for fast single-user retrieval, while the remote Milvus-backed microservice handles project-scale workloads requiring cross-document search across large file collections. Hybrid retrieval combines vector similarity, BM25 keyword matching, and recency scoring, with IndexTag-scoped search ensuring personalization within each user's data.

Question 3

What is GrouPT, and how does the multi-AI group discussion feature work technically?

Accepted Answer

GrouPT is MicrocosmWorks' implementation of structured multi-AI discussions where multiple AI models participate in a conversation with defined roles. Users configure debate, brainstorm, or analysis modes, and the system orchestrates sequential responses from selected models (OpenAI, Claude, Gemini, Grok, Perplexity) via streaming SSE. Each model receives the full conversation context plus its role instructions, enabling comparative analysis of model strengths on the same prompt.

Question 4

How does the Projects collaborative workspace ground documents and spreadsheets in RAG context?

Accepted Answer

MicrocosmWorks built the Projects workspace combining TipTap for document editing, Fortune Sheet for spreadsheets, and an artifact code editor. All project files are automatically indexed into the Milvus RAG layer via a BullMQ embedding pipeline that processes documents on save. When users query the AI within a project context, retrieval is scoped to that project's indexed files, grounding responses in the team's actual documents and data.

Question 5

What does the GPTs/Experts marketplace include, and how are custom GPTs built?

Accepted Answer

MicrocosmWorks shipped a full marketplace system in NexumFive with community-published GPTs (custom AI personas with specialized instructions and knowledge), Workflows (multi-step automated processes with autopilot execution), and Artifacts (reusable HTML/JSX components with live preview). Users build custom GPTs through a no-code builder that configures system prompts, attached knowledge files indexed via FAISS, available tools, and model preferences.

NexumFive: エンタープライズ向けマルチモデルAIチャットプラットフォーム

主な機能

開発ハイライト

技術スタック

私たちの貢献

プラットフォームアーキテクチャ

統合チャットレイヤー

アビリティシステム

Split ModeとGrouPT

成果物システム

ワークフローエンジン

プロジェクトワークスペース

デュアルRAGアーキテクチャ

ハイブリッド検索

管理者および請求

成果

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