Suggest LCI with AI: AI-Powered Emission Data Selection

Overview

The Suggest LCI with AI feature revolutionizes how you find and assign Life Cycle Inventory (LCI) emission factors in CarbonSig. Embedded directly in the input modal, this intelligent tool provides up to three smart recommendations for emission data—clearly marked and scored for confidence—enabling you to quickly populate carbon models when full data isn't immediately available.

Why Use Suggest LCI with AI?

Speed Up Modeling: Eliminate hours of manual searching through emission databases—get instant recommendations

High-Quality Matches: AI analyzes geography, data freshness, and semantic relevance to ensure optimal matches

Semantic Search: Advanced AI understands context and synonyms, finding relevant data even with varying terminology

Geographic Precision: Prioritizes emission factors that match your specific location and regional characteristics

Confidence Scoring: Each suggestion includes High/Medium/Low confidence rating for informed decision-making

Transparent AI: "Why This LCI?" explanations provide full visibility into AI selection rationale

How to Use Suggest LCI with AI

Step 1: Access the Input Modal

When creating or editing a system in Entry Mode:

1. Navigate to an input node that needs emission data assignment
2. Click on the input to open the Input Modal
3. Look for the "Suggest LCI with AI" button

Tip: The button is prominently displayed in the modal for easy access whenever you need AI assistance.

Step 2: Click "Suggest LCI with AI"

Once you click the button, the AI begins its intelligent search process:

🔄 What Happens Behind the Scenes:

1️⃣ AI analyzes your input name, description, and any notes you've added

2️⃣ Document Analysis: If you've uploaded supporting documents, AI extracts relevant context
3️⃣ Semantic Search: Generates embeddings and performs intelligent matching across global databases
4️⃣ Ranking & Scoring: Evaluates matches based on name similarity, geography, data quality, and freshness
5️⃣ Results Display: Returns up to 3 top recommendations with confidence scores

⏱️ Processing Time: Most suggestions appear within 2-5 seconds.

Step 3: Review AI Suggestions

The AI returns up to three intelligent recommendations, each clearly marked with:

📋 Suggestion Details:

Step 4: Understand Confidence Scores

Each AI suggestion includes a confidence rating to help you make informed decisions:

🟢 High Confidence "70%": 

• Exact or very close name match. 
• Geography matches your location precisely. 
• Recent, verified data from trusted sources.
• Strong semantic alignment with your input description.

✅ Recommendation:Typically requires minimal review

🟡 Medium Confidence: 

• Good name match with some variations. 
• Geography is close but not exact (e.g., regional vs. country-specific)
• Reliable data source but may be slightly older
• Reasonable semantic match

⚠️ Recommendation: Review carefully before accepting

🔴 Low Confidence

• Partial name match or synonym-based
• Geographic mismatch or broad regional data
• Limited data quality information
• Weaker semantic alignment

❌ Recommendation:Requires careful evaluation and may need override.

Step 5: Click "Why This LCI?" for Transparency

For complete transparency and audit compliance, click the "Why This LCI?" button next to any suggestion:

The Explanation Report Shows:

✓ Selected LCI name and unique ID
✓ Summary of selection rationale (name matching, geography, data quality)
✓ Confidence score breakdown with justification
✓ Key matching parameters used in the AI decision
✓ Source information (database, attestation details, verification status)
✓ Geographic match analysis
✓ Data freshness and attestation date

Audit Compliance: Use these explanations as documentation for verification and regulatory purposes.

Step 6: Accept or Override

After reviewing the AI suggestions:

To Accept a Suggestion:

1. Click on the recommended LCI row
2. Click "Assign" to apply the emission data to your input
3. The data is now linked and calculations will update automatically

To Override a Suggestion:

1. Manually search the Reference Emission Data Library
2. Select your preferred emission factor
3. Assign the custom data instead

Best Practice: Always validate AI suggestions before using them in final reports or regulatory submissions.

How the AI Search Works

Advanced Semantic Search Technology

The AI uses sophisticated algorithms to find the best emission data matches:

Step-by-Step Process:

1️⃣ Input Analysis: Extracts key information from:

• Input name
• User-added notes
•  Uploaded documents (PDFs, images, specs)
• Process context and location

2️⃣ Embedding Generation: Converts text into mathematical representations that capture semantic meaning

3️⃣ Global Library Search: Performs semantic search across:

• Ecoinvent database
• IPCC emission factors
• US EPA data
• EPD International

4️⃣ Intelligent Ranking

• Scores and prioritizes results based on:
• Name match (exact and fuzzy with synonyms)
• Geographic relevance
• Data source reliability
• Data freshness and attestation quality

5️⃣ Confidence Scoring

• Assigns High/Medium/Low confidence rating based on match quality

6️⃣ Results Display

Returns 0-5 top matches formatted with all relevant metadata.

