Question 1

Are you a consultancy or a product company?

Accepted Answer

IdeaBosque is an AI platform and solutions company. We build, deploy, and operate AI agent applications for customers while providing the infrastructure, support, and ongoing enhancements required for production use. The same platform supports white-label and private deployment options when customers or partners need them.

Question 2

Do you run the agent in production for us?

Accepted Answer

Yes. IdeaBosque can operate the agent as a managed platform, or deploy it privately in your infrastructure when that is the better fit. The buyer choice is operational control: let us run it, run it yourself, or start managed and move private later.

Question 3

How do engagements work commercially?

Accepted Answer

Fixed-scope phases with weekly demos. A one-week Discovery produces the system inventory, workflow map, and build plan with full scope and cost before you commit to the build. Typical first builds run 5-8 weeks. We do not publish rate cards because scope varies with source-system maturity, but you will never start a phase without a fixed price for it.

Question 4

Do I have to use a proprietary frontier model?

Accepted Answer

Yes. Our platform supports leading proprietary frontier models, including enterprise AI services from major providers. We also support open-source and open-weight models, giving you the flexibility to choose the model strategy that best meets your business, security, performance, and cost requirements.

Question 5

Why the Model Context Protocol (MCP) instead of direct REST calls?

Accepted Answer

MCP gives every backend the same reviewed tool surface, so the agent's capabilities are easier to test, audit, and swap. Direct REST calls are fast for a prototype, but they usually become hard to govern once the second or third integration arrives.

Question 6

What if my system does not have an API?

Accepted Answer

If it has a stable programmatic surface, we can usually wrap it. We have patterns for GraphQL, REST, SDKs, database gateways, and controlled file-based exchange. If the only path is manual UI automation, we call that out as a risk before build.

Question 7

Is this only for retail, travel, or hospitality?

Accepted Answer

No. Travel and hospitality are strong proof cases because they combine constrained inventory, dates, occupancy, cancellation rules, FX, and supplier-specific pricing. The same RFQ-to-B2B pattern applies anywhere buyers, suppliers, quotes, approvals, capacity, and downstream transactions need to work across multiple systems.

Question 8

How does the knowledge graph help customer support?

Accepted Answer

The Neo4j knowledge graph stores industry taxonomies, product relationships, supplier mappings, and customer context. When a support inquiry arrives, the AI agent queries the graph to surface relevant context — order history, compatibility, substitutes, and pricing — automatically. Tier-1 questions can be answered autonomously; complex escalations carry full graph context to tier-2, reducing resolution time and escalation rates.

編排能在真實 B2B 系統中報價、定價、預訂並完成交易的 AI 代理。

一個編排主幹，每個後端以相同模組模式接入。

大多數 AI 供應商只展示卻從不交付的五套生產級系統。

AI 代理編排主幹

作為 MCP 模組的系統連接器

知識圖譜推理

資料管線

AI 代理 RFQ 到 B2B 工作流程

每個連接器都長得一樣——這是有意為之。

一個範圍明確的專案通常能交付什麼。

行業知識，結構化以實現即時支援。

行業知識模型

支援代理脈絡層

減少升級

圖譜持續充實

一個四步建構過程，交付的是程式碼而不是黑盒。

發掘

MCP 模組 + 領域模型

代理 + 編排

加固 + 切換

開源、開放權重、與模型無關——這是設計原則。

技術買家會問的問題。

告訴我們您正在把哪些系統拼在一起。