STATUS: ONGOING // PILOT LAUNCH

Laceria Energy

Translating deep research into a Multi-Agent AI Energy Platform.

SECTOR
Energy Tech AI
INTELLIGENCE
Autonomous Agents
// 01_LAB_TO_MARKET_GAP

The "Black Box" Barrier

Laceria's PhD team built groundbreaking algorithms for energy optimization, but they were trapped in the lab. Their predictive models were invisible to the market—no interface, no brand, no connected ecosystem.

The challenge was translating raw mathematical brilliance into a trusted, consumer-facing product. They needed to bridge the gap between "Research Paper" and "Scalable Business."

> [ALGORITHM] Optimizing Grid Load (Node #842)...

> [OUTPUT] Predicted_Savings: 14%

> [SYSTEM] ERROR: NO_USER_INTERFACE_FOUND.

> [SYSTEM] ERROR: IOT_DISCONNECTED.

> [STATUS] Data stuck in CSV export. User adoption: 0.

_ Awaiting Productization...

// 02_INTELLIGENT_MESH

The Multi-Agent Ecosystem

SMART HOMESOLAR ARRAYLACERIA AI COREFORECAST AGENT(Predictive Load)TRADING AGENT(Auto-Sell Surplus)CARBON AGENT(CO2 Optimization)USER APP(Savings & Control)GRID DASH(Network Health)

Multi-Agent System

Deployed autonomous AI agents that negotiate energy loads, forecast spikes, and trade surplus power without user intervention.

IoT Integration

Built a robust MQTT layer to ingest real-time data from smart meters, solar inverters, and HVAC systems.

Dual Interface

Created separate but connected experiences: a simple app for Home Users and a complex dashboard for Grid Operators.

// 03_HUMANIZING_DATA

Scientific yet Approachable

We crafted a design language that unites "Academic Rigor" with "Consumer Trust." The goal was to make complex energy data feel as simple as checking the weather.

  • Visualizing Savings: Abstract algorithms translated into clear "Dollars Saved" metrics.
  • Predictive Alerts: "Turn on AC now to save cost" notifications powered by forecasting agents.
  • Sustainability First: CO2 impact is surfaced alongside price, encouraging green behavior.
LACERIA
GRID STABLE
FORECASTED SAVINGS (TODAY)
€4.50 / 12kWh
AI OPTIMIZATION ACTIVE
DEVICE HEALTH
98%
CO2 REDUCED
12kg
// 04_AGENT_LOGIC
ForecastAgent.pyPython (Azure Databricks)
class EnergyAgent:
    def predict_load(self, history, weather_data):
        # Combine historical usage with real-time weather context
        features = self.engineer_features(history, weather_data)

        # Run inference on LSTM model
        prediction = self.model.predict(features)

        if prediction > self.threshold:
            self.trigger_alert("High Load Predicted: Reduce HVAC")
            self.execute_trade("BUY", amount=prediction - self.capacity)

        return prediction
// 05_PILOT_METRICS
FORECAST ACCURACY
92% (vs 75% baseline)
PREDICTIVE PRECISION
STATUS
PILOT LAUNCH

Currently deploying with major hotel chains in the Netherlands for initial infrastructure testing.

● IN PROGRESS