The EU AI Act Kicks In August 2026: What SaaS Builders Need to Know

What the EU AI Act Actually Is (Without the Legal Waffle)

The EU AI Act (Regulation EU 2024/1689) is the world's first comprehensive legal framework for regulating artificial intelligence. It entered into force on 1 August 2024 and is being phased in over three years.

Think of it as GDPR for AI. Same extraterritorial reach (if your AI's output is used in the EU, you're in scope, regardless of where you're based). Same risk-based approach. Same eye-watering fines. But instead of regulating personal data, it regulates AI systems based on how much risk they pose to people's health, safety, and fundamental rights.

The key dates you need to care about:

2 February 2025 (already passed): Prohibited AI practices banned. AI literacy obligations applied.

2 August 2025 (already passed): General-purpose AI model obligations. Member States designated national competent authorities.

2 August 2026 (coming up fast): The big one. High-risk AI system requirements for Annex III systems. Transparency obligations under Article 50. Full enforcement framework activated. EU-level fines for GPAI providers. Every Member State must have at least one AI regulatory sandbox.

2 August 2027: High-risk AI systems embedded in regulated products (medical devices, machinery, toys, etc.). Deadline for existing GPAI models placed on the market before August 2025.

Now, the Digital Omnibus proposed by the Commission in November 2025 would push back the August 2026 high-risk deadline. If adopted, Annex III high-risk systems would get until December 2027 (or 6 months after the Commission confirms harmonised standards are ready, whichever comes first). Product-embedded high-risk systems would get until August 2028.

But the Digital Omnibus is still a proposal. It needs to pass through Parliament and Council. At time of writing (March 2026), it hasn't been adopted. And 127 civil society organisations have urged the Commission to halt the proposals entirely. So don't bet your compliance strategy on it.

The Four Risk Tiers: Where Does Your Product Sit?

The AI Act classifies every AI system into one of four risk tiers. Each tier has different obligations. Here's what they actually mean for SaaS builders.

Tier 1: Unacceptable Risk (Banned)

These AI systems are prohibited outright. You cannot build them, sell them, or deploy them in the EU. Full stop.

What's banned:

Subliminal manipulation: AI that changes someone's behaviour without them being aware of it, in a way that causes harm. Think dark patterns on steroids, systems designed to manipulate people into decisions they wouldn't otherwise make.

Exploitation of vulnerabilities: AI that targets people based on age, disability, or socioeconomic situation to manipulate their behaviour harmfully. For example, an AI toy that encourages children to do dangerous things.

Social scoring: Using AI to rate people based on their personal characteristics, social behaviour, or predicted behaviour in ways that lead to detrimental treatment. The classic dystopian scenario.

Predictive policing of individuals: AI that assesses a person's risk of committing a crime based solely on profiling or personality traits (with limited law enforcement exceptions).

Untargeted facial image scraping: Building facial recognition databases by scraping images from the internet or CCTV without consent.

Emotion recognition in workplaces and education: AI that detects emotions in workplace or educational settings (with limited exceptions for safety or medical reasons).

Biometric categorisation by sensitive attributes: Using AI to categorise people by race, political opinions, trade union membership, religious beliefs, or sexual orientation based on their biometric data.

Real-time remote biometric identification in public spaces: Facial recognition in public areas for law enforcement (with very narrow exceptions for terrorism, serious crime, and missing persons).

Fines for deploying prohibited systems: up to 35 million euros or 7% of global annual turnover.

What this means for SaaS builders: If anything in your product resembles these use cases, stop. Now. This isn't a compliance exercise; it's a hard ban. Most mainstream SaaS products won't have these issues, but pay attention to edge cases. If your marketing automation tool uses behavioural manipulation techniques that could be considered "subliminal", or if your HR SaaS uses emotion detection in interviews, you have a problem.

Tier 2: High Risk (Heavily Regulated)

This is where most of the regulation's teeth are. High-risk AI systems are allowed but must comply with extensive requirements before being placed on the market and throughout their lifecycle.

There are two pathways to high-risk classification:

Pathway 1: Safety components in regulated products (Annex I). If your AI system is a safety component of a product covered by existing EU product safety legislation (medical devices, toys, vehicles, machinery, lifts, radio equipment, aviation, marine) and that product requires a third-party conformity assessment, the AI component is high-risk. This applies from August 2027.

