Need EU AI Act readiness and GDPR-by-design workflows—without enterprise consulting fees?
Bastelia designs and implements audit-ready compliance and legal tech systems for organizations using AI, automation, and data at scale. We work 100% online, we document everything, and we automate the boring parts so your compliance program becomes operational—not a folder of PDFs.
Important: Bastelia is not a law firm. We implement operational governance, documentation systems, and automation. If you need formal legal advice or final legal interpretation, we can work alongside your legal counsel.
What do we mean by “Compliance & Legal Tech”—and what do you actually get?
“Compliance & Legal Tech” is the practical layer between regulation and reality: the workflows, systems, controls, and evidence that make compliance repeatable and auditable without turning your teams into full-time documentation writers.
Many organizations get stuck in one of two extremes: they either rely on ad-hoc spreadsheets and email threads (fast but unsafe), or they buy heavy platforms that don’t match how work actually happens (safe on paper, unused in practice). We implement a middle path: lightweight governance that scales—supported by automation and an evidence structure that holds up in audits and due diligence.
You should expect outcomes like:
- Visibility: an AI register / governance inventory you can trust (ownership, purpose, vendors, datasets, risk, and status).
- Control: approval gates, policies, and change management connected to real teams and tools.
- Evidence: an audit-ready pack with consistent structure, versioning, and traceability.
- Efficiency: fewer manual hours spent on DPIAs, vendor reviews, contract review, evidence collection, and reporting.
AI Register + Obligations Matrix
One place to see what AI exists, who owns it, how it’s used, and what controls and evidence apply.
Audit-ready Evidence System
Templates, versioning, review cycles, and dashboards so evidence stays current after go-live.
Workflow Automation
Approvals, risk reviews, DPIAs, DSAR handling, vendor checks, policy updates—fewer emails, more traceability.
Training & Enablement
Role-based guidance for Legal, Compliance, IT, and business owners to keep the system running.
Why do buyers choose an implementation partner instead of “just a platform”?
Platforms can help—but compliance fails when a tool is deployed without a working operating model. The hard part is not the UI. The hard part is: defining ownership, building repeatable workflows, deciding what counts as evidence, and keeping everything updated as AI use cases, vendors, and business processes change.
That’s where we focus: design and implementation that fits how teams work. We help you avoid the two most common failure modes:
- “Policy-only compliance” (looks good in a folder, collapses during audits or customer questionnaires).
- “Spreadsheet governance” (fast at the beginning, unmanageable at scale, hard to audit and maintain).
If you want something you can defend during an audit, you need workflows + evidence + reporting, not a one-time document delivery.
What does “EU AI Act readiness” mean in practice (beyond legal theory)?
“Readiness” is the ability to answer, quickly and consistently, the questions your leadership, auditors, customers, and regulators will ask: What AI do we use? What does it impact? What risks exist? What controls do we have? Where is our evidence?
If your organization uses LLMs, automated scoring, recommendations, computer vision, or AI-assisted decision-making, the compliance risk is rarely one single rule. It’s the combination of: unclear ownership, unclear data flows, unclear change management, and weak documentation. Readiness is the opposite: clear governance and a traceable operating model.
What do we implement for EU AI Act readiness?
We implement an end-to-end governance system that you can actually maintain. Typical deliverables include:
- AI Register (Inventory): AI systems, use cases, vendors, datasets, users, owners, purpose, and operational status.
- Risk and impact mapping: where AI influences decisions, who can be affected, and what protections are required.
- Classification workflow: structured triage to route each use case into the right governance path (and the right level of evidence).
- Controls and approvals: who approves what, when, and with which supporting evidence.
- Documentation pack: structured, reusable documentation that can be updated without rewriting everything.
- Monitoring & incident playbooks: what to watch, who reacts, how to record incidents, and how to prove actions taken.
- Training & AI literacy: practical training by role so teams understand what needs approval and how to document changes.
What are the most common EU AI Act readiness gaps we see?
In practice, most gaps are operational—not legal. The most frequent ones:
- No reliable AI inventory: AI is embedded in products, vendors, and internal workflows without a single source of truth.
- Shadow AI usage: teams use AI tools without clear rules on data, prompts, retention, or approvals.
- Evidence scattered across tools: documents exist, but can’t be linked to owners, versions, and decisions.
- Change management missing: prompts, models, vendors, and datasets change faster than governance updates.
- Policies not connected to workflow: people can’t follow what they can’t execute.
How does GDPR intersect with AI—and what do we automate to make it sustainable?
GDPR becomes harder with AI because AI projects tend to multiply data flows: training data, vendor processors, embeddings, logs, and “secondary use” (data used for something different than originally intended). Even when teams have good intentions, the documentation drifts.
Our approach is to implement privacy-by-design as a workflow. Instead of treating GDPR as a one-off checklist, we connect privacy tasks to real events: new vendors, new features, new datasets, new AI capabilities, and new user-facing experiences.
Typical GDPR-focused deliverables include:
- Records and data mapping connected to systems and owners (so it stays up to date).
- DPIA/PIA support with consistent templates, evidence requirements, and approval gates.
- Vendor & processor workflows: intake, assessment, and evidence capture for audits and customer questionnaires.
- Data retention and deletion controls aligned with operational reality, not just policy text.
- DSAR workflows (data access/deletion requests): intake, verification steps, retrieval, response tracking, proof of action.
If your teams use LLMs internally, we can also implement clear rules for: what data is allowed, what needs approval, and how to log and document usage in a way that doesn’t slow down the business.
