AI Transparency & Responsible Use

Our Commitment to Transparency

At Quantome, we believe in being completely transparent about how we use artificial intelligence to enhance your business analytics experience. This page outlines what AI models we use, how we use them, and most importantly, how we protect your data privacy.

 

Which AI Models We Use

We leverage state-of-the-art large language models (LLMs) to power our intelligent features:

• Primary AI Provider: OpenAI's GPT-4 and GPT-4 Turbo models

• Purpose: Natural language understanding, business insights generation, and intelligent assistance

• Integration: Our AI assistant helps you analyze metrics, build financial models, and gain actionable insights from your business data​

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How We Use AI

Our AI capabilities are designed to make business analytics accessible and actionable:

1. Intelligent Chat Assistant

• Answer questions about your business performance in natural language

• Explain complex metrics and financial models in simple terms

• Provide guidance on building forecasts and financial projections

• Suggest relevant metrics and KPIs based on your business type

2. Automated Onboarding

• Identify your business type and recommend appropriate templates

• Suggest relevant metrics based on your industry

• Help you build initial financial models and dashboards

• Guide you through data integration setup

3. Smart Metric & Model Creation

• Assist in creating custom metrics using natural language

• Help build financial models with formula suggestions

• Generate dashboard configurations based on your needs

• Provide business context and best practices

4. Data Analysis & Insights

• Generate visualizations and charts from your metrics

• Identify trends and patterns in your business data

• Provide period-over-period comparisons and analysis

• Offer strategic recommendations based on your data

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Your Data Privacy: Our Core Principle

We do NOT expose your raw business data to AI models. This is a fundamental design principle of our platform.

What We Share with AI:

• Metric definitions and names (e.g., "Monthly Recurring Revenue", "Customer Acquisition Cost")

• Model structures and formulas (e.g., "Gross Profit = Revenue - Cost of Goods Sold")

• Aggregated metric values (e.g., "Revenue trend over time")

• Business context you provide (e.g., "I run a SaaS business")

What We NEVER Share with AI:

• Raw transactional data (individual orders, customer records, etc.)

• Personal identifiable information (PII) (customer names, emails, addresses)

• Sensitive business details (pricing strategies, vendor information)

• Complete datasets (your Google Sheets, database tables, etc.)

 

 

How This Works: A Privacy-First Architecture

Our system is designed with a privacy-preserving layer between your data and AI:

Your Raw Data → Quantome Platform → Metrics & Models → AI Assistant

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Example:

• Your Raw Data: 10,000 individual customer transactions with names, emails, purchase details

• What AI Sees: "Revenue metric shows $150,000 in Q1 2024 with 15% growth"

The AI helps you understand and work with your metrics and models, not your underlying sensitive data.

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Data Security & Compliance

• Encryption: All data is encrypted in transit (TLS) and at rest

• Access Control: Strict role-based access controls and multi-tenant isolation

• API Security: AI interactions are authenticated and logged

• No Training: Your data is never used to train AI models

• Retention: AI conversation logs are retained only for service improvement and can be deleted upon request

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Your Control & Rights

You maintain full control over AI features:​

• Data Deletion: Request deletion of AI conversation history

• Transparency: Review what information was shared with AI in your activity logs

• Feedback: Report concerns or provide feedback on AI interactions

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Continuous Improvement

We are committed to:

• Regularly reviewing and updating our AI usage policies

• Staying current with AI safety and privacy best practices

• Being transparent about any changes to our AI providers or methods

• Listening to user feedback and concerns about AI features

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