DjiniousA team of specialised AI engineers carries a unique need from requirements and simulation through integration, safety and manufacturing preparation—in mass-production timeframes.
requirement = "hold tank level" model = agent.model(requirement) result = simulate(model)
Assign the outcome once. The agents coordinate the disciplines, tools, approvals and evidence needed to deliver the complete system.
A unique need, stated as an outcome.
Architecture, mathematics and digital replicas.
Components, physics and complete-system behavior.
Simulation evidence and safety guarantees.
Build-ready artifacts and manufacturing context.
Control, observe and continuously improve.
Four application platforms already developed, two spatial platforms arriving by the end of August, and one orchestration layer coordinating the work.
System simulation and modelling, digital replicas, and a mathematical toolbox—all driven by AI.
System integration and simulation, integrated physical simulation with NVIDIA Isaac Sim, and component-library management.
Every build is re-proved from the emitted code itself.
A proven-code toolchain for IEC 61508 SIL 4: from Lean 4 specifications and kernel-checked proofs to restricted embedded Rust and per-build certification evidence.
SCADA, control centre and operational digital twins for supervising real systems after deployment.
The spatial context that lets engineering agents understand the world around a system.
3D environment reconstruction from LiDAR, video and photographic data, creating a spatial digital twin.
GIS mapping designed as a governed spatial substrate for AI agents.
It receives the assignment, coordinates specialised engineering agents and approvals, and keeps delivery moving across every platform.
Requirements, models, proofs and operational evidence stay connected.
Each engineering discipline is handled through its own governed tools.
Every handoff carries simulation results, decisions and traceability.
The team works from assignment through manufacturing preparation and operations.
Lessons from building and verifying an AI-defined, safety-critical ERTMS/ETCS system.
A candid engineering account of directing AI agents to build a complete railway signalling system at extraordinary speed—and the verification lessons that followed.
Read on ertms.ignitial.ioHow AI can operate as a governed co-engineer across requirements, architecture, implementation, testing, documentation and safety analysis.
Read on ertms.ignitial.ioA practical look at closed-loop testing where the EVC, RBC and train-dynamics simulator interact against real system-level scenarios.
Read on ertms.ignitial.ioWhether you are defining a system or evaluating the company behind the platform, start the conversation here.
Share the engineering challenge, operating context and result you need.
Discuss the company, ecosystem and the opportunity ahead.
Give the project to the team—or meet the company building it.