Pathway 2: Specific use cases listed in Annex III. This is the one most SaaS builders need to worry about. If your AI system falls into any of these eight categories, it's high-risk by default (from August 2026, or December 2027 if the Digital Omnibus passes):

1. Biometrics: Remote biometric identification, biometric categorisation by sensitive attributes, emotion recognition.

2. Critical infrastructure: AI managing safety components in digital infrastructure, road traffic, water, gas, heating, or electricity supply.

3. Education and vocational training: AI that determines access to education or vocational training, evaluates learning outcomes, assesses student performance, or monitors cheating during tests.

4. Employment and workers management: AI for recruitment, job application filtering, CV screening, candidate evaluation, performance monitoring, task allocation, or decisions about promotions, terminations, and work assignments.

5. Access to essential services: This is the big one for SaaS builders. It includes AI for evaluating creditworthiness or establishing credit scores (except fraud detection), risk assessment and pricing for life and health insurance, evaluating and classifying emergency calls, and dispatching emergency services.

6. Law enforcement: AI for assessing reoffending risk, profiling in criminal investigations, analysing evidence, and polygraph systems.

7. Migration, asylum, and border control: AI for examining visa and asylum applications, risk assessments, and document verification.

8. Administration of justice: AI for researching and interpreting facts and law, or for alternative dispute resolution.

Critical detail: if your AI system performs profiling of natural persons (automated processing of personal data to assess aspects of a person's life like work performance, economic situation, health, preferences, behaviour, or location) within any of these Annex III categories, it is always high-risk. No exceptions.

Let me translate that for specific SaaS products:

If you build fintech/lending SaaS that uses AI for credit scoring or loan eligibility: high-risk.

If you build HR/recruitment SaaS that uses AI to screen CVs, rank candidates, or assess performance: high-risk.

If you build edtech SaaS that uses AI to grade assessments or determine admissions: high-risk.

If you build insurance SaaS that uses AI for risk assessment or pricing on life and health policies: high-risk.

If you build marketing SaaS like GrowCentric.ai that optimises ad campaigns, does audience segmentation, and adjusts pricing: almost certainly NOT high-risk (more on this below).

If you build ecommerce platforms like my work on Auto-Prammer.at with Solidus, using AI for product recommendations, dynamic pricing, and cart recovery: almost certainly NOT high-risk (with important caveats).

What high-risk providers must do (Articles 9 to 15):

• Implement a continuous risk management system throughout the AI's lifecycle
• Ensure data governance (training/validation data must be relevant, representative, and as error-free as possible)
• Create and maintain detailed technical documentation (Annex IV)
• Build automatic event logging into the system
• Provide transparency and clear instructions to deployers
• Enable meaningful human oversight
• Ensure accuracy, robustness, and cybersecurity
• Conduct a conformity assessment before market placement
• Register in the EU database
• Affix CE marking
• Implement post-market monitoring
• Report serious incidents

Fines for non-compliance with high-risk obligations: up to 15 million euros or 3% of global annual turnover.

Tier 3: Limited Risk (Transparency Obligations)

This is where most SaaS products with AI features will land. Limited-risk systems have one primary obligation: transparency.

If your AI system:

• Interacts directly with people (chatbots, virtual assistants): you must inform users they're interacting with AI.
• Generates synthetic content (text, images, audio, video): the content must be marked in a machine-readable format that enables detection.
• Uses emotion recognition or biometric categorisation: you must inform the people being subjected to it.
• Generates deepfakes: must be clearly labelled as artificially generated or manipulated.

These transparency obligations apply from August 2, 2026. There's no Digital Omnibus delay for these.

The exemption for limited-risk classification under Annex III applies when your AI system does one of the following:

• Performs a narrow procedural task
• Improves the result of a previously completed human activity
• Detects decision-making patterns without replacing human assessment
• Performs a preparatory task for a human assessment

But here's the catch: even if your system meets one of these exemptions, if it profiles natural persons, it's automatically high-risk regardless.

Tier 4: Minimal Risk (Unregulated)

The vast majority of AI systems fall here. Spam filters, AI-enabled games, basic recommendation engines, inventory management, content optimisation. No mandatory obligations under the AI Act, though voluntary codes of conduct are encouraged.

No fines specific to minimal-risk systems, but you should still be aware of GDPR, the Cyber Resilience Act, and the NIS2 Directive if applicable.