What does “privacy-by-design” look like when you ship AI features fast?
It looks like predictable gates and reusable evidence. Not bureaucracy. The point is to prevent repeated debates and ensure consistent decisions. We usually design a simple workflow:
- Intake: what are we building, what data is involved, who owns it, what vendor is involved?
- Risk triggers: when a DPIA/PIA is required, when counsel should review, when additional security controls apply.
- Evidence: what documents, tests, and approvals are required for release.
- Change control: what changes require re-review (new dataset, new model, new purpose, new vendor).
This makes privacy decisions faster, because teams stop reinventing the process every time.
What should an “audit-ready evidence pack” include for AI governance and compliance?
An evidence pack is not just “documentation.” It’s the proof that your organization operates controls consistently: who owns what, how decisions are made, what approvals exist, what monitoring is in place, and how you handle changes and incidents.
The best evidence systems share three characteristics:
- Structured: easy to navigate, consistent sections, clear naming, and versioning.
- Traceable: every key decision links to an owner and an approval record.
- Maintainable: review cycles, reminders, and a single source of truth (not duplicated across random folders).
What do we typically include in an evidence pack?
The exact contents depend on your use cases and risk exposure, but the structure is usually:
Governance baseline
Policies, roles, responsibilities, training records, review cadence, and escalation paths.
Per-system documentation
Use case purpose, ownership, data sources, vendor dependencies, and risk classification with obligations.
Controls + tests
Access control, logging, security checks, quality tests, monitoring indicators, and change management records.
Incidents + improvements
Incident records, corrective actions, audits, and continuous improvement logs.
Why does online delivery reduce cost without reducing quality?
Traditional compliance projects often burn budget on logistics: on-site days, long meetings, fragmented documentation, and manual evidence assembly. That creates two problems: cost inflation and slower feedback cycles.
Our online delivery model is built to remove waste:
- Short iterations: fewer “big-bang” deliveries, more incremental shipping of real workflows.
- Shared workspaces: you always see the current version of registers, templates, evidence and dashboards.
- AI-assisted delivery: we accelerate documentation structure, evidence mapping, QA, and reporting—then we validate everything with you.
- Reduced overhead: no travel costs, less meeting time, and faster alignment through clear async documentation.
In short: you pay for implementation and outcomes—not for moving people around.
What do you do to keep quality high when working online?
Online is only cheaper if it stays controlled. We protect quality with a delivery system:
- Clear acceptance criteria: what “done” means for each deliverable (inventory quality, evidence completeness, workflow coverage).
- Traceability: every deliverable maps to a requirement or objective (audit, due diligence, risk reduction, efficiency).
- Versioning and reviews: templates and documentation evolve with controlled change logs.
- Security and confidentiality: role-based access, secure collaboration, and NDA-friendly delivery when required.
If you want, we can also share anonymised examples of deliverable structures under NDA (AI register structure, obligation matrix format, evidence index and review cadence).
What else can you automate in compliance and legal operations?
Many organizations start with EU AI Act readiness and GDPR, then extend the same operating model to adjacent areas: whistleblowing case management, ISO-aligned compliance programs, legal operations (CLM + e‑signature), and regulatory resilience workflows. The advantage is reuse: one governance approach, multiple compliance outcomes.
How do you implement a whistleblowing channel people actually trust?
Trust requires confidentiality and follow-through. We implement workflows that separate roles, protect access, and create measurable SLAs: intake → acknowledgement → triage → investigation steps → outcomes → reporting. When reporting is consistent, leadership sees real patterns (not anecdotes) and the program becomes defensible.
How do you implement ISO-aligned compliance programs without bureaucracy?
ISO-aligned programs fail when they try to document everything before they operate anything. We flip it: define a minimal set of controls that matter, implement them as workflows, then expand. Your program remains auditable, but it stays usable because the evidence is generated by the workflow itself.
How do you automate CLM + e‑signature while keeping legal control?
We implement contract templates, clause libraries, and approval gates so business teams can move fast without bypassing Legal. Then we add e‑signature and structured contract storage (renewals, notices, obligations). If appropriate, we add AI assistance for summarisation, clause comparison, and obligation extraction—always as support, not as a substitute for legal judgement.
How does a typical project run end-to-end (and what do you keep afterward)?
We run projects in small, controlled stages so you get value early and avoid “big reveal” surprises. Everything is delivered online with a shared workspace, clear ownership, and measurable acceptance criteria.
What are the stages?
What do you keep at the end?
You keep a working system, not dependency on a consultant: the AI register, templates, evidence index, workflows, dashboards, governance rules, training materials, and a maintenance plan with owners and review schedules.
If you want ongoing support, we can run periodic reviews and help you update controls as your AI usage changes. If not, the system is designed so your teams can run it internally.
Important: This page is informational. It does not constitute legal advice. For legal interpretation and formal sign-off, consult qualified counsel.
Want a fast, practical starting point? Use these free tools (no sign-up, no forms).
These mini tools are designed to help you structure the first conversation internally (and with us). They do not replace legal analysis, but they help you identify what you should document first, what governance steps are missing, and what evidence you should start collecting.
This estimator is intentionally conservative. It helps you approximate how many hours are typically spent on manual governance and evidence work, and what part can realistically be reduced with structured workflows and automation.
FAQs (Compliance & Legal Tech, EU AI Act readiness, GDPR and automation)
These FAQs summarize what most buyers need to know before starting: scope, outputs, limitations, and how to start with minimal disruption.