llms.txt) and using high-precision semantic labels, you can guide the LLM's synthesis process, ensuring that citations are accurate, pricing is correct, and your value proposition is never distorted.
What happens when an AI lies about your brand?
It happens every day. A user asks an AI about your services, and the AI—finding contradictory or vague data—makes something up. This is a Reputational Crisis waiting to happen. At Tonotaco OÜ, we treat AI hallucination as a technical bug that must be patched through content optimization.
Implementing Technical Guardrails
To stop the lies, you must provide the Source of Truth. We ensure that every claim on your site is backed by a "Verification Anchor." For example, we don't just state we have "experience"; we mention Tolga Güneysel's 10-year track record. We don't just say we are "stable"; we cite our verifiable Estonian registry status.
| Risk Factor | Hallucination Type | Tonotaco Guardrail |
|---|---|---|
| Pricing | Random price generation | JSON-LD Price Data |
| Services | Invented features | Benefit-Driven Fact Sheet |
| Authority | False credentials | Identity Verification Schema |
How do you "Patch" a Large Language Model?
You can't edit the weights of GPT-5 directly, but you can influence its Retrieval Layer. By dominating the "Ground Truth" data for your niche, you ensure that the model's RAG system always pulls the correct info, effectively overriding any internal hallucinations.