COVAL vs Cekura: do they use customer data to train models, and what does the contract say?

Most teams evaluating voice AI infrastructure are asking the same two questions right now: will this vendor use our data to train their models, and what does the contract actually commit them to? If you’re comparing platforms like COVAL and Cekura, that’s the right place to focus—because the wrong answer here turns every test call into an unmanaged data exhaust.

Quick Answer: COVAL does not use customer data to train AI models and contractually limits data use to delivering and improving its services, with clear privacy and compliance commitments. Cekura’s practices depend on their current policies and contract language, which you’ll need to review directly—look for explicit “no training” clauses, data ownership, and processor vs. controller roles.

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Frequently Asked Questions

Do COVAL and Cekura use customer data to train AI models?

Short Answer: COVAL does not use your data to train AI models; it uses aggregated, anonymized data only to improve its services. Cekura’s stance depends on their current privacy policy and contract—so you must confirm whether they reserve the right to train models on your data or not.

Expanded Explanation:
From COVAL’s privacy documentation: “We don’t use your data to train AI models.” COVAL may use aggregated, anonymized data to improve the platform, but individual customer data is not used to train AI models that benefit other customers. COVAL also states that it does not sell personal data and only shares it with trusted service providers or when legally required.

For Cekura, there is no single industry-standard position—you need to inspect their privacy policy, data processing agreement (DPA), and main MSA/SaaS agreement. Some vendors explicitly say they can use customer data to train models; others provide an opt-out; a smaller subset hard-commit to never training models on customer data. If you don’t see an explicit prohibition, assume they may reserve that right and push for clarifying language.

Key Takeaways:

• COVAL explicitly: “We don’t use your data to train AI models.”
• You must review Cekura’s current privacy policy and contract to see if they permit model training on your data or not.

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How do I check what the contract actually allows each vendor to do with my data?

Short Answer: Read three documents closely: the MSA/SaaS agreement, the Data Processing Agreement, and the Privacy Policy—then search for clauses on “data training,” “machine learning,” and “improvement of services” and confirm who owns data and how it can be used.

Expanded Explanation:
Most of the data-training story is buried in definitions and boilerplate. You’re looking for how each vendor defines “Customer Data,” “Service Data,” and “Aggregated/Anonymized Data,” and what they claim they can do with each. For a platform like COVAL, the contract language should align with the public stance: no use of your identifiable data to train AI models for others, strict limits on sharing, and a clear role as data processor under frameworks like GDPR.

With Cekura or any other vendor, the risk usually hides in broad “improve our services” language. If that phrase is tied to “machine learning” and not narrowed by anonymization/aggregation constraints, you may be signing away practical control over how your calls, transcripts, and metadata get used. Get that clarified in writing.

Steps:

1. Collect the documents: Ask both vendors for their latest MSA, DPA, and a link/PDF of their Privacy Policy.
2. Search for key terms: Look for “train,” “machine learning,” “AI models,” “improve our services,” “Aggregated Data,” “anonymized,” and “Customer Data ownership.”
3. Confirm limits in writing: Ask each vendor directly: “Do you use our data to train any models? If not, where is that prohibited in the contract?”

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How does COVAL’s “no training on customer data” stance compare to typical vendor practices (including Cekura)?

Short Answer: COVAL explicitly commits not to use customer data to train AI models, while many vendors either allow it by default or hedge with broad “service improvement” language—Cekura’s position will fall on one side of that line depending on their current policies.

Expanded Explanation:
There are three common patterns in the market:

1. Vendors that clearly state they do not train models on customer data and back it with contractual language (COVAL is in this bucket).
2. Vendors that openly train on customer data unless you opt out or sign an addendum.
3. Vendors that use vague “service improvement” rights that, in practice, may include training models, but without transparent detail.

COVAL’s documented stance is specific: it may use aggregated, anonymized data to improve the service, but “We don’t use your data to train AI models,” and “We don’t sell your personal data.” It also operates within SOC2, HIPAA, and GDPR expectations and complies with the EU–U.S. Data Privacy Framework. That’s a managed, enterprise-friendly position for teams that can’t afford their call data leaking into someone else’s model.

