AI inference cost dropped more than 280x in 24 months. The bottleneck is no longer the model, it is execution. Bunker structures data and AI governance applied to decisions in high-complexity B2B firms, before the endless pilot and before regulatory fines.
Data & AI by the numbers
of companies use AI in at least one function, only 6% capture EBIT impact above 5% attributable to AI: gap between adoption and value
McKinsey QuantumBlack / State of AI 2025of generative AI projects will be abandoned after proof of concept by the end of 2025: pilot purgatory without governance
Gartner Press Release jul/2024of IT leaders cite integration as a blocker to AI adoption. Average company runs 897 applications, only 29% integrated
MuleSoft / Salesforce Connectivity Benchmark 2024of Brazilian companies have mature AI governance, against 21% globally. 95% plan to adopt agentic AI within 2 years without governance defined
IBM Institute for Business Value / 5 Trends 2026The silent risk in enterprise AI
When data, model, and governance operate disconnected, each system produces a different version of the truth. The result is opinion-based decisions, agents without guardrails, and generative AI without traceability. Bunker structures data governance and AI governance within the same Protocol, before operations multiply the error.
The real scenario
Each one operates in silence. Together, they separate companies that scale AI with governance from companies that abandon pilots after six months of POC.
95% of Brazilian companies plan to adopt agentic AI within 2 years. Only 27% have mature governance. NIST AI RMF, ISO 42001, and EU AI Act become procurement prerequisites, not differentiators. Without defined governance, an agent becomes a regulatory liability.
Average company runs 897 applications with only 29% integrated. 95% of IT leaders say integration blocks AI adoption. Without contextual data, an agent decides on fragments; and fragments produce wrong answers in convincing tone.
MuleSoft / Salesforce Connectivity Benchmark 2024 ↗30% of generative AI projects will be abandoned after PoC by end of 2025. 40% of agentic AI projects will be cancelled by 2027. Common to all: started without decision architecture, without operating criteria, without outcome metrics. Beautiful pilot, zero production.
RAG systems in production hallucinate in 17 to 33% of queries, even when correct data is in the base. Without decision lineage, no one audits why the agent recommended X instead of Y. AI error without traceability is error that repeats at scale.
Stanford HAI / AI Index Report 2025 ↗AI operations do not fail for lack of platform. They fail because governance, modeling, predictability, and agents operate as four parallel projects. The Bunker Protocol connects these dimensions into a single architecture, with governed data at the source, models calibrated for real operation, and AI applied where it produces auditable outcome, not where it produces demo.
We do not sell dashboards, we do not sell models, we do not sell agents. We design the architecture that turns data and AI into auditable decisions.
Bunker Protocol applied to Data & AI
Transformation
Without Bunker
With Bunker
The first step is a Structural Diagnosis. No commitment, no generic PowerPoint. Assess whether your scenario justifies a different architecture before the EU AI Act enters into force on August 2, 2026.
What we do in Data and AI
Modeling
We build predictive segmentation models with AI that connect customer profile, purchasing behavior, and account potential, so every commercial decision starts from criterion, not volume.
Learn morePredictability
We implement demand forecasting and sales forecast models with AI, with governed assumptions, auditable scenarios, and measured MAPE, reducing the gap between projection and operation.
Learn moreAI Agents
We structure AI agents in B2B sales and operations with guardrails, human supervision, and Model Context Protocol for integration, where AI runs the repetitive work without losing decision traceability.
Learn moreGovernance
We structure data governance with origin, quality, lineage, and access, aligned with DAMA-DMBOK and LGPD, so every indicator and every AI decision has known and traceable provenance.
Learn moreFrequently asked questions
AI governance is the set of policies, controls, and metrics that define who decides with AI, who audits, and which limits operate. It differs from data governance in scope: data is raw material, AI is the decision layer. At Bunker, both are structured within the same Protocol, aligned with NIST AI RMF and ISO 42001.
Data governance handles the raw material: quality, lineage, access, LGPD compliance. AI governance is the upper layer: decision about when and where AI decides, with which guardrails, under which responsibility. One feeds the other. Without data governance, there is no reliable AI governance.
In four phases of the Bunker Protocol. Structural Diagnosis maps where AI is already in use (including shadow AI). Prioritization Architecture defines which cases pass, which do not, with which controls. Tailored Engagement installs guardrails, human supervision, and metrics. Outcomes and Transfer hands the method to the internal team to operate with autonomy.
Yes. The European regulation applies to any company whose AI system produces outputs used within the European Union, regardless of headquarters location. A Brazilian company selling SaaS to a European client, providing a model to a Portuguese fintech, or processing data of an EU citizen is in scope. Fines up to 35 million euros or 7% of global annual revenue. In force for high risk: August 2, 2026.
ISO/IEC 42001 is the first international standard for AI management systems, published in December 2023. Certification is becoming a procurement prerequisite in regulated and export-oriented companies. It works like ISO 27001 did for information security: without it, you lose the contract. Bunker structures the management system for certification within the Protocol, without proprietary tool lock-in.
An AI agent is a system that executes multi-step tasks autonomously, chooses tools and calls APIs, rather than just answering messages like a copilot. In B2B sales: lead triage, opportunity scoring, proposal generation, follow-up. In operations: order analysis, action recommendation. Bunker structures agents with guardrails, human supervision, and Model Context Protocol for integration.
Structural Diagnosis first. In 4 to 6 weeks, you leave with an inventory of AI in use (includes shadow AI), risk classification, and prioritized roadmap. Most of the value comes from what you decide to stop doing, not what you buy new. The Bunker Protocol delivers decision architecture before any platform investment.
