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 We do not publish rate cards because scope varies with source-system maturity, but you will never start a phase without a fixed price for it.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.

编排能在真实 B2B 系统中报价、定价、预订并完成交易的 AI 智能体。

一个编排主干，每个后端用相同的模块模式接入。

大多数 AI 供应商只演示却从不交付的五套生产级系统。

AI 智能体编排主干

作为 MCP 模块的系统连接器

知识图谱推理

数据管道

AI 智能体 RFQ 到 B2B 工作流

每个连接器都长得一样——这是有意为之。

一个范围明确的项目通常能交付什么。

行业知识，结构化以实现即时支持。

行业知识模型

支持智能体上下文层

减少升级

图谱持续充实

一个四步构建过程，交付的是代码而不是黑盒。

发现

MCP 模块 + 领域模型

智能体 + 编排

加固 + 切换

开源、开放权重、与模型无关——这是设计原则。

技术买家会问的问题。

告诉我们您正在把哪些系统拼在一起。