Where GrowCentric.ai Falls (And Why I Did the Analysis)

Let me walk through how I classified my own product, because this is the exercise every SaaS builder needs to do.

GrowCentric.ai is a multi-agent marketing optimisation SaaS. It analyses marketing campaign performance, reallocates budget across channels, segments audiences, detects multi-agent conflicts between competing clients, and autonomously adjusts campaign parameters. As I described in my AI marketing automation post, it uses specialised agents (Analytics, Budget Allocation, Audience, Conflict Resolution) that coordinate through a shared event bus.

Step 1: Is anything prohibited? No. We don't do subliminal manipulation, social scoring, biometric identification, emotion recognition, or anything on the banned list. Our system optimises marketing campaigns, it doesn't manipulate individuals without their awareness.

Step 2: Does it fall under Annex III? Let me check each category:

• Biometrics? No.
• Critical infrastructure? No.
• Education? No.
• Employment? No. We don't screen candidates or evaluate workers.
• Access to essential services? This is the closest. We don't do credit scoring, insurance pricing, or emergency services. We optimise advertising spend and audience targeting. The question is whether marketing audience segmentation could constitute "profiling" that affects access to essential services.

The answer: no. GrowCentric segments audiences for advertising purposes. It doesn't determine whether someone gets a loan, insurance, healthcare, or government benefits. It decides which people see which adverts, and how much to bid for their attention. That's marketing, not gatekeeping to essential services.

However, this is where it gets nuanced. If a GrowCentric client were a financial services company using our audience segmentation to decide who receives loan offers, that specific use could arguably constitute profiling that affects access to financial services. In that case, the deployer (the financial services company) would have high-risk obligations, even if GrowCentric as the provider remains limited-risk.

This provider/deployer distinction is crucial for SaaS builders. You might build a general-purpose tool that's limited-risk, but your customers might deploy it in high-risk ways. You need to think about this in your terms of service, your documentation, and your instructions for use.

Step 3: Does it have transparency obligations? Yes. GrowCentric uses AI agents that interact with client dashboards and generate reports. We should disclose that these are AI-generated. Our system also generates synthetic content (ad copy variations, audience descriptions). These need to be detectable as AI-generated.

Step 4: What about profiling? GrowCentric profiles user behaviour for advertising segmentation (browsing patterns, purchase history, engagement signals). Under GDPR, this is already regulated. Under the AI Act, profiling within Annex III categories makes a system automatically high-risk. But marketing profiling for advertising purposes is not within Annex III categories. It's governed by GDPR and the ePrivacy Directive instead.

My classification: GrowCentric.ai is a limited-risk AI system with transparency obligations. It is not high-risk because its use cases (marketing campaign optimisation, advertising audience segmentation, budget allocation) don't fall within any Annex III category.

But I'm documenting this assessment formally, because Article 6(4) requires providers who believe their Annex III-adjacent system isn't high-risk to document that assessment before placing it on the market. I'd rather have the documentation and not need it than need it and not have it.

Where Auto-Prammer.at Falls

My ecommerce marketplace Auto-Prammer.at, built on Ruby on Rails and Solidus, uses several AI features that I've described across this blog series:

• Product recommendations (intelligent recommendation engine)
• Dynamic pricing (pricing engine with guardrails)
• Predictive churn detection (churn prediction agent)
• Cart recovery (intelligent cart recovery)
• Data quality auditing (data audit systems)

None of these fall within Annex III categories. Product recommendations are standard ecommerce functionality. Dynamic pricing adjusts product prices based on market conditions, not individual creditworthiness or insurance risk. Churn prediction flags at-risk customers for retention campaigns, it doesn't determine access to essential services. Cart recovery is marketing automation.

Classification: Minimal to limited risk. Transparency obligations apply where AI interacts with users (e.g., chatbot features must disclose they're AI). Dynamic pricing should be transparent to customers (which is also a GDPR requirement under automated decision-making).

But again, I document everything. The data quality audit systems, the pricing guardrails, the audit logging on every agent decision, these aren't just good engineering practice. They're exactly the kind of infrastructure that makes compliance straightforward if your classification ever gets challenged.

The Obligations That Apply to Everyone (Even Limited-Risk)

Regardless of your risk classification, several things apply from August 2026:

AI literacy (currently under Article 4, though the Digital Omnibus proposes shifting this to Member States): Ensure your staff and users understand the AI systems they're working with. Train your team on what the AI does, how it works, what its limitations are, and how to interpret its outputs.

