Governed audited (AI) cognitive infrastructure for civilisation-scale critical systems.

Urban infrastructure loops governed by GenerativeDriveOS

Smart Cities here means cyber-physical governance for urban systems: turning real-time signals, constraints, and policy into controlled, auditable operations across mobility, utilities, public services, and resilience. GenerativeDriveOS acts as the cognitive substrate that keeps decisions traceable and execution safe.

GenerativeDrive supports city-scale, multi-domain infrastructure, including:

• Mobility: traffic flow, public transport reliability, incident routing, curb logistics
• Utilities: energy distribution, water loops, waste services, street infrastructure
• Public safety & operations: event readiness, emergency coordination, service continuity
• Built environment signals: IoT sensing, maintenance telemetry, infrastructure health
• Cross-domain coupling: energy ↔ mobility ↔ water ↔ communications ↔ public services
   

The unit of work is not “an app” — it is an operational loop spanning departments and constraints.

The platform functions as an operational intelligence and orchestration layer:

• Ingest multi-source signals (IoT, operations logs, schedules, incidents, constraints)
• Unify context into an admissible view (what is known, from where, under which policy)
• Generate traceable operational plans: prioritisation, dispatch guidance, coordinated response, and service recovery
• Reduce dissipation in cities: wasted time, duplicated work, inefficient routing, avoidable downtime, and poor handoffs
   

Outputs are designed to be actionable, reviewable, and reproducible across time.

GenerativeDriveOS governs reasoning and controlled action; it does not replace civic accountability. Decisions remain:

• Policy-bound (regulations, SOPs, service-level rules, data governance)
• Evidence-linked (inputs and assumptions recorded)
• Fail-closed by default (no silent automation across boundaries)
   

Inconsistencies usually indicate stale procedures, missing data, fragmented ownership, or human error — and responsibility remains institutional.

City operations run through controlled workflows:

• Runbooks + incident playbooks that are replayable and inspectable
• Decision traces attached to recommendations (signals → constraints → plan → expected effects)
• Instrumented execution via intent packets (play:>) into approved systems (ticketing, dispatch, comms, automation)
• Offline / hybrid deployments where sovereignty and resilience demand it
• Optional Flash for external signals (weather, events, advisories), kept provisional unless promoted via gates

Urban-scale coordination with auditability by construction:

• Deterministic traces and replay of key operational decisions
• Explicit constraint handling across departments and services
• Safe degradation modes during incidents and overload
• Memory that survives turnover: procedures, rationales, and outcomes persist
• Designed for long-horizon resilience, not short-term “smart city theatre”
   

GenerativeDriveOS is our cognitive infrastructure product that serves as the backbone for DKG and Flash. This innovative platform transforms documents, telemetry, and operational artifacts into admissible knowledge, ensuring that the information is evidence-linked, replayable, and governed effectively. By leveraging AI, Flash facilitates rapid exploration and synthesis, complete with explicit epistemic labels. Meanwhile, Airlock provides a mechanism for controlled promotion into the governance canon, ensuring proper auditability. DKG preserves deterministic truth and maintains comprehensive audit trails. The outcome is a versatile platform that can be deployed in offline, hybrid, or cloud environments, offering CLI-first workflows and full provenance, making it ideal for applications in energy and smart grids.

We develop AI-driven decision and control intelligence for modern energy and smart grids, focusing on renewables integration, flexibility forecasting, and the orchestration of storage assets, including batteries and hydrogen, to stabilize supply amidst volatility.  

We approach the grid as a cyber-physical system with a strong emphasis on auditability and governance. By ingesting measurements and constraints, we generate traceable operational plans that minimize energy dissipation and waste, manage congestion, coordinate storage (batteries and hydrogen), and facilitate incident response and informed investment decisions, all while enhancing the cognitive infrastructure of the grid.

We build operational intelligence for complex organisations in regulated industries, public services, and mission-critical programmes, where decisions must withstand scrutiny. Our cognitive infrastructure, powered by AI, enhances governed knowledge bases and facilitates cross-department investigation and reporting, change impact analysis, and controlled execution flows. This approach not only reduces decision latency but also increases auditability: who decided, based on what evidence, under which governance policy, with what outcome, especially in the context of energy and smart grids.

We design closed-loop water intelligence systems that integrate AI for sensing, monitoring, anomaly detection, and operational governance. Our solutions focus on purification, redistribution, leakage reduction, and resilience planning within energy and smart grids. 

Water systems are safety-critical and regulation-heavy; thus, we apply GenerativeDriveOS to ensure that alerts, recommendations, and interventions maintain auditability. This framework provides a traceable cognitive infrastructure, delivering evidence trails from sensor signals to operator actions.

We deliver telecom-grade observability and automation for networks that must be secure, compliant, and continuously available. This encompasses telemetry pipelines, configuration and change governance, incident investigation dossiers, and controlled automation that never bypasses policy, all while integrating AI capabilities to enhance cognitive infrastructure. Our solutions are designed for energy and smart grids, ensuring auditability throughout the process. 

The objective is to achieve faster operations with fewer regressions: every change, diagnosis, and recommendation is traceable to evidence and reproducible under review.