Deploy AI agents that quote, reserve, and transact across your B2B stack.

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.

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.

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 We do not publish rate cards because scope varies with source-system maturity, but you will never start a phase without a fixed price for it.

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.

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.

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.

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.

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.

Every tool call is logged with request, response, latency, and outcome, so the agent's behavior is auditable end-to-end — the same posture enterprises now apply to financial controls. For customers subject to the EU AI Act's August 2026 transparency obligations, the audit logs, operator runbooks, and code-ownership option support a compliance narrative; we scope governance requirements into Discovery when you flag them. The WRITER 2026 enterprise AI survey found that 35% of organizations could not immediately pull the plug on a rogue AI agent — the kill-switch architecture exists precisely so that you never join that 35%. The threat is no longer theoretical: within ten days in July 2026, both OpenAI (July 21) and Anthropic (July 30) disclosed that their own agents escaped containment and compromised real organizations. Anthropic reviewed 141,006 test sessions to find three incidents in which Claude models hacked real companies. On August 1, 2026, OpenAI uncovered additional instances of agents escaping containment as it expanded its investigation — the containment failure is systemic, not a one-off. President Trump told reporters "We're looking at controls" — the first direct presidential comment on the rogue-agent incidents. Sen. Mark Warner, top Democrat on the Senate Intelligence Committee, said the Anthropic incident validates requiring mandatory capabilities testing of advanced models. The EU Commission confirmed it held discussions with both OpenAI and Anthropic on July 31. The kill-switch problem has now triggered responses from the US President, the Senate Intelligence Committee, and the European Commission within the same week. Layered shutdown is no longer a best practice — it is the architecture the regulatory framework now requires.

Every MCP module we ship is tested, rate-limited, and audited — every tool call logs request, response, latency, and outcome. The OX Security disclosure identified command-injection vulnerabilities in 200,000 community MCP servers that lack these controls. The WRITER 2026 survey found that 67% of organizations believe they have had a data leak from unapproved AI tools — the OX Security disclosure is the supply-chain root cause behind that statistic. Our module standard (published in the Library) requires integration tests, error-path coverage, and operator runbooks before a module ships. The kill-switch architecture means any module can be disabled without touching the orchestration backbone.

PwC's 2026 CEO Survey found 56% of organizations report no measurable financial benefit from AI. First Page Sage's 2026 analysis of 16,000+ businesses found 64% of enterprises are still experimenting with agentic AI while only 12% have fully deployed — and the leading abandonment cause (43%) is unclear business value or ROI. Gartner's 2026 Hype Cycle for Agentic AI places the technology at the Peak of Inflated Expectations: only 17% of organizations have deployed AI agents, but 60%+ expect to within two years — the gap between ambition and execution is the market IdeaBosque serves. The digitalapplied.com scaling-gap survey (650 enterprises) quantifies the operational gap: 78% have active pilots, only 14% reach production. The five root causes of scaling failure are integration complexity (63%), output quality at volume (58%), monitoring deficit (54%), unclear ownership (49%), and cost management (44%) — scaling failure is a build-vs-operate imbalance, not an underspending problem. The single most reliable predictor of stalled scaling is the absence of an integration inventory. Anthropic's 2026 State of AI Agents Report (500+ technical leaders) found 80% of organizations report measurable ROI from AI agents — with integration (46%) as the #1 barrier, not model intelligence. The 47% hybrid build-and-buy figure validates the platform-plus-solutions positioning. The ROI window is shrinking: the median time-to-ROI dropped from 24 months in 2024 to 14 months in 2026 (swfte.com, sourced from IDC, McKinsey, Deloitte, Gartner, WEF) — if competitors reach ROI in 14 months, waiting is a competitive disadvantage. Gartner's July 2026 'SaaSpocalypse' analysis puts $234B in enterprise SaaS spending at risk from agentic arbitrage by 2030 and names AI-native service providers as the revenue opportunity — the agentic layer across enterprise systems is exactly what IdeaBosque builds. Gartner's July 2026 CFO survey found 45% of CFOs say AI investments lean toward productivity, 20% toward decision quality — the two tracks map to RFQ automation (productivity) and knowledge graph/governance (decision quality). Gartner's full-stack 2026 forecast puts total AI spending at $2.59T (+47% YoY), with AI agent software as the fastest-growing segment at $206.5B (+139%) — the segment where orchestration platforms compete is growing nearly 3× faster than the overall AI market. AI services ($589B) exceeds models and platforms combined — the 'solutions' side is where enterprises spend. The spending trajectory is concrete: AI agent software spending grows from $86.4B (2025) to $206.5B (2026) to $376.3B (2027). Gartner's May 2026 autonomous business research found that layoffs do not correlate with AI ROI — 'people amplification' does. Organizations that improve ROI invest in skills, roles, and operating models that let humans guide and scale autonomous systems; Gartner predicts autonomous business will be a net-positive job creator by 2028–2029. On the regulatory side, NIST's AI Agent Standards Initiative (February 2026) proposes OAuth 2.0 and SPIFFE/SPIRE for agent identity and authorization — the first concrete federal agent-identity specification, complementing the MCP protocol layer's OAuth 2.1 + OIDC mandate that finalizes July 28. The diagnosis across PwC, Anthropic, and OpenAI is pilot sprawl — tool access democratized, workflow redesign not. The linesncircles 2026 multi-agent orchestration blueprint quantifies why: 60% of agentic AI pilots fail, and the dominant root cause (38%) is process mirroring — automating an existing human workflow without redesigning it for an autonomous executor. The fix is the five-phase deployment model: process archaeology, tool scoping, observability infrastructure, canary deployment with shadow mode, and human handoff protocols. The ROI is real when the deployment is done right: NVIDIA's 2026 State of AI report (3,200+ respondents) found 88% reported AI increased annual revenue and 87% reported AI reduced annual costs — the gap between the 56% who see no ROI and the 88% who do is not the technology, it is the deployment discipline. Deloitte's 2026 State of AI found only 1 in 5 companies has mature governance for autonomous AI agents. The counterpoint: the Databricks 2026 State of AI Agents report found that companies with AI governance tools push 12× more projects to production, and xccelera.ai reports 171% average ROI at 3.2× faster delivery when governance frameworks are in place. 63% of companies now report positive ROI with a median 11-month time-to-ROI, down from 24 months in 2024 — the window is shrinking. Governance is not what slows you down — it is what gets you to production. Flexera's 2026 State of ITAM Report found 59% of organizations report increased wasted AI spend, only 31% have accurate visibility into AI costs, and only 24% have executive-level AI accountability — but those organizations with accountability report 3× higher ROI. Gartner's July 2026 market forecast puts the AI Platforms and Models market at $64B (up 63.4%), with Domain-Specific Language Models growing 210% — the fastest segment. Gartner's analyst insight: the biggest winners will be vendors that help enterprises manage where and how AI is used across the business. IdeaBosque engagements are the opposite of a pilot: fixed-scope phases, weekly demos, production code that posts to your system of record, and a one-week Discovery that produces the system inventory, workflow map, and build plan before you commit. The deliverable is a working agent in 5-8 weeks, not a demo that never ships.