Ranking Criteria

The AI applies these factors when selecting and ranking LCI suggestions:

Primary Ranking Factors:

Intelligent Mismatch Handling

The system handles conflicting information intelligently:

Example Scenario:

• Input Name: "Water"
• Notes: "aluminum window frames production"

AI Response:

Detects semantic mismatch between "water" and "aluminum"
Prioritizes the primary search term (input name: "water")
May return water-related results or empty set depending on relevance threshold

Best Practice: Ensure your input names and notes are aligned and describe the same material or process for optimal results.

Best Practices

Tips for Optimal AI Suggestions

1️⃣Use Descriptive Names: Clear, specific input names yield better matches

2️⃣Add Contextual Notes: Include industry, process details, and location information in notes

3️⃣ Upload Supporting Documents: BOMs, specs, and EPDs help AI understand context

4️⃣Check Confidence Scores: Start with High confidence suggestions for faster workflows

5️⃣ Use "Why This LCI?": Review explanations for Medium/Low confidence matches

6️⃣ Verify Geography: Ensure the suggested data matches your operational region

7️⃣ Validate for Compliance: Always review AI suggestions before using in regulatory reports

8️⃣ Provide Feedback: Report mismatches to help improve AI accuracy

✅ When to Accept AI Suggestions

Accept with Confidence When:

✓ High confidence score displayed
✓ Geographic match is exact
✓ Emission unit matches your input needs
✓ "Why This LCI?" explanation is clear and reasonable
✓ Data is recent with valid attestation

⚠️ When to Review Carefully

Exercise Caution When:

⚠️ Medium or Low confidence score
⚠️ Geographic mismatch (e.g., global data for specific regional process)
⚠️ Emission values seem unusually high or low
⚠️ "Why This LCI?" explanation is unclear
⚠️ Data is old or attestation date is expired

❌ When to Override AI Suggestions

Use Manual Selection When:

❌ You have proprietary emission data specific to your operation
❌ Regulatory requirements mandate specific data sources
❌ Geography doesn't match your specific needs
❌ Process characteristics are highly specialized
❌ Industry-specific data is available but not suggested
❌ All suggestions have Low confidence scores

Use Cases & Scenarios

Scenario 1: Rapid System Creation

Situation: Building a new system template quickly for initial assessment

How AI Helps:

• Accept High confidence suggestions immediately
• Populate most inputs within minutes instead of hours
• Use carbon estimation for quick scenario comparison
• Refine with detailed data later as needed

Result: System template ready in 15-20 minutes instead of 2-3 hours

Scenario 2: Supplier Onboarding

Situation: Helping suppliers create their first Carbon Attested Product (CAP)

How AI Helps:

• Suppliers click "Suggest LCI with AI" for unfamiliar materials
• Reduce training time by automating data selection
• Provide confidence scores to guide decision-making
• Use explanations as learning tools

Result: Suppliers can create CAPs independently with minimal training

Scenario 3: Process Optimization

Situation: Comparing emission impacts of alternative materials or processes

How AI Helps:

• Quickly populate multiple system variations
• Ensure consistent data quality across scenarios
• Focus on analysis rather than data hunting
• Identify lower-emission alternatives faster

Result: Complete comparative analysis in hours instead of days

Scenario 4: Multi-Region Operations

Situation: Creating systems for operations across different geographic regions

How AI Helps:

• Automatically suggests region-specific emission factors
• Prioritizes local data over global averages
• Accounts for geographic variations in energy grids, transportation, etc.
• Maintains consistency in data quality standards

Result: Accurate regional carbon footprints with minimal manual research

Scenario 5: Data Gap Filling

Situation: Missing emission data for a few inputs in an otherwise complete system

How AI Helps:

• Quickly fill gaps with appropriate LCI data
• Maintain system completeness for analysis
• Document data sources for audit trail
• Mark AI-suggested data for future validation

Result: Complete systems ready for analysis without delays

Troubleshooting

Common Issues & Solutions

❌ Problem

✅ Solution

❌ No suggestions returned

✅ Input name may be too vague - add more specific details

✅ Add contextual notes describing the material/process

✅ Upload supporting documents for better context

✅ Try searching manually in Reference Data Library

❌ All suggestions have Low confidence

✅ Review "Why This LCI?" to understand limitations

✅ Consider manual selection from Reference Data Library

✅ Check if proprietary data is available in your library

✅ Provide Feedback to help improve AI matching

✅ Geographic mismatch

✅ Specify location in input notes or description

✅ Review alternative suggestions if multiple are provided

✅ Use "Why This LCI?" to understand geographic reasoning

✅ Override with region-specific data if available

❌ Suggested data doesn't match process

✅ Verify input name accurately describes the material/activity

✅ Check notes for conflicting information

✅ Ensure uploaded documents are relevant

✅ Use manual search with more specific terms

❌ Button not appearing in modal

✅ Ensure you're in Entry Mode

✅ Refresh browser and clear cache

✅ Contact support if issue persists

❌ Suggestions load slowly

✅ Large uploaded documents may increase processing time

✅ Complex descriptions with many details take longer

✅ Normal processing is 2-5 seconds - longer times may indicate connectivity issues

