GDPR & data protection
Lawful basis, data minimisation, retention limits, DPIAs where required, and EU-appropriate data residency — applied to training data, embeddings, logs, and inference.
Responsible AI for regulated Europe
Beyond compliance checklists — AI you can defend to boards, auditors, and data protection officers.
Responsible AI is not a slide at the end of a pitch deck. For our clients in banking, manufacturing, and international institutions, it is how we design every production LLM, RAG pipeline, and agentic workflow — with privacy, security, and human accountability built in from day one.
Built for European regulation
We align delivery with GDPR obligations and EU AI Act expectations — especially for high-risk use cases, cross-border data, and systems that influence operational decisions.
EU AI Act readiness
Risk classification, documentation, transparency for users, human oversight for consequential decisions, and technical measures that match the intended risk tier.
Sector-specific rules
Financial services, manufacturing, and public-sector constraints — mapped to architecture choices, access controls, and audit evidence your DPO and regulators expect.
Our responsible AI framework
Five pillars we apply on every AI programme — from pilot to production.
Governance & accountability
Clear ownership, approval gates, model and prompt change control, and decision logs — so AI behaviour is traceable, not tribal knowledge.
Data trust & privacy
Source-grounded RAG, PII handling, redaction, and retention policies — answers from your data without leaking what should stay protected.
Security by design
Managed Identity, Key Vault, network isolation, tool boundaries for agents, and prompt-injection mitigations — aligned with Zero Trust patterns we ship in production.
Human oversight
Escalation paths, confidence thresholds, and human-in-the-loop for high-impact decisions — automation where it helps, judgment where it matters.
Cost & behaviour observability
Token, latency, and quality monitoring with FinOps guardrails — so AI scales without surprise bills or silent model drift.
How this shows up in delivery
- Architecture reviews that include DPO and security stakeholders early — not after the pilot demo
- Evaluation datasets and regression checks before promoting prompt or model changes
- Private Azure AI Foundry deployments with no training on your data unless explicitly agreed
- Documentation packs for audit: data flows, model choices, controls, and known limitations
- Handover that your team can operate — not a black box that only consultants understand
“Regulators and boards do not ask whether you used AI. They ask whether you can explain what it does, what data it uses, and who is accountable when it is wrong.”