We connect to your systems, map how AI operates in your organization, assemble the right regulatory framework, configure your existing tools for governance, build what's missing, embed it in everyone's workflow, and transfer everything. You operate independently.
Start Your Governance JourneyThe Pattern We See Everywhere
Every enterprise we engage with faces the same structural challenges. These are not hypothetical scenarios.
Every leader recognizes that governance is needed — yet it consistently gets deferred. It sits on next year's roadmap, then the year after. It remains everyone's concern but rarely anyone's priority.
Hundreds of AI agents operate across credit, fraud, and customer service with no centralized registry, no risk classification, and no audit trail.
Teams select governance tools before defining what governance means. Months of evaluation, PoCs, and integrations get discarded. Framework first, then tools.
EU AI Act high-risk enforcement begins August 2, 2026, with fines up to 7% of global revenue. Many enterprises have no conformity assessment process defined.
Some of the most technically advanced organizations — mature data platforms, full observability, modern infrastructure — are still in the early stages of AI governance. When external scrutiny arrives (regulatory audit, IPO, investor due diligence), every governance gap becomes a material risk.
AI agents across CRM, observability, notebooks, and custom systems — often without a unified view of how many exist, what data they access, or who is responsible for them.
Market Analysis
Three types of players exist. None of them solve the real problem: the distance between "a framework exists" and "governance operates daily."
Platform Vendors
SaaS platforms with risk dashboards, automated bias testing, and pre-built policy templates. Some are Forrester and IDC recognized.
Where they stop
No dynamic multi-framework curation. Ship static policy packs. Only reach engineering teams. Create vendor lock-in that takes governance capability with it when the contract ends.
Consulting Firms
Major firms produce assessments and send experienced partners for the pitch, junior analysts for the work. They deliver a PDF.
Where they stop
Strategy and advisory, not engineering execution. No code, no tool configuration, no workflow embedding. The deliverable is a roadmap someone inside must then interpret, implement, and sustain.
Point Tools
Each solves its piece with depth and precision: model versions, fairness metrics, infrastructure monitoring, adversarial attacks.
Where they stop
Cannot connect outputs to governance frameworks. An observability tool detects latency degradation but cannot map it to an EU AI Act directive or Bacen revalidation requirement.
The Accelerator
A methodology-driven engagement that maps what exists, curates the right governance framework, configures your tools, builds what's missing, and transfers everything to you.
Artifact-first. Evidence-based.
Configure existing. Build missing.
Governance in the workflow.
Step by Step
An example of how the accelerator operates from first contact to full client independence.
We request read-only access to the systems where AI operates — source code repositories, data platforms, observability tools, agent platforms, workflow management, and LLM gateways. We consume existing APIs and logs. Nothing is installed on client infrastructure. Typical setup: 2-3 days.
Using an artifact-first methodology — reading code, configs, logs, and traces before conducting any interview — we build an evidence-based picture of how AI actually operates across the organization. We identify AI systems that may not be centrally tracked, map real data access patterns, and document the decision points where governance needs to be present.
AI specialist agents — each trained on a specific regulatory domain (such as EU AI Act, LGPD, Bacen, NIST, OWASP, ISO 42001, among others) — analyze the client's context and assemble a customized framework. A master orchestrator resolves conflicts between overlapping regulations. The output is a structured, machine-readable framework that the client validates and owns permanently.
Most enterprises already have tools that can serve governance purposes — they just aren't configured for it. Observability platforms gain governance context. Data catalogs become AI asset registries. Data access controls extend to cover agent behavior. Infrastructure-as-code templates incorporate governance modules. LLM gateways gain prompt monitoring. No new platforms to purchase.
Where gaps remain after configuring existing tools, purpose-built governance agents are designed and deployed — for example, an agent registry, a data segregation monitor, an LLM usage governance layer, or an evidence generation engine. Each agent is linked to a specific framework directive and deployed on the client's cloud infrastructure.
Lightweight interceptors are installed at the decision points identified during discovery — inside the tools each role already uses. A developer sees a governance check in their pull request. A product manager sees a risk classification when creating an AI feature. An operations lead gets a revalidation alert in their change management system. Leadership gets an auto-generated dashboard. The interface doesn't change — governance appears as context, not as a separate system.
At engagement end, the client receives all source code, interceptor configurations, the curated framework, the evidence system, operational runbooks, training materials, and a clear ownership matrix. The client's team is trained to operate, maintain, and extend everything independently. An optional regulatory update subscription is available as frameworks evolve — but the governance system works without it.
