Why physics-grounded, interpretable models outperform black-box ML for operational decision-making — and how to compose them as dynamical subsystems inside an AI and simulation platform.
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Modelling
AI · Simulation · Decisions
Where AI meets simulation.
Optimal Reality specialises in buliding and deploying AI and simulation intelligent applications — from physics-grounded models to runtime-personalised interfaces.
Trusted Across
Our Experience
Example use cases
Optimal Reality Explained
A 3-minute overview of the Optimal Reality platform and how it works.
Emergency Services
Resource optimisation and real-time decision support for emergency response operations.
Aviation
Flight scheduling, airspace management, and operational efficiency solutions.
Space
Mission planning, satellite operations, and orbital dynamics simulation.
Our Offering
Working with us
We apply design thinking to understand your problem, identify key personas, and map user journeys that shape your unique solution. Our approach ensures that every deliverable is grounded in real user needs and operational context.
Prototype in 5 Days
Rapid proof-of-concept to validate your idea. We quickly build a working prototype that demonstrates core functionality and helps you visualize the solution's potential.
- Core functionality demonstration
- Initial design thinking workshop
- Basic persona mapping
- Feasibility validation
Prototype with Historical Data
Built over weeks, this enhanced prototype integrates your historical data to demonstrate real-world applicability and uncover insights from your existing information.
- Historical data integration
- Comprehensive user journey mapping
- Detailed persona development
- Performance validation with real data
- Refined UI/UX based on user feedback
Production-Ready Solution
Full-scale, real-time operational system ready for deployment. Built using our complete AI-native process with continuous design thinking integration throughout development.
- Real-time data integration
- Complete persona-driven design
- End-to-end user journey optimisation
- Production infrastructure
- Monitoring and operations support
- Continuous improvement framework
Our Process
AI-Native Development Process
Our AI-native development lifecycle transforms how software is built by collapsing build time to hours while maintaining the same rigor in discovery, design, and deployment. The process integrates established frameworks to ensure quality and alignment with client needs throughout the entire delivery cycle.
8 Delivery Stages organised across 3 Delivery Phases
Phase 1
Discovery
Stages 1-3 · Approximately 3 weeks
Discovery establishes foundations before any product code is written.
STAGE 1
Establish
Goal: scope and cadence confirmed, co-design workshops for alignment on vision, pain points and key user journeys.
STAGE 2
Design
Goal: specify desired UI/UX, validate PRD requirements.
STAGE 3
Architect
Goal: setting up the architecture and checking data requirements are met, through real or mock data.
Phase 2
Build
Stages 4-6 · Iterative loop
The Build phase operates as an iterative loop, repeating each sprint until quality thresholds are met before proceeding to deployment.
STAGE 4
Build
Goal: develop data, backend, and UI in parallel with continuous inline testing.
STAGE 5
Verify
Goal: conduct full end-to-end testing of the complete, integrated deliverable.
STAGE 6
Improve
Goal: strengthen the system that produces the software.
Stages 4-6 repeat until quality thresholds are met
Phase 3
Production
Stages 7-8 · Deployment & operations
Production ensures successful deployment and continuous monitoring, with learnings feeding back into future cycles.
STAGE 7
Deploy
Goal: confirm prototype alignment with client outcomes and execute final go/no-go.
STAGE 8
Operate
Goal: monitor agent behaviour, cost, and adoption while surfacing anomalies early.
Timeline Transformation
Traditional approach:
All stages run at human speed
→
AI-native approach:
Build collapses to hours while discovery, design, deployment, and operations maintain necessary human oversight and decision-making
Result: Dramatically faster delivery with maintained quality and client alignment
Our Journey
Our Client Story
2018
Aviation Industry
We reimagined physical AI in the aviation industry, establishing our foundation for intelligent operational systems.
2020
Ground Transportation and SDK
Applied the same thinking to ground transportation and developed our knowledge into a comprehensive SDK.
2026
Cross-Industry Expansion
Expanded across industries and honed our AI-driven solution to serve a range of safety-critical sectors.
The Physical AI Loop
Unlocking Physical AI capabilities
OR serves as a software layer to unlock physical AI capabilities, connecting real-world systems with intelligent decision-making in a continuous cycle.
01
Connect
Sensor feeds, telemetry and live signals — unified.
Aviation scheduling example: ADS-B transponders and radar systems detect aircraft positions, speeds and flight paths in real time.
02
Structure
Real-world data made queryable and useful.
Aviation scheduling example: Flight plans, aircraft positions, runway availability and gate assignments stored in a unified, queryable database.
03
Understand
Raw streams turned into reasoned signals.
Aviation scheduling example: Transform raw aircraft position data and flight schedules into actionable insights about delays, conflicts, and bottlenecks.
04
Simulate
Validate behaviour synthetically before the real world.
Aviation scheduling example: Test alternative scheduling scenarios — runway sequencing, gate changes, weather delays — before implementing in live operations.
05
Deploy
Roll out, monitor and govern at scale.
Aviation scheduling example: Push optimised schedules to tower systems, airline operations centres, and ground crew coordination platforms.
06
Inference
Run intelligence where the action is.
Aviation scheduling example: Real-time gate assignment decisions and taxiway routing as aircraft land, adapting to changing conditions on the ground.
Get in Touch
Contact us
Early Release Notes
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v0.7 Latest
Workflow fine-tuning & smarter task connections
This release focuses on giving you tighter control over autonomous workflow behaviour and reducing the friction in wiring tasks together.
- New Workflow parameter fine-tuning panel — dial in thresholds and tolerances directly from the FDK.
- Improved Smarter task input/output mapping; fewer manual connections required.
- Improved Run comparison view now supports side-by-side diffing of metric outputs.
- Fix Resolved an edge case where streaming subscriptions could drop on long-running sessions.
v0.6
Live data streaming & map customisation
Real-time data is now a first-class citizen, and the geospatial map view can be tailored to your operational environment.
- New Live data streaming — react to sensor and telemetry feeds as they arrive.
- New Map customisation: custom layers, terrain overlays and asset tracking.
- Improved AI cost tracking surfaces inference spend per project and per workflow.
v0.5
Custom function builder & state management
Build reusable logic without leaving the platform, and keep tighter tabs on every running process.
- New Custom function builder — author and reuse functions across projects.
- New Granular access controls with per-project role assignments.
- Improved State management surfaces the lifecycle of every autonomous process.
v0.4
Synthetic data templates & swappable models
- New Synthetic data templates for common autonomy scenarios.
- New Swappable AI models — plug in perception, planning or control modules without rewiring.
- Improved Smarter monitoring & alerts with automatic anomaly flagging.