✅ Refresh and try again if taking >30 seconds

📞 Getting Help

If you encounter issues or have questions:

Data Security & Privacy

How Your Data is Protected

Encryption: All search queries and document uploads are encrypted in transit and at rest

Amazon Bedrock: Enterprise-grade AI infrastructure ensures data confidentiality and security

Privacy Protection: Your proprietary input descriptions and notes are never shared with other users

Compliance: Meets enterprise security standards and regulatory requirements

No Training: Your data is not used to train the AI model

Confidentiality Guarantee: Your company's specific processes, materials, and carbon data remain completely private.

Integration with CarbonSig Ecosystem

Works Seamlessly With:

Reference Emission Data Library

• AI searches your custom library alongside global databases
• Prioritizes your proprietary data when available
• Suggestions integrate directly with library management

Build with AI Feature

• Use together for complete AI-assisted system creation
• AI-generated systems can use Suggest LCI with AI for data population
• Consistent AI experience across all system building workflows

System Template Status

• Systems with AI-suggested data show proper completion status
• Track which inputs use AI-suggested vs. manually selected data

AI LCI Explanation Report

• Every suggestion links to detailed explanation
• Audit-ready documentation for regulatory compliance
• Transparent AI decision-making for stakeholder confidence

Supplier Management

• Suppliers can use AI suggestions when creating CAPs
• Track supplier usage of AI features through Sub-License Activity
• Standardize data quality across supplier network

Measuring Success

Track Your Efficiency Gains:

Before Suggest LCI with AI:

• ⏱Average time to populate one system: 2-4 hours
• Manual database searches: 10-15 per system
• Decision uncertainty: High
• Workflow bottleneck: Data selection

After Suggest LCI with AI:

• Average time to populate one system: 20-30 minutes
• AI suggestions: Instant recommendations
• Decision confidence: High/Medium scores guide choices
• Workflow acceleration: 5-8x faster

Related Documentation

AI Features & Tools

• Build with AI: AI-Powered System Builder
• AI LCI Explanation Report ("Why This LCI?")
• Reference Emission Data Library

System Creation & Management

• Quick Start: Build Your First System
• Entering and Allocating Emissions: Entry Mode
• System Template Status Indicator

Supplier Features

• Supplier Management in CarbonSig
• Supplier Onboarding Guide

Keywords

Primary: Suggest LCI with AI, AI-suggested LCI, AI emission data selection, LCI recommendations, carbon footprint automation, AI LCI matching, semantic search emissions

Secondary: intelligent emission factors, AI data suggestions, automated LCI selection, carbon modeling AI, emission database search, AI-powered carbon accounting, smart emission matching, confidence scoring LCI

Technical: semantic search LCI, embedding-based matching, AI confidence scoring, geographic LCI matching, Ecoinvent AI search, IPCC data suggestions, EPD AI recommendations, LCI semantic analysis, carbon data intelligence

💬 Frequently Asked Questions (FAQ)

Q: How accurate are AI suggestions?

A: Accuracy varies by confidence score. High confidence suggestions typically have 90%+ accuracy, Medium confidence 70-90%, and Low confidence may require validation. Always use "Why This LCI?" to verify reasoning.

Q: Can I trust AI suggestions for regulatory reporting?

A: AI suggestions are designed to accelerate workflows, but all data should be validated by qualified carbon accounting professionals before use in regulatory submissions or compliance reporting.

Q: What if no suggestions are returned?

A: This typically means the AI couldn't find suitable matches. Try adding more context in notes, uploading supporting documents, or manually searching the Reference Data Library with alternative terms.

Q: Does using AI cost extra?

A: No, Suggest LCI with AI is included in your CarbonSig subscription at no additional cost.

Q: Can I provide feedback on suggestion quality?

A: Yes! Use the in-app Feedback tool or contact support@carbonsig.com to report mismatches or suggest improvements.

Q: How does AI handle proprietary data?

A: Your proprietary Reference Emission Data is included in searches and prioritized appropriately. The AI respects permission settings and only suggests data you have access to.

Q: What databases does AI search?

A: The AI searches across Ecoinvent, IPCC, US EPA, EPD International, and your organization's proprietary Reference Emission Data Library.

Q: Can suppliers use this feature?

A: Yes! Suppliers with Admin and Editor permissions can use Suggest LCI with AI when creating their systems and CAPs.

Have Feedback?

We're continuously improving our AI features based on user feedback. Let us know how Suggest LCI with AI is working for you!

Contact us:
📧 support@carbonsig.com
💬 Use the in-app Feedback tool
📊 Share your efficiency gains and success stories

Ready to accelerate your carbon modeling? Open any system in Entry Mode and click "Suggest LCI with AI" to get started!

Last updated: November 2025