Three Moats
Three structural advantages that no platform vendor, consulting firm, or point tool replicates.
No platform assembles a customized regulatory mix dynamically. We curate across 57+ regulations using specialist agents that understand where NIST recommends but the EU AI Act mandates, where LGPD's right to explanation intersects with Bacen's model validation. The output is a framework you own, not a subscription you rent.
Governance platforms reach developers through CI/CD. We reach product managers through Jira, operations through ServiceNow, analysts through LLM gateways, business users through agent platforms, and leadership through auto-generated dashboards. Governance operates at the point of decision for every role.
Every line of code, every agent, every playbook, every configuration transfers to the client. You operate independently after the engagement. No subscription dependency, no vendor lock-in. In a market where lock-in is the business model, full IP transfer is what enterprises are asking for.
Regulatory Coverage
A continuously growing knowledge base, expanding with every client engagement and regulatory update.
L1
International Standards
ISO 42001, NIST AI RMF, OWASP LLM Top 10, OWASP Agentic Top 10, IEEE 7000, OECD AI Principles, PCI-DSS, Basel III/IV
L2
Country-Specific Regulations
Mandatory overlays based on operating geographies. LGPD, EU AI Act, GDPR, HIPAA, PIPEDA, LFPDPPP, and more per jurisdiction.
L3
Industry-Specific Requirements
Financial Services, Healthcare, Automotive, Telecom, Energy, Consumer & Retail, Real Estate, Manufacturing.
Americas
Europe (EU-wide)
Europe (National)
Africa
57+
Regulations Curated
12
Countries Covered
8
Industries Mapped
4
Continents
Real Impact
Technology descriptions are abstractions. What matters is what changes for each person who touches AI.
Data Analyst Using an LLM
Before
Broad LLM access without tracking or guardrails. Customer data may enter prompts without visibility or audit trail.
After
LLM gateway interceptor logs the interaction. Nudge: "Customer identifiers detected. Anonymization applied. Logged for audit." Workflow unchanged. Full evidence generated.
Data Scientist Deploying a Model
Before
Pushes a credit scoring model through CI/CD. Passes unit tests, integration tests, and benchmarks — but regulatory compliance is not part of the pipeline.
After
PR interceptor: "High-risk under EU AI Act Article 6 and Bacen. Conformity assessment required. Bias check scheduled. Model card auto-generating." Deploys in days, not months, with full evidence.
Compliance Officer
Before
AI incident occurs. Two weeks reconstructing what happened by interviewing people, pulling logs from six systems, assembling a timeline manually.
After
Opens dashboard. Agent, timestamp, risk classification visible. Evidence trail: model version, data inputs, governance checks, framework directive. Full trail to the regulator within one business day.
Leadership / Board
Before
Board asks "are we compliant?" CTO says "we're working on it" with no data. No visibility, no metrics, no evidence of progress.
After
Auto-generated dashboard: 47 AI systems governed, 3 high-risk with conformity assessments complete, coverage at 85% trending upward, zero unassessed high-risk systems. Operational maturity, not promises.
Who We Are
Engineers, PhDs, and former AI leaders from the world's largest enterprises. We built AI at scale, operated it under regulatory pressure, and now we help the best teams do the same — faster.
Part of Act Digital. Connected to MIT CSAIL and USP. Present across the Americas, Europe, and Africa. When the engagement ends, you own everything we built.
By Industry
Each industry has its own regulatory stack, its own AI patterns, and its own governance challenges. Here is how the accelerator adapts.
The Challenge
A digital bank in Brazil had democratized AI agents across credit, fraud, and customer service — every analyst with broad LLM access, hundreds of agents with no centralized registry, and data criticality not yet differentiated. Regulatory pressure from LGPD and the central bank was mounting, while governance remained a recognized but unaddressed priority.
The Approach
Staged engagement: established data criticality for the most impactful assets first, then layered AI governance on credit and fraud models — where regulatory exposure was highest. Curated a framework blending LGPD, central bank model risk requirements, NIST, and OWASP. Configured existing data mesh and observability tools for governance. Built LLM usage governance and evidence generation agents.
