Consulting
AI consulting, built for European rules
Strategy, governance, and delivery for organisations putting AI into production under EU law.
European organisations do not get to choose between moving fast on AI and staying compliant — the EU AI Act, GDPR, and sector regulators make both mandatory at once. We work at that intersection: assessing where AI is genuinely worth doing, designing the governance that makes it defensible, and building the systems that carry it. The same team writes the assessment and ships the code, which is why our recommendations tend to be ones we can actually implement.
Book an AI readiness call01
Strategy & assessment
Before anything gets built: where AI actually pays off in your operation, and what it will cost to run it under European rules.
AI Opportunity Assessment
A structured pass over your processes and data to separate the use cases with real economic weight from the ones that only demo well. Output is a ranked shortlist with effort, dependency, and risk attached to each.
EU AI Act Readiness Review
Classify each of your AI systems against the Act's risk tiers, map the obligations that follow — technical documentation, logging, human oversight, transparency — and identify where your current setup does not yet meet them.
Build vs. Buy Analysis
Model provider, open-weights, or in-house: an honest total-cost comparison covering inference spend, vendor lock-in, data residency, and the engineering headcount each path assumes.
Data Readiness Diagnostic
Most AI programmes stall on data, not models. We audit lineage, quality, access control, and lawful basis before a single training run is scheduled.
02
Governance & compliance
The paperwork and controls that make an AI system defensible to a regulator, an auditor, or your own board.
AI Governance Framework
Roles, approval gates, and escalation paths for putting a model into production and taking it out again. Written to be used by the people who actually run the systems, not filed away.
GDPR-Compliant AI Architecture
Lawful basis, purpose limitation, data minimisation, and subject rights designed into the system rather than retrofitted — including how you honour erasure requests against a trained model.
Model Risk Management
Bias evaluation, drift monitoring, and performance thresholds with defined owners, so degradation is caught by a dashboard rather than a customer complaint.
Technical Documentation & Audit Trail
Model cards, dataset provenance, evaluation records, and decision logs kept current as a by-product of your pipeline instead of a quarterly scramble.
Data Sovereignty & Residency
Keeping regulated workloads inside EU jurisdiction — EU-region inference, self-hosted open-weights models, and clear boundaries on what may cross to a third-country provider.
03
Architecture & system design
The shape of the system before the first line of it exists: where inference runs, how data reaches it, and where the boundaries sit.
AI System Architecture Design
A reference architecture for the AI capability you are adding — inference placement, data path, trust boundaries, and which components have to stay inside EU jurisdiction. Delivered as diagrams and written decision records your engineers can build from, with the trade-off behind each choice stated rather than implied.
Model Serving & Inference Topology
Routing between hosted and self-hosted models, batching and caching strategy, latency budgets per call path, and defined behaviour when a provider degrades or rate-limits. The cost envelope is worked out before you commit to a topology, not after the first invoice.
Data & Retrieval Platform Design
Ingestion, chunking, embedding, and index lifecycle designed together with access control — so retrieval cannot return a document the requester was never entitled to read, and re-indexing is a scheduled job rather than a rebuild.
Agent Infrastructure & Oversight Boundaries
Tool permissions, execution sandboxes, approval checkpoints, and step-level audit logging placed in the architecture rather than left to prompt instructions. What an agent is allowed to touch should be enforced by the system, not requested of the model.
Architecture Review for AI Adoption
For systems that already exist: where AI can attach without destabilising them — integration seams, latency headroom, failure isolation, and the blast radius when a model returns something wrong.
04
Implementation
We do not stop at the deck. The same team designs, builds, and hands over the running system.
LLM & RAG Systems
Retrieval pipelines over your own corpus, with evaluation harnesses that measure grounding and refusal behaviour rather than vibes.
Agentic Workflow Engineering
Autonomous agents scoped to a bounded task with explicit tool permissions, human checkpoints, and full step-level observability. The practice behind this is documented in detail on our Agentic Engineering page.
MLOps & Evaluation Pipelines
Versioned datasets, reproducible training, regression suites, and staged rollout — so a model update is a routine deploy, not an event.
Legacy System Integration
Connecting AI capability to the ERP, CRM, and internal services you already run, without a migration programme as a precondition.
Team Enablement
Hands-on transfer to your engineers and operators, so the system does not become a dependency on us. We consider a handover successful when your team ships the next change.
05
Assurance
Ongoing evidence that the system still behaves the way you told the regulator it does.
