Cut Monthly Data Prep from 40 Hours to 4 Hours
Point A
Your sustainability analyst spends 2 days every month categorizing 5,000 general ledger transactions to the right emission factors. It's 2026—there should be a better way.
Point B
Our NLP engine categorizes 98.3% of transactions automatically. OCR extracts utility bill data without manual typing. Anomaly detection flags questionable supplier responses. Your team reviews exceptions, not entire datasets.
Concrete Benefits
No marketing fluff. Just measurable outcomes you can verify.
Automated Transaction Categorization
AI reads transaction descriptions ('Qantas SYD-MEL') and maps to correct Scope 3 category (6: Business Travel) and emission factor (0.115 kgCO2e/km domestic flight). Manual review only for 1.7% edge cases.
Utility Bill OCR
Upload PDF electricity bills. AI extracts meter number, kWh consumed, billing period, and NMI. No manual data entry. Works with 150+ utility providers globally.
Supplier Data Validation
When supplier reports 0.05 kgCO2e/$ but industry benchmark is 0.32 kgCO2e/$, AI flags for review ('Possible data quality issue: 84% below peer median'). Prevents garbage-in-garbage-out problem.
Climate Disclosure Gap Analysis
Upload AASB S2 or CSRD requirements. AI scans your existing data and highlights missing fields (e.g., 'Scenario analysis: Not started', 'Scope 3 Category 11: Incomplete'). Shows compliance score.
Report Generation Automation
AI drafts narrative sections for sustainability reports (governance structure, risk management process, reduction strategy) based on your carbon data and stakeholder inputs. Edit draft vs writing from blank page.
Continuous Learning Model
AI accuracy improves with your feedback. When you override a category, model learns your business-specific rules. After 3 months, categorization accuracy typically hits 99.5%+.
How It Works
From unstructured data (PDFs, spreadsheets, emails) to structured carbon inventory in minutes. AI handles repetitive pattern recognition; humans handle judgment calls.
Train AI on Your Data
Train AI on Your Data
Initial setup: review 100-200 sample transactions. Confirm or correct AI's categorization. Model learns your vendor names, cost centers, and business-specific emission sources.
Automated Monthly Processing
Automated Monthly Processing
Each month, AI processes new transactions, utility bills, and supplier responses. Categorizes to emission factors. Flags 1-2% exceptions for human review (e.g., 'New vendor: can't determine category').
Review Exceptions Dashboard
Review Exceptions Dashboard
Instead of reviewing 5,000 transactions, you review 85 flagged items. Approve AI suggestions or override with correct category. Takes 30 minutes vs 8 hours manual.
Validate Quality Checks
Validate Quality Checks
AI shows variance analysis ('Total emissions +12% vs last month. Primary drivers: Electricity up 18% due to summer cooling, Air travel up 9%'). Confirm variances are real, not data errors.
Generate Draft Reports
Generate Draft Reports
AI drafts sustainability report sections using templates and your data. Writes governance narrative based on org chart and policy documents. Drafts risk management process from workshop notes.
Export for Human Review
Export for Human Review
AI-generated content exported to Google Docs for editing. Legal reviews forward-looking statements. Communications polishes language. Workflow manages approvals before publishing.
Product Features That Do the Heavy Lifting
Our AI Categorization Engine uses NLP to map GL transactions to emission factors, learning from your feedback to improve accuracy over time. Learn more →
The AI Report Generator drafts narrative sections for sustainability reports based on your carbon data, reducing writing time by 70%. Learn more →
Utility Bill OCR extracts consumption data from PDF bills automatically—no manual typing required for 150+ utility providers. Learn more →
Real-World Results
How companies in your industry use NetNada to solve specific problems.
Challenge
Sustainability analyst spent 16 hours monthly categorizing electricity, gas, and maintenance expenses across 45 buildings. Unsustainable workload.
Solution
AI auto-categorized 97% of transactions after 2-month training. Analyst reviews 75 flagged transactions in 1 hour. Gained 15 hours/month for strategic work (tenant engagement, solar feasibility).
93% reduction in data prep time
Challenge
First AASB S2 report required scenario analysis narrative. Team had data but no idea how to write required disclosure.
Solution
Used NetNada AI to generate draft based on scenario modeling results. AI wrote 1,200-word narrative covering 1.5°C, 2°C, 4°C pathways and revenue impacts. Team edited for accuracy, submitted on time.
Draft report in 2 hours vs 2 weeks estimated
Challenge
8 facilities, 24 meters, bills from 6 different utility providers. Manual data entry took 4 hours monthly and had 5-10% error rate.
Solution
OCR processed all bills automatically. Accuracy improved to 99.8% (AI extracts exact meter readings). Freed up analyst to work on reduction initiatives instead of data entry.
4 hours saved monthly + higher accuracy
What Customers Say
"The AI categorization is shockingly accurate. It understands our vendors better than manual spreadsheet lookups. Saves us hours every month and catches errors we used to miss."
Ollie Nelson
Sustainability Associate
Zip Co
Frequently Asked Questions
Common questions about this solution
Let AI Handle the Tedious Parts of Carbon Accounting
See how sustainability teams use AI to automate transaction categorization, utility bill processing, and report drafting—freeing 30+ hours monthly for strategic work.