Key Regulations
LGPD · Bacen · NIST AI RMF · OWASP LLM + Agentic · PCI-DSS
The Challenge
An automotive OEM in Europe with AI systems across ADAS, connected vehicles, and manufacturing faced the EU AI Act high-risk deadline. ADAS systems are automatically classified high-risk under Annex III. The governance lead had reversed a previous team's approach — insisting on framework before tools — but the deadline was approaching with no risk assessment process, no conformity assessment workflow, and a primary data platform that resisted integration.
The Approach
Urgency-driven engagement: curated a framework combining EU AI Act (mandatory), NIST risk assessment, ISO 42001 certification path, and OWASP Agentic — bridging existing ISO 26262 ASIL classifications to EU AI Act risk tiers. Built risk classification and conformity assessment agents. Installed hard gates on ADAS deployments. Governance operates independently of the client's data platform — no integration dependency.
Key Regulations
EU AI Act · GDPR · ISO 26262 · UNECE WP.29 · ISO 42001 · OWASP Agentic
The Challenge
A card network in Brazil with mature technical infrastructure — serverless data lake, full observability, modern infrastructure-as-code — but governance at the earliest stages. Agents were proliferating across CRM automation, observability, data science, and custom builds with no unified view. The data team operated as the organization's "oracle." The company was preparing for an IPO, making governance a prerequisite for going public — especially around data segregation between banking partners and transactional data access controls.
The Approach
Three-front engagement: data ownership foundation, cross-platform agent governance, and IPO readiness evidence. Leveraged existing observability as a fast-track for discovery — all APIs were already cataloged. Curated a framework combining LGPD, payment regulations, PCI-DSS, securities requirements, and OWASP. Built a cross-platform agent registry, data segregation enforcement, and auto-generated evidence packages for auditors.
Key Regulations
LGPD · Bacen (Payments) · PCI-DSS · CVM/SEC · OWASP LLM + Agentic · NIST
The Scenario
A pharmaceutical company in Europe deploying AI for drug interaction analysis, clinical trial optimization, diagnostic support, and patient data processing. AI models that touch patient health data require clinical validation, continuous monitoring, and documentation that meets regulatory scrutiny — from FDA AI/ML guidance to EU MDR to HIPAA. The challenge: multiple regulatory frameworks overlap, each with different evidence requirements, and AI development moves faster than validation cycles.
The Approach
Framework curation combining FDA AI/ML predetermined change control, EU MDR/IVDR conformity, HIPAA documentation requirements, ANVISA medical device classification, and ICH clinical practice guidelines. Hard gates on clinical AI deployment. Auto-generated model cards meeting each regulator's evidence format. Bias detection integrated into model validation pipelines.
Key Regulations
HIPAA · FDA AI/ML · EU MDR/IVDR · ANVISA · ICH · GDPR · ISO 42001
The Scenario
An energy utility in the Americas using AI for grid management, demand forecasting, smart metering, and predictive maintenance on critical infrastructure. AI systems operating within SCADA and OT environments face cybersecurity requirements that traditional IT governance doesn't cover. Regulatory frameworks span grid security, environmental compliance, and functional safety — with different requirements for AI operating in operational technology versus information technology.
The Approach
Framework curation combining NERC CIP for critical infrastructure, IEC 62443 for OT cybersecurity, ANEEL energy regulations, ISO 27001 for security management, and NIST AI RMF for risk methodology. Governance interceptors adapted for OT environments where availability takes precedence over blocking. Evidence generation designed for both IT compliance and OT safety audits.
Key Regulations
NERC CIP · IEC 62443 · ANEEL · ISO 27001 · NIST AI RMF · EU AI Act
The Scenario
A telecom operator in Europe and Latin America deploying AI for network optimization, customer profiling, churn prediction, automated customer service, and fraud detection. AI models making real-time decisions about service quality, pricing, and customer interactions — under regulatory scrutiny around consumer rights, data protection, and network neutrality. Multiple jurisdictions with different telecom-specific regulations layered on top of general AI governance.
The Approach
Framework curation combining EU Electronic Communications Code, ANATEL regulations, FCC guidance, LGPD/GDPR for customer data, and OWASP for AI agent security. Governance interceptors on customer-facing AI (chatbots, recommendation engines) and network-level AI (optimization, resource allocation). Evidence generation designed for telecom regulatory submissions.
Key Regulations
EU ECC · ANATEL · FCC · LGPD/GDPR · OWASP LLM · NIST AI RMF
Ready?
Governance that operates. Framework you own. Zero lock-in. Real evidence for real regulators.