Transparency for AI-generated content (Article 50): If your system generates synthetic text, images, audio, or video, it must be marked in a machine-readable format. If users interact with a chatbot, they must know it's AI. Deepfakes must be labelled.

Record-keeping for providers: Even minimal-risk providers should maintain records of their AI systems, training data, and intended purposes. Not because the Act requires it for minimal-risk, but because (a) your classification could be challenged, (b) other regulations (GDPR, CRA) require similar documentation, and (c) it's just good engineering practice.

What This Looks Like in Code: Compliance Architecture for Rails/Solidus

Let me get practical. If you're building SaaS on Ruby on Rails (as I do), here's what AI Act compliance looks like architecturally.

Audit Logging for Every AI Decision

This is non-negotiable for high-risk systems (Article 12 requires automatic event logging) and strongly recommended for everyone else. Here's the pattern I use across all my AI agent implementations:

module AIAuditLoggable
  extend ActiveSupport::Concern

  included do
    after_create :publish_audit_event
  end

  def record_ai_decision(agent:, input:, output:, confidence:, metadata: {})
    AIAuditLog.create!(
      agent_name: agent,
      input_data: sanitise_pii(input),
      output_data: output,
      confidence_score: confidence,
      model_version: current_model_version(agent),
      decision_timestamp: Time.current,
      human_override: false,
      override_reason: nil,
      explainability_trace: generate_explanation(agent, input, output),
      metadata: metadata.merge(
        request_id: Current.request_id,
        user_id: Current.user&.id,
        session_id: Current.session_id
      )
    )
  end

  def record_human_override(audit_log_id:, override_by:, reason:, new_output:)
    log = AIAuditLog.find(audit_log_id)
    log.update!(
      human_override: true,
      override_by: override_by,
      override_reason: reason,
      override_output: new_output,
      override_timestamp: Time.current
    )
    publish_override_event(log)
  end

  private

  def sanitise_pii(data)
    # Strip PII before logging to comply with data minimisation
    PIISanitiser.sanitise(data)
  end

  def generate_explanation(agent, input, output)
    ExplainabilityService.explain(
      agent: agent,
      input_features: input,
      output: output
    )
  end
end

Every AI decision gets logged with: what agent made it, what input it received, what output it produced, how confident it was, which model version was used, and a human-readable explanation of why it made that decision. Every human override is also logged.

This gives you traceability (Article 12), transparency (Article 13), human oversight evidence (Article 14), and GDPR compliance for automated decision-making (Article 22) all in one pattern.

Human Override Mechanism

Article 14 requires high-risk systems to be designed for effective human oversight. Even for limited-risk systems, this is good practice:

class TieredAutonomyController
  AUTONOMY_LEVELS = {
    observe_only: {
      description: 'AI observes and reports, no action taken',
      requires_approval: true,
      auto_execute: false
    },
    low_risk: {
      description: 'AI executes routine decisions within tight bounds',
      requires_approval: false,
      auto_execute: true,
      max_impact_euros: 100,
      max_affected_users: 10
    },
    medium_risk: {
      description: 'AI proposes, human approves before execution',
      requires_approval: true,
      auto_execute: false,
      approval_timeout: 4.hours
    },
    high_risk: {
      description: 'AI proposes, senior human approves',
      requires_approval: true,
      auto_execute: false,
      approval_roles: ['admin', 'compliance_officer'],
      approval_timeout: 24.hours
    }
  }.freeze

  def execute_decision(agent:, decision:, autonomy_level:)
    level = AUTONOMY_LEVELS[autonomy_level]

    if level[:auto_execute] && within_bounds?(decision, level)
      execute_and_log(agent, decision, autonomy_level)
    else
      queue_for_approval(agent, decision, level)
    end
  end

  def emergency_stop(agent:, reason:)
    # Any human can halt any AI agent at any time
    AgentManager.pause(agent)
    AIAuditLog.create!(
      agent_name: agent,
      event_type: 'emergency_stop',
      override_by: Current.user.id,
      override_reason: reason
    )
    AlertService.notify_team(
      "AI agent #{agent} halted by #{Current.user.name}: #{reason}"
    )
  end
end

This tiered autonomy pattern ensures that AI decisions with higher impact require higher levels of human approval. And anyone can hit the emergency stop at any time. I use this exact pattern in GrowCentric.ai's agent architecture and in every agent I build for client implementations.

