Many AI ideas, no clear priorities
We shape a portfolio of use cases, value criteria and a realistic roadmap grounded in available data, risk and delivery capacity.
AI Code · Operational AI · Edge Intelligence · Connected Enterprise
Engineering AI for real operating environments
AI Code designs secure AI, IT/OT and enterprise solutions for industrial, infrastructure and technology organisations — from strategy and target architecture to pilots and operations.
Delivery modelOne system
- 01Strategy
- 02Architecture
- 03Pilot
- 04Operations
Data · Models · Infrastructure · Integration
01Operational AIAI embedded in processes and operations
02Secure AIdata, access, DLP and local models
03Edge IntelligenceComputer Vision and analytics near the asset
04Connected EnterpriseAI connected to IT/OT, ERP and infrastructure
When AI becomes a systems challenge
An AI initiative is rarely just a model. It needs a business objective, data, infrastructure, security, integration and operational accountability.
AI must work with sensitive data
We design on-premise and hybrid environments, RAG, DLP policies, access models and observability without sacrificing control.
A pilot is not becoming a real system
We connect models to IT/OT, ERP, CRM and operating processes, defining the path to production.
Four connected areas of expertise
The architecture is built around the operational challenge, organisational constraints and the client's ability to own the outcome — without dependence on one model or platform.
AI strategy and architecture
AI strategy, use-case portfolio, target architecture, technology choices and delivery economics.
Secure AI and agents
Enterprise LLMs, RAG, local models, DLP controls and agents with governed access to data and business functions.
Computer vision and edge AI
Video analytics, inspections, event and object detection, edge processing and reliable model operations in the field.
IT/OT and enterprise platforms
IIoT, digital twins, ERP/CRM, integration and infrastructure: moving from fragmented systems to a governed platform.
From uncertainty to an operating system
The engagement starts at the maturity level of the challenge: a focused diagnostic can lead into architecture, a pilot or programme leadership.
Diagnose
Objective, constraints, stakeholders, data and decision criteria.
FocusArchitect
Target model, delivery options, risks, investment ranges and roadmap.
SystemProve
A testable hypothesis, prototype in context and an informed scale decision.
EvidenceDeliver
Integration, security, observability, support processes and capability transfer.
OperationsPractice and research
All insightsWeekly field notes on secure AI, Edge AI, agentic systems, time-series models and complex architecture — without recycled press releases or disclosed client data.
A DLP Control Plane for Enterprise AI: Combining Cloud and Local Models
A practical LLM access architecture built around data classification, user context, auditability and safe AI-agent operations.
Edge AI and the Urban Digital Twin: Designing a Governed System
How to connect local video analytics, urban data and scenario modelling without losing privacy or operational control.
Industrial Video Inspection and Predictive Diagnostics: Architecture for Reliable Operations
How to combine Edge AI, engineering rules, telemetry and human expertise in an industrial operating environment.
AI Code / Next step
Discuss the project