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Let artificial intelligence do the heavy lifting. Dcycle's AI engine classifies invoices, detects anomalies, and surfaces the highest-impact reduction opportunities , all automatically, so your team can focus on taking action.

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AI Insights
DashboardClassificationAnomalies
Live
ManufacturingScope 1-2-33 facilities
50xFaster classification
95%EF accuracy
24/7Monitoring
AI
Auto-classification
1,247 items classified today
Active
Anomaly detection
3 anomalies flagged · 0 critical
Monitoring

Trusted by 2,000+ companies using AI to accelerate ESG reporting

Magnum Capital
Sabadell Venture Capital
Adara Ventures
Queka Real Partners
50x

faster data classification , from weeks of manual categorisation to seconds with AI

How it works

From raw data to actionable insights

Smart auto-classification

Upload an invoice or consumption file. Dcycle's AI automatically identifies the expense type, matches it to the correct emission factor, and categorises it into the right GHG Protocol scope and category , no manual intervention.

Smart Classification95% accuracy
invoice_electricity_q1.pdfAI processing...
Type: Electricity consumptionConfidence: 98% · Scope 2
EF: Spain grid mix 20260.138 kgCO₂e/kWh · MITECO
Category: GHG Cat. 2 — Purchased electricityAuto-mapped · Ready for reporting

Real-time anomaly detection

The AI engine monitors every data point entering the platform. It flags outliers, data gaps, and inconsistencies before they reach your reports , saving hours of manual review. Your team reviews exceptions only, not every line.

Anomaly Detection3 flagged
2Alerts
847Auto-verified
12hSaved
Fleet diesel — Jan 2026⚠ +340% vs. average · Likely data entry error
Water — Q1 missing⚠ Gap detected · Barcelona plant · Expected: ~450 m³
Electricity — All facilitiesWithin expected range · No issues
Natural gas — All facilitiesSeasonal pattern · Normal

Patterns, trends and reduction recommendations

AI analyses your historical data to uncover consumption trends, seasonality, and deviations. See which facilities, categories, or suppliers contribute most to your footprint , and identifies the highest-impact reduction opportunities, prioritised by savings potential and feasibility.

Trends & PatternsAI analysis
Top contributorElectricity42% of total emissions
YoY trend-12.3%vs. same period 2025
1
Madrid HQ — Electricity42% of total · Seasonal peak Jul-Aug
2
Barcelona — Natural Gas28% of total · +8% vs. last year
3
Fleet — Diesel18% of total · -23% after optimisation

Dcycle Agent , ask your data

Query any sustainability data in natural language. Ask 'What was my biggest emission source in Q1?' or 'Which supplier has the highest footprint?' and get instant answers based on your real data , no navigating tables or exporting reports.

Dcycle AgentAI
¿Cuál fue mi mayor fuente de emisiones en Q1 2026?
Electricidad — Madrid HQ fue la mayor fuente con 186 tCO₂e (42% del total).
Electricidad42%
Gas natural28%
Flota diésel18%
Pregunta algo sobre tus datos...
Smart Classification95% accuracy
invoice_electricity_q1.pdfAI processing...
Type: Electricity consumptionConfidence: 98% · Scope 2
EF: Spain grid mix 20260.138 kgCO₂e/kWh · MITECO
Category: GHG Cat. 2 — Purchased electricityAuto-mapped · Ready for reporting
Anomaly Detection3 flagged
2Alerts
847Auto-verified
12hSaved
Fleet diesel — Jan 2026⚠ +340% vs. average · Likely data entry error
Water — Q1 missing⚠ Gap detected · Barcelona plant · Expected: ~450 m³
Electricity — All facilitiesWithin expected range · No issues
Natural gas — All facilitiesSeasonal pattern · Normal
Trends & PatternsAI analysis
Top contributorElectricity42% of total emissions
YoY trend-12.3%vs. same period 2025
1
Madrid HQ — Electricity42% of total · Seasonal peak Jul-Aug
2
Barcelona — Natural Gas28% of total · +8% vs. last year
3
Fleet — Diesel18% of total · -23% after optimisation
Dcycle AgentAI
¿Cuál fue mi mayor fuente de emisiones en Q1 2026?
Electricidad — Madrid HQ fue la mayor fuente con 186 tCO₂e (42% del total).
Electricidad42%
Gas natural28%
Flota diésel18%
Pregunta algo sobre tus datos...

Additional features

See how Dcycle compares to the alternatives

Your choice Manual process Legacy tools

Automatic emission factor mapping

AI selects the most accurate emission factor from thousands , by country, sector, and activity type , with 95% accuracy.

Cross-validation of data

Every data point is automatically cross-checked against external sources and historical patterns to ensure reliability before inclusion in reports.

24/7 continuous monitoring

AI works around the clock , detecting changes, recalculating emissions, and alerting on deviations in real time.

Continuous learning

The model improves with every data point you process. The more you use Dcycle, the more accurate classification and anomaly detection become.

Explainable and auditable AI

Every AI decision is documented , which emission factor was selected, why, and with what confidence level. Full traceability for auditors.

From weeks to minutes

What used to take weeks of manual work , classifying invoices, finding emission factors, spotting errors , now takes minutes. Your team focuses on action, not data processing.

“Dcycle helped us consolidate 6 facilities across 2 countries into a single source of truth. Our auditors couldn't believe the level of traceability.”
Lina Pena Corp. Services Director, KLN Iberia
“I see your help as super necessary , I can't imagine maintaining the contract without your advisory component. We went from scattered spreadsheets to a unified system in weeks.”
Yolanda Carazo Environmental Manager, Embutidos Monells
“Our investors value our ESG commitment. Dcycle made us a reference in the financial sector , we can respond to any audit or client request instantly.”
Teresa de Vicente Sustainability Lead, Finsolutia

Learn more about ESG and sustainability

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Bulk Upload for Facility Consumption

You can now upload electricity, water, and fuel consumption data for all your facilities in bulk using a single file.

Read more

Frequently asked questions

How does Dcycle's AI classify sustainability data?
Dcycle's AI engine automatically identifies expense types from invoices and documents, matches them to the correct emission factors, and categorises them into GHG Protocol scopes and categories , eliminating weeks of manual classification work.
Can the AI detect errors or anomalies in my ESG data?
Yes. The AI monitors every data point entering the platform in real time, flagging outliers, data gaps, and inconsistencies before they reach your reports. Your team only reviews exceptions, not every line.
Is Dcycle's AI explainable and auditable?
Every AI decision is fully documented , which emission factor was selected, why it was chosen, and with what confidence level. This provides complete traceability for auditors and compliance teams.

Collect once. Use everywhere.

See how Dcycle can cut your reporting time by 70% and give your auditors what they need , the first time.

See Dcycle in action