Technical Documentation Generator

High-risk systems need Annex IV technical documentation. Even if you're limited-risk, having this ready protects you if your classification is challenged:

class AISystemDocumentationGenerator
  def generate_for(ai_system)
    {
      general_description: {
        intended_purpose: ai_system.intended_purpose,
        provider: ai_system.provider_details,
        version: ai_system.current_version,
        date: Date.current,
        interaction_with_other_systems: ai_system.integrations
      },
      risk_classification: {
        tier: ai_system.risk_tier,
        annex_iii_assessment: ai_system.annex_iii_assessment,
        profiling_assessment: ai_system.profiles_individuals?,
        classification_rationale: ai_system.classification_rationale,
        documented_by: ai_system.classification_documented_by,
        documented_at: ai_system.classification_documented_at
      },
      development_process: {
        design_specifications: ai_system.design_specs,
        training_methodology: ai_system.training_details,
        validation_methodology: ai_system.validation_details,
        testing_methodology: ai_system.testing_details,
        data_governance: ai_system.data_governance_policy
      },
      monitoring_and_oversight: {
        human_oversight_measures: ai_system.oversight_measures,
        post_market_monitoring_plan: ai_system.monitoring_plan,
        incident_reporting_procedure: ai_system.incident_procedure,
        audit_log_retention_policy: ai_system.log_retention_policy
      },
      accuracy_and_robustness: {
        performance_metrics: ai_system.latest_metrics,
        known_limitations: ai_system.known_limitations,
        cybersecurity_measures: ai_system.security_measures
      }
    }
  end
end

Transparency Disclosure

For Article 50 compliance, here's how I handle chatbot and AI-generated content disclosure in Rails:

module AITransparency
  # Middleware that adds AI disclosure headers to responses
  class DisclosureMiddleware
    def initialize(app)
      @app = app
    end

    def call(env)
      status, headers, response = @app.call(env)

      if env['ai.generated_content']
        headers['X-AI-Generated'] = 'true'
        headers['X-AI-Model'] = env['ai.model_identifier']
        headers['X-AI-Provider'] = 'GrowCentric.ai'
      end

      [status, headers, response]
    end
  end

  # Helper for views
  module ViewHelpers
    def ai_disclosure_banner
      content_tag(:div, class: 'ai-disclosure', role: 'status',
                        'aria-label': 'AI-generated content notice') do
        "This content was generated with AI assistance. "\
        "#{link_to 'Learn more', ai_transparency_path}"
      end
    end

    def chatbot_disclosure
      content_tag(:p, class: 'chatbot-disclosure') do
        'You are communicating with an AI assistant. '\
        'A human team member is available if you prefer.'
      end
    end
  end
end

The Provider vs. Deployer Distinction (And Why SaaS Builders Need to Care)

The AI Act distinguishes between providers (who develop AI systems) and deployers (who use them). As a SaaS builder, you're typically the provider. Your customers are the deployers.

This matters because:

As a provider, your obligations centre on building the system correctly: risk management, data governance, technical documentation, conformity assessment, transparency, logging, and instructions for use.

As a deployer, your customers' obligations centre on using the system correctly: following your instructions, implementing human oversight, ensuring input data relevance, monitoring operations, and reporting incidents.

But here's the trap for SaaS builders. If your customer uses your general-purpose marketing tool for a high-risk purpose (say, a bank uses your audience segmentation to determine loan offer eligibility), the deployer bears high-risk obligations. But you, as the provider, need to have given them adequate instructions and documentation to fulfil those obligations.

Moreover, if a deployer uses your AI system under their own name or trademark, or modifies its intended purpose, or makes substantial modifications, they become a provider themselves under the Act.

What this means practically:

1. Your terms of service should specify the intended purpose of your AI system
2. Your documentation should clearly state what uses are and aren't covered
3. If you know customers are using your product in high-risk ways, you may have obligations as a provider of a high-risk system
4. Your instructions for use must enable deployers to comply with their own obligations

How the AI Act Intersects With Other Regulations You Already Know

If you've been following this blog series, you'll recognise a pattern. European regulation doesn't exist in isolation. The AI Act works alongside:

GDPR: If your AI system processes personal data (and most do), GDPR applies in parallel. Automated decision-making under GDPR Article 22 requires similar safeguards to the AI Act's human oversight requirements. Data protection impact assessments complement the AI Act's fundamental rights impact assessments. The Digital Omnibus proposes allowing legitimate interest as a legal basis for AI training data, which would affect how you collect and process training datasets.