Model Evaluation & Red-Teaming
Adversarial testing for prompt injection, data leakage, jailbreaks, and failure modes specific to your domain — reported with reproduction steps.
Post-Deployment Monitoring
Drift, cost, latency, and quality tracked against agreed thresholds, with alerting wired to an owner rather than a shared inbox.
Fractional AI Leadership
Ongoing advisory for organisations that need senior AI judgement in the room regularly but do not yet need a full-time hire.
01 / 05
- Strategy & assessment
- Governance & compliance
- Architecture & system design
- Implementation
- Assurance
Strategy & assessment
Before anything gets built: where AI actually pays off in your operation, and what it will cost to run it under European rules.
AI Opportunity Assessment
A structured pass over your processes and data to separate the use cases with real economic weight from the ones that only demo well. Output is a ranked shortlist with effort, dependency, and risk attached to each.
EU AI Act Readiness Review
Classify each of your AI systems against the Act's risk tiers, map the obligations that follow — technical documentation, logging, human oversight, transparency — and identify where your current setup does not yet meet them.
Build vs. Buy Analysis
Model provider, open-weights, or in-house: an honest total-cost comparison covering inference spend, vendor lock-in, data residency, and the engineering headcount each path assumes.
Data Readiness Diagnostic
Most AI programmes stall on data, not models. We audit lineage, quality, access control, and lawful basis before a single training run is scheduled.
AI Governance Framework
Roles, approval gates, and escalation paths for putting a model into production and taking it out again. Written to be used by the people who actually run the systems, not filed away.
GDPR-Compliant AI Architecture
Lawful basis, purpose limitation, data minimisation, and subject rights designed into the system rather than retrofitted — including how you honour erasure requests against a trained model.
Model Risk Management
Bias evaluation, drift monitoring, and performance thresholds with defined owners, so degradation is caught by a dashboard rather than a customer complaint.
Technical Documentation & Audit Trail
Model cards, dataset provenance, evaluation records, and decision logs kept current as a by-product of your pipeline instead of a quarterly scramble.
Data Sovereignty & Residency
Keeping regulated workloads inside EU jurisdiction — EU-region inference, self-hosted open-weights models, and clear boundaries on what may cross to a third-country provider.
AI System Architecture Design
A reference architecture for the AI capability you are adding — inference placement, data path, trust boundaries, and which components have to stay inside EU jurisdiction. Delivered as diagrams and written decision records your engineers can build from, with the trade-off behind each choice stated rather than implied.
Model Serving & Inference Topology
Routing between hosted and self-hosted models, batching and caching strategy, latency budgets per call path, and defined behaviour when a provider degrades or rate-limits. The cost envelope is worked out before you commit to a topology, not after the first invoice.
Data & Retrieval Platform Design
Ingestion, chunking, embedding, and index lifecycle designed together with access control — so retrieval cannot return a document the requester was never entitled to read, and re-indexing is a scheduled job rather than a rebuild.
Agent Infrastructure & Oversight Boundaries
Tool permissions, execution sandboxes, approval checkpoints, and step-level audit logging placed in the architecture rather than left to prompt instructions. What an agent is allowed to touch should be enforced by the system, not requested of the model.
Architecture Review for AI Adoption
For systems that already exist: where AI can attach without destabilising them — integration seams, latency headroom, failure isolation, and the blast radius when a model returns something wrong.
LLM & RAG Systems
Retrieval pipelines over your own corpus, with evaluation harnesses that measure grounding and refusal behaviour rather than vibes.
Agentic Workflow Engineering
Autonomous agents scoped to a bounded task with explicit tool permissions, human checkpoints, and full step-level observability. The practice behind this is documented in detail on our Agentic Engineering page.
MLOps & Evaluation Pipelines
Versioned datasets, reproducible training, regression suites, and staged rollout — so a model update is a routine deploy, not an event.
Legacy System Integration
Connecting AI capability to the ERP, CRM, and internal services you already run, without a migration programme as a precondition.
Team Enablement
Hands-on transfer to your engineers and operators, so the system does not become a dependency on us. We consider a handover successful when your team ships the next change.
Model Evaluation & Red-Teaming
Adversarial testing for prompt injection, data leakage, jailbreaks, and failure modes specific to your domain — reported with reproduction steps.
Post-Deployment Monitoring
Drift, cost, latency, and quality tracked against agreed thresholds, with alerting wired to an owner rather than a shared inbox.
Fractional AI Leadership
Ongoing advisory for organisations that need senior AI judgement in the room regularly but do not yet need a full-time hire.
Technologies