Agent infrastructure for B2B operations

Orchestrate AI agents that quote, price, reserve, and transact across real B2B systems.

Your buyers get accurate, reservable quotes in minutes instead of days. IdeaBosque connects AI agents to the systems you already run: ERP, CRM, ecommerce, supplier catalogs, booking, payments, and support knowledge. Every backend is exposed through governed Model Context Protocol (MCP) modules, so the workflow is reviewable, auditable, and production-ready from the first release.

Request a scoped build Read the engineering brief

One-week discovery. You get a system inventory, workflow map, and fixed scope whether or not you build with us.

First agent live in 5-8 weeksEvery tool call logged and auditableOwn the code or let us run itFrontier or open-weight models: your choice

01—System Map

One orchestration backbone, every backend wired through the same module pattern.

No ad-hoc glue. RFQ engines, supplier catalogs, CRM, ERP, ecommerce, booking, payments, and knowledge graphs are each wrapped in MCP modules with consistent tools, audit logging, and rate limits.

User Layer

Buyer / Operator

Agent Layer

AI Agent - Orchestration Backbone

Module Layer

MCP Modules

System Layer

RFQ Engine

Supplier Catalogs

ERP / CRM

Ecommerce

Booking Systems

Payments

Knowledge Graph Engine

Infrastructure

Data Pipeline (Dagster)

Audit Logs / Observability

The orchestration backbone uses governed MCP tools to discover catalog options, create buyer requests, price supplier quote lines, hold constrained inventory, apply discounts, lock FX, snapshot cancellation terms, and hand accepted quotes to order, booking, or payment systems. Every call is logged with request, response, latency, and outcome.

02—Capabilities

Five production-grade systems most AI vendors demo but never ship.

AI Agent Orchestration Backbone

Stop stitching point-to-point integrations that break when one system changes. IdeaBosque delivers a multi-step agent runtime that routes intent across Model Context Protocol (MCP) modules, manages workflow state, and enforces rate limits, structured errors, and audit logs from the start.

System Connectors as MCP Modules

Each connector is built once, tested, and reused - not rebuilt per project. Modules for HubSpot, NetSuite, BigCommerce, WooCommerce, Shopify, Brightpearl, ShipStation, Canto, ResolvePay, and similar systems ship with tests, rate limits, and Python 3.8+ compatibility.

Knowledge Graph Reasoning

Answer the questions your database cannot: substitutes, alternatives, compatibility, lead-time matches, and margin rules. Neo4j-backed product, catalog, supplier, customer, and industry knowledge graphs give the agent relationship context before it acts.

Data Pipelines

Your warehouse stays current without a data engineer babysitting loads. Dagster-orchestrated pipelines move source-of-truth data into S3, Redshift, and Athena with Hive-partitioned files, watermark-based incremental loads, and idempotent writes.

AI Agent RFQ-to-B2B Workflows

Quote in one governed flow what currently takes your team days of swivel-chair work: RFQ intake, catalog discovery, supplier quote generation, tiered pricing, availability holds, FX, cancellation snapshots, RMA processing, asset sync, and B2B order intake.

See the RFQ architecture

03—Engineering Standards

Every connector looks the same — on purpose.

“Every connector we ship looks the same.”

Uniform MCP_CONFIGURATION pattern — tools, resources, prompts declared the same way in every module
Python 3.8+ compatibility — modules run on legacy infra without runtime upgrades
httpx HTTP/2 clients with exponential backoff and pluggable rate limiters
Audit-first: every tool call logs request, response, latency, and outcome
PII at the boundary — customer data lives in source systems; the orchestration backbone never persists PII in cleartext
Knowledge graphs complement, not replace — Neo4j stores the static catalog graph; live state stays in the system of record

Read the MCP module code standard →

04—Representative Outcomes

What a scoped engagement typically delivers.

