Graiphic presents NEST, an advanced architecture enabling buildings to learn, predict, and optimize their energy behaviour — autonomously, locally, and efficiently.
Read the full NEST Whitepaper (PDF)
Buildings represent nearly 40% of global energy consumption and a substantial portion of greenhouse gas emissions.
Traditional Building Management Systems (BMS) remain static, cloud-dependent, and unable to adapt to evolving environmental or occupancy conditions.
NEST (Next Energy Smart Twin) introduces a new paradigm.
It combines Generative AI, Data-Driven Digital Twins, and Reinforcement Learning into a unified, self-learning, and sovereign AI that enables buildings to understand themselves and adapt continuously.
NEST operates through three coordinated stages, executed entirely on the edge:
-
Generative AI Bootstrapping
A large language model (LLM) initiates intelligent HVAC control based on semantic reasoning and real-time data.
→ Immediate operational intelligence without historical data. -
Data-Driven Digital Twin Construction
A predictive model learns the thermal and energy dynamics of the building.
→ Continuous self-learning without requiring BIM or cloud services. -
Reinforcement Learning Optimization
An RL agent trains on the digital twin to discover optimal control policies.
→ Fully autonomous and progressively improving energy performance.
All computation, data storage, and control remain fully local, ensuring:
- Compliance with GDPR and ISO 50001
- Very low latency
- Broad interoperability (BACnet, Modbus, MQTT)
- Complete independence from cloud infrastructures
NEST occupies a new category of sovereign, explainable AI dedicated to real-time building energy optimisation.
| Dimension | Classical BMS | Cloud Predictive Control | NEST |
|---|---|---|---|
| Autonomy | Fixed rules | Limited adaptivity | Self-learning |
| Infrastructure | Cloud-dependent | Cloud-assisted | Fully local |
| Intelligence | Manual tuning | Partial prediction | Full cognitive loop |
| Energy Savings | <10% | 15–20% | 25–35% |
| ROI | >36 months | ~30 months | <12 months |
NEST runs natively on platforms such as NVIDIA Jetson Orin, Jetson Nano, and DGX Spark (2025).
The system is built entirely on ONNX Runtime (opset 20, IR 10) to execute inference, learning, and control on site.
Key elements:
- Local time-series database for telemetry
- Embedded web dashboard for supervision and manual override
- Standard interfaces (BACnet, Modbus, MQTT, REST)
- Edge-first execution ensuring privacy, resilience, and predictability
- Energy savings: 25–35%
- Return on investment: less than 12 months
- Comfort deviation: ±0.3 °C
- Compliance: EU Green Deal, BACS Directive, ISO 50001
- Foundations: Generative AI + Digital Twin + Reinforcement Learning
By turning each building into an autonomous energy agent, NEST aligns AI innovation with sustainability and strategic sovereignty.
NEST transforms buildings into adaptive, intelligent systems capable of self-optimisation.
By embedding intelligence directly at the hardware layer, the solution eliminates cloud dependency, enhances explainability, and delivers measurable performance gains in real time.
Graiphic seeks collaboration with:
- Equity investors supporting large-scale deployment across Europe and Africa
- Industrial partners providing pilot sites and validation opportunities
- HVAC, IoT, and energy companies integrating their products within NEST’s ONNX-based orchestration
For collaboration inquiries:
[email protected]
https://www.graiphic.io
- NEST Whitepaper 1.0 (PDF): ./Doc/NEST_Whitepaper_1.0.pdf
- Whitepaper Cover (PNG): ./img/NEST_Whitepaper.PNG
- NEST Logo (SVG): ./img/logo%20nest.svg
- Orville Chart (PNG): ./img/Orvillechart_GTB.png
Distributed under the MIT License.
© 2025 Graiphic — Advancing Sovereign AI for Energy Efficiency.