Four pillars, one delivery approach.

• Companies whose AI pilots stalled at the integration boundary
• Leadership teams moving from isolated experiments to end-to-end operations
• IT organisations with clear identity, data, and procurement policies

• Task-specific AI agents for multi-step workflows across tools
• In-product copilots for operations, finance, and legal
• Retrieval and drafting automation over existing knowledge stores
• Event-driven AI inside your message bus or data fabric
• Secure connection to ERP, CRM, DMS, ticketing, and identity providers

• An AI system that survives real traffic and carries daily decisions
• Measurable reduction in manual handling on a prioritised workstream
• An architecture that absorbs the next AI feature without a rebuild

Discovery → architecture → build → pilot → production. The same team that designs, builds, and supports. Written deliverables at every phase; code and system are yours.

• Regulated industries with formal vendor security reviews
• Enterprises bringing AI into sensitive or personal data flows
• Leaders who need demonstrable control over data residency and access

• Threat models and security posture documents for AI systems
• EU-resident, private, and on-prem deployments with customer-managed keys
• Identity, role, and policy design across internal and external users
• Secure retrieval with document-level access control
• Assessments of existing AI, agent, and automation environments

• A deployment your security team can sign off on independently
• Documented controls for residency, isolation, retention, and audit
• Clear answers to every question in a typical enterprise vendor review

As a standalone engagement or a cross-cutting thread inside an AI integration project. Deliverables are written: architecture, threat model, runbooks, and a vendor security review pack.

• Finance, legal, and compliance functions with concrete regulatory obligations
• Enterprises responding to the EU AI Act, DORA, NIS2, or sector-specific rules
• Leadership teams that need a consolidated view of AI and automation risk

• AI register, model inventory, and risk-tier classification
• Control design for data provenance, retention, evaluation, and incident response
• Human-in-the-loop and escalation policy, implemented operationally
• Readiness assessments against EU AI Act, DORA, NIS2, and internal policies
• Incident simulations and continuous evaluation pipelines

• An up-to-date, audit-ready view of your AI and automation landscape
• Controls that live inside the systems, not only in the policy document
• A clear path from identified risk to measured mitigation

Scoped assessment → control catalogue → implementation → review cycle. We work alongside your risk, compliance, and IT teams and leave behind documented, reusable artefacts.

• Teams whose answers are scattered across drives, wikis, and DMS
• Organisations with strict access and traceability requirements
• Enterprises that need structured data out of unstructured documents

• Retrieval-augmented systems over policies, contracts, and handbooks
• Internal knowledge assistants with document-level access control
• Document intelligence: extraction, classification, and validation at scale
• Policy and evidence management with versioning
• ISO 27001- and audit-grade documentation pipelines

• Answers with citations that hold up in regulated contexts
• Lower onboarding and support load through fast, grounded access
• A reusable retrieval substrate for the next AI initiatives

Retrieval-first, model-second. We establish retrieval quality against a written evaluation set before layering the user experience.