5-8 weeks

A scoped engagement typically puts the first agent in production in five to eight weeks.

from scoped brief to first production release

Typical engagement length for a focused MCP agent or RFQ workflow.

6-10

A first program commonly produces six to ten reusable MCP modules.

reusable MCP modules per program

Each connector ships with tests, rate limits, and auth; reused across projects.

30-60%

Shared MCP modules typically remove a third to two-thirds of integration glue code.

reduction in integration glue code

Replacing per-project bespoke clients with the shared MCP module pattern.

1 flow

One governed RFQ flow connects request intake, pricing, holds, approvals, and handoff.

from request intake to quote acceptance

A single governed path from catalog discovery through supplier pricing, approvals, inventory holds, and downstream handoff.

Representative targets, not guarantees. Actual outcomes depend on scope, source-system maturity, and your team's capacity to participate.

05—AI-Powered Knowledge Graph for Customer Support

Industry knowledge, structured for instant support.

The transaction graph also becomes a support graph: supplier relationships, product hierarchies, compatibility rules, pricing tiers, and cross-reference data are available to support agents and AI assistants at the moment an inquiry arrives.

Industry Knowledge Model

A graph model of your industry's products, suppliers, customers, taxonomies, and relationships - built from your source systems and enriched with external standards. It supports natural-language questions such as which suppliers carry an equivalent to part X with lead time under five days.

Support Agent Context Layer

When a customer inquiry arrives, the knowledge graph surfaces relevant context automatically - order history, product compatibility, substitute options, pricing rules, and supplier status - so support agents and AI assistants respond with accurate, relationship-aware answers instead of searching across disconnected systems.

Escalation Reduction

AI assistants query the graph to answer tier-1 questions autonomously - product specs, order status, substitute availability, and pricing tiers - deflecting routine inquiries before they reach a human. Complex cases escalate with full graph context attached, so tier-2 agents start informed.

Continuous Graph Enrichment

Dagster pipelines sync new products, suppliers, pricing changes, and customer interactions into the graph on a schedule. The knowledge graph stays current without manual maintenance, and every support interaction queries the latest state of your industry data.

06—How We Engage

A four-step build that hands over code, not a black box.

Discovery

Inventory of source systems, RFQ/business workflow map, MCP tool catalog draft, agent scope, success metrics.

1 week

MCP Modules + Domain Model

One connector per source system plus request, quote, quote item, supplier item, pricing, availability, and policy entities as needed.

2-3 weeks

Agent + Orchestration

AI agent orchestration backbone, system prompt, tool descriptions, session store, RFQ workflow actions, Dagster pipeline if data sync is involved.

2 weeks

Hardening + Cutover

Observability, rate limits, fallback paths, feature-flag rollout, operator runbook, quote/booking/order handoff checks.

1 week

07—Stack We Build On

Open-source, open-weight, and model-agnostic by design.

No proprietary lock-in. The orchestration backbone and Model Context Protocol (MCP) module pattern work with any model that can call tools reliably and follow structured instructions, whether open-weight or frontier.

RuntimeOpen-source / open-weight LLM

ProtocolMCP

GraphNeo4j

OrchestrationDagster

CloudAWS

LanguagePython

Transformdbt

WarehouseRedshift

08—FAQ

Questions we expect from technical buyers.

Are you a consultancy or a product company?

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.

Do you run the agent in production for us?

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.

How do engagements work commercially?

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.

Do I have to use a proprietary frontier model?

No. IdeaBosque is model-agnostic. We can integrate proprietary frontier models, open-weight models, or a mixed model strategy depending on your security, cost, latency, and accuracy requirements.

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

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.

What if my system does not have an API?

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.

Is this only for retail, travel, or hospitality?

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.

How does the knowledge graph help customer support?

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.

09 — Contact

Tell us what systems you're stitching together.

Send the systems involved, the workflow to automate, and the handoff you need. An engineer reads every brief and replies within two business days with a fit and risk assessment.

Send project brief