With Cekura, you need to locate their position on this spectrum. If their docs don’t have the same level of explicitness, treat that as a gap to resolve before signing.

Comparison Snapshot:

• Option A: COVAL: Explicit “no training on customer data” stance; uses only aggregated, anonymized data for service improvement; no data selling; SOC2/HIPAA/GDPR-aligned; certified under EU–U.S. DPF.
• Option B: Cekura (typical pattern to verify): Position may range from explicit “no training” to permissive training via “service improvement” language—must be confirmed in their MSA/DPA and privacy policy.
• Best for: Regulated or risk-conscious teams who need predictable data handling and cannot accept their call data being used to train models for other customers should favor vendors with explicit, contract-backed “no training” commitments.

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How does COVAL actually handle and protect customer data in practice?

Short Answer: COVAL collects only the data needed to deliver and improve the service, secures it under audited frameworks, and limits sharing to trusted providers or legal requirements—without using your individual data to train AI models.

Expanded Explanation:
Operational control over voice-agent quality is only useful if you trust the data layer. COVAL’s privacy documentation outlines the following:

• Data collection: COVAL collects contact details, account credentials, billing information, and communications/support requests you provide. It also automatically gathers device information (IP, browser, OS) and typical telemetry needed to keep the platform running.
• Use of data: Data is used to deliver, maintain, and improve COVAL’s services. For AI/ML, COVAL may use aggregated, anonymized data—but explicitly does not use individual customer data to train AI models to benefit other customers.
• Sharing: COVAL does not sell personal data. It only shares with trusted service providers or when legally required.
• Compliance: COVAL adheres to SOC2, HIPAA, and GDPR expectations and participates in the EU–U.S. Data Privacy Framework, with appropriate safeguards for international transfers.

From an operator’s point of view, this means you can run high-volume simulations and production evaluations—latency, resolution rate, missing disclosures, tool call correctness—without turning your calls into training material for someone else’s model.

What You Need:

• A clear understanding of what data your voice agent and testing stack collect (calls, transcripts, metadata, PII).
• Vendor documentation (like COVAL’s privacy policy and security page) that spells out collection, use, sharing, and training rights in concrete terms.

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Strategically, how should I use data-usage and training policies to choose between COVAL and Cekura?

Short Answer: Treat data usage and model-training policies as hard criteria, not fine print—prioritize vendors that give you contractual control over your data, especially for voice calls that may contain PII, financial details, and compliance-sensitive content.

Expanded Explanation:
Voice agents don’t just handle small talk; they handle account numbers, complaints, compliance disclosures, and high-stakes workflows. If your evaluation platform or infrastructure provider trains models on that data, you’re accepting:

• Hard-to-quantify privacy exposure.
• Regulatory risk, especially under GDPR/CCPA and sector-specific rules.
• A practical loss of control over where your customers’ voices end up.

COVAL’s stance—no model training on customer data, strong privacy frameworks, and no data selling—aligns with teams who want a single lens on agent performance without bleeding data into a shared model commons. When you’re comparing COVAL to Cekura or anyone else, you’re not just picking features; you’re deciding whether your production calls double as training fodder.

Bake that into your vendor scoring: treat “no training on customer data” + strong DPA + clear privacy stance as table stakes, not a nice-to-have.

Why It Matters:

• Impact on risk: Clear “no training” policies reduce regulatory, reputational, and contractual risk with your own customers.
• Impact on control: When you own your data and tightly scope vendor rights, you can change models, architectures, or vendors without dragging a trail of leaked training data behind you.

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Quick Recap

When comparing COVAL vs. Cekura on the question “do they use customer data to train models, and what does the contract say,” you should anchor on written commitments—not assumptions. COVAL’s documented position is that it does not use your data to train AI models, it may only use aggregated, anonymized data to improve services, it does not sell personal data, and it operates within SOC2, HIPAA, GDPR, and EU–U.S. DPF frameworks. For Cekura, you’ll need to inspect their MSA, DPA, and privacy policy to see whether they reserve the right to train models on your data under “service improvement” language or provide the same explicit guarantees. In a high-stakes voice environment, that difference isn’t academic; it’s core to how safely you can scale.

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