Artificial Intelligence Transparency Statement

Fonema Inc. Last updated: March 30, 2026

1. What is Fonema?

Fonema is a conversational artificial intelligence platform that automates communications via phone, WhatsApp, and web. Our AI agents can:

• Make and receive phone calls
• Hold conversations via text message on WhatsApp
• Provide support and chat responses on web widgets
• Record, transcribe, and analyze conversations
• Take actions based on configured business logic

Fonema is designed to be a non-human agent that provides communication automation to businesses and organizations in Latin America.

2. How Fonema's AI Works

2.1 Technical Architecture (High-Level)

The Fonema platform operates using the following flow:

1. VOICE/TEXT INPUT    End-user → Microphone/Keyboard → Captured Audio/Text  2. INPUT PROCESSING    Audio → Speech-to-Text (STT) Transcription    Text → Intent and Context Analysis  3. LLM PROCESSING    Transcription/Text → Large Language Model (Claude, GPT-4, etc.)    LLM processes input and generates response  4. RESPONSE SYNTHESIS    Text Response → Text-to-Speech (TTS) Synthesis    Generated Audio → Transmission to user  5. STORAGE AND ANALYSIS    Full Recording → Encrypted Storage    Transcriptions → Behavior Analysis    Metadata → Reports and Optimization 

2.2 Language Models

Fonema uses third-party large language models:

• OpenAI (GPT-4, GPT-3.5): General conversational processing
• Anthropic (Claude): Alternative processing with a focus on safety

These models are pre-trained on public and private historical data. Our use of these models introduces inherent limitations and risks.

2.3 Custom Configuration

Customers can customize agents using:

• System Prompts/Instructions: Directives that define the agent's behavior
• Dynamic Context: Information about the end-user, interaction history, customer data
• Logic Flows: Conditions and actions executed based on user responses
• Knowledge Bases: Documents, policies, or data that the agent can consult

The quality of responses depends directly on the quality of these configurations.

3. Limitations and Critical Warnings

3.1 Hallucinations and Inaccuracies

Fundamental limitation: Language models can generate responses that are:

• Factually incorrect
• Fabricated or "hallucinated" (information that does not exist)
• Inconsistent with instructions
• Grammatically disordered or confusing

Example: If you request information about a customer policy, the agent might generate a response that sounds plausible but is completely false.

Mitigation:

• Use well-structured context/knowledge bases
• Regularly monitor transcripts
• Implement manual validation for critical decisions
• Use agents in "consultant" mode (requires human approval) for important transactions

3.2 Not a Real Person

Critical requirement: End-users MUST know they are speaking with AI, not a person.

The Fonema agent:

• Is NOT an employee, salesperson, or human representative
• Has NO personal experiences, emotions, or consciousness
• Cannot make independent legal or medical decisions
• Does NOT have persistent memory between conversations (unless explicitly configured)
• MAY make mistakes a human would avoid

Failure to disclose its AI nature violates emerging regulations and may constitute fraud.

3.3 No Capacity for Professional Advice

Legal Disclaimer: Fonema agents must NEVER:

• Providing legal advice
• Providing medical advice or diagnosis
• Providing financial or investment advice
• Providing accounting or tax advice
• Providing mental health counseling or therapy

These areas require licensed human professionals. If the user needs professional advice, the agent must immediately escalate to a human or provide professional contacts.

3.4 No Emotions, Consciousness, or Personal Opinions

Conceptual Clarity:

• The agent does NOT emotionally "understand" the user's problem
• The agent does NOT have personal preferences, political beliefs, or opinions
• The agent is NOT capable of genuine empathy (it can simulate emotion recognition)
• The agent does NOT have independent intentions or desires

Any response that seems personal is algorithmically generated, not genuine.

3.5 Quality Depends on Configuration

Agent quality depends on:

• Quality of the prompt/system instructions
• Completeness of the knowledge base
• Quality of context data
• Selection and tuning of the LLM model
• Accuracy of conversation flow logic

An agent with poor configuration will produce poor responses, even when using powerful models.

3.6 No Performance Guarantees

Fonema does NOT guarantee:

• Specific conversion rates
• Specific accuracy or error rate
• That the agent will understand all user requests
• That responses will be completely accurate or helpful
• That the agent will not offend some users
• That the agent will solve all customer problems
• Specific business outcomes

Performance varies depending on use case, configuration, and end-users.

4. Voice Data Processing

4.1 Voice Data Flow

When an end-user calls:

1. Capture: Audio is transmitted encrypted to Fonema servers
2. Transcription: Audio is converted to text using STT (Speech-to-Text) service
3. LLM Processing: Text is sent to language model (OpenAI/Anthropic)
4. Voice Generation: Response is converted to audio using TTS (Text-to-Speech) service
5. Storage: Full recording, transcription, and metadata are stored

4.2 Recording Storage

• Duration: Recordings are retained according to client configuration (typically 30-90 days)
• Location: Stored in Amazon S3 in the United States (us-east-1 by default)
• Encryption: In transit (TLS) and at rest (AES-256)
• Access: Limited access to authorized Fonema personnel for support
• Compliance: Complies with retention regulations based on jurisdiction

4.3 Transcriptions and Metadata

• Processed and stored as non-audio data
• Used for reports, analysis, and agent improvement
• Available for client download
• Processed by sub-processors (OpenAI, Anthropic, AWS) under DPA

5. Training Data Policy

5.1 Client Data Protection

Clear policy: Fonema does NOT use client data to train AI models without explicit consent.