The Cyber Resilience Act (covered in my CRA post): If your AI-powered SaaS product has a digital element (which it does), CRA applies. Security-by-design, vulnerability handling, and incident reporting requirements overlap with the AI Act's cybersecurity and incident reporting obligations. The good news: compliance with one helps with the other. The audit logging and incident response mechanisms you build for AI Act compliance double as CRA compliance.

The NIS2 Directive (covered in my NIS2 post): If your SaaS is critical infrastructure or serves critical infrastructure clients, NIS2's cybersecurity requirements layer on top. The supply chain security obligations under NIS2 mean your enterprise clients will be asking about your AI governance as part of their vendor assessments.

EU Product Safety Legislation: For AI embedded in physical products, sectoral safety laws (medical devices, machinery, toys) create additional conformity assessment requirements.

The common thread across all of these? Audit logging, incident reporting, risk management, documentation, and human oversight. If you build your architecture to handle these once, you get compliance coverage across multiple regulations.

This is exactly the approach I've taken with GrowCentric.ai and my client implementations. The same ActiveSupport::Notifications event bus that powers the agentic AI architecture also feeds the audit logging system. The same tiered autonomy controller that enables meaningful human oversight also satisfies the AI Act's human oversight requirements. The same technical documentation that describes the system for users also forms the basis of regulatory compliance documentation.

Build the architecture once. Get compliance everywhere.

What You Should Actually Do Right Now (Five Months Out)

Here's the practical checklist. Not legal advice (I'm a developer, not a lawyer), but engineering guidance from someone building compliant AI SaaS for the European market.

Step 1: Map your AI systems. List every AI feature in your product. Not just the obvious machine learning models, but also the recommendation algorithms, the automated decision-making, the content generation, the chatbots. If it "infers from inputs to generate outputs such as predictions, content, recommendations, or decisions," it's potentially an AI system under the Act's definition.

Step 2: Classify each system. For each AI feature, walk through the risk tiers. Is it prohibited? Does it fall under any Annex III category? Does it profile individuals within those categories? If not high-risk, does it have transparency obligations? Document your reasoning.

Step 3: Determine your role. Are you a provider (developing the AI system), a deployer (using someone else's AI system), or both? For each third-party AI tool you integrate (OpenAI, Anthropic, Google), understand whether you're building an AI system on top of their GPAI model, which could make you a provider of that system.

Step 4: Implement the technical foundations. Audit logging. Human override mechanisms. Transparency disclosures. Explainability traces. Data governance pipelines. Technical documentation templates. These are needed regardless of your risk tier and they're far cheaper to build now than to retrofit later.

Step 5: Review your contracts. Terms of service should specify intended purpose. Privacy policies should address AI-specific processing. Vendor agreements for third-party AI components should clarify compliance responsibilities.

Step 6: Monitor the Digital Omnibus. Track the legislative process. If it passes, you get more time for high-risk obligations. If it doesn't, August 2026 stands. Either way, transparency obligations are unaffected.

The Bottom Line

The EU AI Act is not something that's coming eventually. For SaaS builders serving European markets, August 2026 is five months away and the transparency obligations are definitely happening on that date.

Most SaaS products will be limited or minimal risk. That's the good news. But limited risk still means transparency obligations, and the classification exercise itself needs to be done and documented properly. Getting it wrong can mean being treated as a high-risk system without the infrastructure to comply.

The even better news is that compliance with the AI Act largely overlaps with good engineering practice. If you're already building with proper audit logging, human oversight, data governance, and documentation (as I've argued throughout this blog series for CRA, NIS2, and GDPR reasons), you're most of the way there.

And if you're building AI-powered features on Rails and Solidus, or launching an AI SaaS product for the European market, and you want help navigating the classification, building the compliance architecture, or implementing the technical foundations, that's what I do. From GrowCentric.ai to client implementations, every system I build has European compliance baked in from the first line of code.

Because retrofitting compliance is expensive. Building it in is just good architecture.