AI Services

Engineering AI that
earns its deployment.

Every Gorp Labs engagement is built from first principles against your specific operational constraints. We do not adapt general-purpose models to safety-critical contexts. We build for them.

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AI Strategy
Before a model is trained, before a dataset is assembled, the most important work is understanding whether AI is the right answer — and if so, exactly what kind.
Feasibility Problem framing ROI modelling Risk assessment Roadmapping
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What this involves

We begin every engagement with a structured discovery process. We map your operational environment, your data landscape, your regulatory constraints, and your risk tolerance before proposing any solution. Many AI projects fail not because the model is wrong, but because the problem was never properly framed.

Our strategy engagements typically run four to eight weeks and conclude with a clear, justified recommendation — including the cases where we advise against an AI solution.

Capabilities
  • AI opportunity identification and prioritisation across your operational landscape
  • Technical feasibility assessment including data readiness audits
  • Safety and regulatory risk mapping against applicable frameworks (ISO, IEC, NHS, MoD)
  • Build vs. buy vs. partner analysis with vendor evaluation support
  • AI governance framework design aligned to your organisation's risk appetite
  • Multi-year AI roadmap with clear investment milestones and success metrics
Deliverables
Strategy Report
Executive-ready document covering opportunity landscape, recommended approach, risk assessment, and phased roadmap.
Data Readiness Audit
Structured assessment of your data assets, gaps, governance posture, and preparation requirements.
Governance Framework
Policies, review processes, and accountability structures for responsible AI deployment in your organisation.
Investment Case
Financial model covering build costs, expected benefits, and risk-adjusted return on investment.
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ML Engineering
We build the models. Designed against your specific constraints, validated against your specific failure modes, owned entirely by you.
Model design Training pipelines Evaluation frameworks Fine-tuning Safety testing
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What this involves

Our ML engineering practice covers the full model lifecycle — from architecture selection and dataset curation through training, evaluation, and handover. In safety-critical contexts, evaluation is not a final step. It is woven through every stage of development.

We do not take pre-trained general models and retrofit them to your context. Where domain-specific pre-training is warranted — as it often is in nuclear, healthcare, and security — we build it.

  • Architecture design: supervised, semi-supervised, self-supervised, and reinforcement learning approaches
  • Domain-adaptive pre-training for regulated-sector corpora
  • Training pipeline engineering with experiment tracking and reproducibility guarantees
  • Adversarial robustness testing and out-of-distribution evaluation
  • Interpretability and explainability tooling for regulatory and operational review
  • Model documentation to EU AI Act, ISO 42001, and sector-specific standards
Trained Model
Fully documented, versioned, and validated model with complete training provenance.
Evaluation Report
Performance metrics across standard and adversarial benchmarks relevant to your domain.
Model Card
Regulatory-ready documentation covering intended use, limitations, and performance characteristics.
Training Pipeline
Reproducible pipeline with full documentation enabling your team to retrain as data evolves.
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Data Architecture
The model is only as good as the data it learns from. In safety-critical environments, data quality, provenance, and governance are non-negotiable.
Data pipelines Governance Quality frameworks Provenance tracking Security
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What this involves

We design and build the data infrastructure that makes AI systems reliable in production. This means pipelines that are auditable, data stores that are secure, and quality frameworks that catch issues before they reach a model.

In regulated sectors, data architecture is as much a legal question as an engineering one. We design for compliance from the outset — not as an afterthought.

  • End-to-end data pipeline design and implementation for ML workloads
  • Data quality frameworks with automated monitoring and alerting
  • Provenance and lineage tracking for audit and regulatory review
  • Secure data environments for classified or sensitive operational data
  • Data governance policy design aligned to GDPR, NHS DSP, and sector standards
  • Synthetic data generation for training in data-scarce or privacy-constrained contexts
Data Architecture Design
Documented architecture with component specifications, security model, and scaling plan.
Pipeline Implementation
Production-ready pipelines with monitoring, alerting, and full operational documentation.
Governance Framework
Policies, access controls, and review processes for ongoing data management.
Quality Audit
Assessment of existing data assets with remediation recommendations and prioritised action plan.
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Deployment & Monitoring
Go-live is not the end of the engagement. In safety-critical environments, a model that drifts undetected is a liability. We build observability in from the start.
MLOps Drift detection Incident response Performance monitoring Retraining pipelines
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What this involves

We design deployment architectures that prioritise reliability, auditability, and safe failure modes. Every system we deploy includes monitoring dashboards, drift detection, and defined escalation procedures before it operates in a live environment.

We offer ongoing operational support at agreed service levels — from quarterly reviews to 24/7 monitoring partnerships — scaled to the criticality of your deployment.

  • MLOps pipeline design and implementation across cloud and on-premise environments
  • Real-time model performance monitoring with configurable alerting thresholds
  • Data and concept drift detection with automated retraining triggers
  • Incident response playbooks and runbooks for operational teams
  • Audit logging and performance reporting for regulatory review cycles
  • Capacity planning and cost optimisation for production ML workloads
Deployment Architecture
Production-ready deployment with infrastructure-as-code, CI/CD, and rollback procedures.
Monitoring Dashboard
Real-time visibility into model performance, data quality, and system health.
Ops Runbook
Operational documentation covering monitoring procedures, escalation paths, and incident response.
SLA Framework
Defined service levels, monitoring commitments, and response time guarantees for ongoing support.
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Regulatory Advisory
We understand the regulatory environments our clients operate in. AI compliance is not a checkbox — it is an engineering discipline.
EU AI Act ISO 42001 NHS standards ONR guidance GDPR
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What this involves

Regulated-sector AI has specific compliance obligations that general-purpose AI practitioners often do not understand. We work within the regulatory frameworks of nuclear (ONR), healthcare (MHRA, NHS Digital, CQC), defence (DSTL, MoD), and financial services (FCA) — not around them.

Our advisory practice covers compliance gap analysis, documentation support for regulatory submissions, and expert witness engagements for procurement and assurance processes.

  • EU AI Act conformity assessment for high-risk AI systems
  • ISO/IEC 42001 AI Management System implementation support
  • MHRA AI as a Medical Device (AIaMD) classification and regulatory pathway guidance
  • ONR guidance interpretation for AI applications in nuclear licensed sites
  • NHS DTAC (Digital Technology Assessment Criteria) compliance support
  • Expert advisory for AI procurement and assurance processes
Compliance Gap Analysis
Assessment of your current position against applicable frameworks with prioritised remediation plan.
Regulatory Documentation
Technical documentation packages supporting regulatory submissions and audit processes.
Conformity Assessment
Structured evaluation against specific regulatory standards with auditable evidence package.
Advisory Retainer
Ongoing regulatory intelligence and advice as frameworks evolve — particularly EU AI Act implementation.
01
Understand
Map the environment, the data, the constraints, and the real problem.
02
Architect
Design from first principles. Justify every decision against your constraints.
03
Build
Iterative development with your team embedded in the process.
04
Validate
Stress-test against adversarial conditions and edge cases before deployment.
05
Sustain
Monitor, adapt, and support through the full operational lifecycle.

Tell us about your environment.

We'll tell you whether we can help — and if we can, exactly how. Every engagement starts with an honest conversation.

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