• End-user conversations are NOT used to improve models
• Client configuration data is NOT used for training
• Personal data is NOT used for training

This is our DEFAULT policy (opt-out).

5.2 Consent for Training

If a client gives EXPLICIT consent to use training data:

• Anonymization: Data will be anonymized and personal identifiers removed
• Aggregation: Data will be combined with other clients' data
• Transparency: Client will receive reports on data usage
• Revocation: Consent can be revoked at any time

This is configured in the client's Master Service Agreement (MSA).

5.3 Third-Party Training

The LLM models we use (OpenAI, Anthropic) have their own training policies:

• OpenAI: May use data to improve models (check OpenAI Business Terms)
• Anthropic: Does not use enterprise client data for training without consent

Fonema can request an opt-out on behalf of the client with OpenAI for enterprise accounts.

6. Client Responsibilities

6.1 Mandatory AI Disclosure

The client MUST:

• Disclose that it is AI to end-users before or at the start of interaction
• Do so in a clear, accessible, and immediately understandable manner
• In the end-user's language
• Comply with emerging regulations (EU AI Act, local regulations)

6.2 Behavior Monitoring

The client MUST:

• Regularly monitor agent transcripts and outputs
• Verify that the agent does not make critical errors
• Monitor compliance with regulations (TCPA, recording, consent)
• Audit knowledge base for accuracy

6.3 Appropriate Configuration

The client MUST:

• Configure the agent appropriately for your use case
• Use clear and precise prompts
• Provide updated context and knowledge base
• Escalate to humans for complex or critical issues

6.4 Regulatory Compliance

The client MUST:

• Comply with all applicable laws (TCPA, GDPR, CCPA, LGPD, etc.)
• Obtain required end-user consents
• Comply with local recording regulations
• Comply with AI disclosure requirements

6.5 Prohibition of High-Risk Areas

The client MUST NOT use Fonema for:

• Medical diagnosis or treatment
• Legal advice
• Critical financial decisions
• Identity authentication (without additional verification)
• Discriminatory decisions regarding employment, credit, or housing

7. Known Risks

7.1 Bias

AI models can reflect biases in training data:

• Gender bias in job recommendations
• Racial bias in credit assessments
• Age bias in product offerings
• Language bias (better performance in English than in less represented languages)

Regularly audit responses for bias; monitor affected users.

7.2 Privacy and Security

Potential risks:

• Insertion of sensitive data into prompts (card numbers, SSN)
• Prompt injection to bypass safeguards
• Interception of unsecured communication
• Unauthorized access to recordings

Mitigation: Use encryption; implement access controls; provide security training.

7.3 Toxicity

Models can generate:

• Offensive or discriminatory language
• Hateful content responses if provoked
• Sexually explicit language

Mitigation: Use guardrails; supervise responses; implement content filters.

7.4 Failed Legal Compliance

Non-compliance risks:

• Failure to disclose AI (fraud)
• Recording without required consent (TCPA)
• Making spam calls to numbers on the DNC list (TCPA)
• Non-compliance with emerging AI regulations

Mitigation: Review Acceptable Use Policy; consult local legal counsel.

8. Applicable AI Regulations

8.1 European Union AI Act

Article 50 (Transparency): AI systems must inform users that they are interacting with AI.

• Applies if users are in the EU
• Fonema complies by disclosing in client settings
• Client is responsible for implementing disclosure to end-users

8.2 Emerging Local Regulations

Multiple jurisdictions are developing AI regulations:

• Mexico: LFTR includes transparency requirements for automated communications
• Brazil: LGPD requires disclosure of automated processing
• Colombia, Argentina, Chile: Emerging regulations under development

Recommendation: Consult with local legal counsel regarding compliance.

8.3 Data Privacy Laws

Existing privacy regulations apply:

• GDPR (EU): AI processes must comply with GDPR
• CCPA (California): Disclosure of consumer data use
• LGPD (Brazil): Consent for personal data processing
• LFTR (Mexico): Telecommunications and privacy regulations

9. Continuous Improvement

Fonema invests in improving:

• Accuracy: Reducing hallucination and error rates
• Security: Strengthening against prompt injection and attacks
• Fairness: Reducing bias in responses
• Compliance: Upholding emerging regulations

This statement will be updated as technology and regulations evolve.

10. Contact for AI Concerns

If you have AI-related concerns or incidents:

Email: ai-safety@fonema.ai Subject: "AI Safety/Compliance Concern"

Provide:

• Description of concern
• Specific transcripts or examples
• Use case context
• Date and time of incident

11. Glossary of Terms

12. Additional Resources

• Fonema's Acceptable Use Policy
• Data Sub-Processors
• Public SLA
• Master Service Agreement (MSA)
• Article 50 of the EU AI Act

Note on entities: These terms are issued by Fonema Inc. For customers in Mexico contracting through Fonología, S.A. de C.V., the specific conditions set forth in Annex D of the Master Services Agreement apply.

Fonema Inc. Last updated: March 30, 2026