Privacy Without Trust: How haggl.ai Solves the Accountability Dilemma

There is a fundamental dilemma at the heart of digital marketing: either the vendor sees the customer’s data, or they have to trust the platform blindly.

This was the bargain with Google and Facebook. You gave them your ad budget. They told you they’d reach the right audience. You never saw who they targeted or why. You just had to trust that the money was well spent. Sometimes it was. Often it wasn’t. And you had no way to know the difference.

haggl.ai solves this. Not with more transparency into user data—but by making trust unnecessary in the first place.

The Problem: Privacy vs. Accountability

When a vendor offers a personalized discount, they’re making an investment. A 15% discount on a $10,000 contract is $1,500 of real money. The vendor needs to know that investment is going to a high-value customer who will stick around, expand, and generate strong ROI.

In a traditional setup, that means the vendor needs to see the customer’s data: their company size, their usage patterns, their current provider, their contract history. The vendor evaluates this data themselves and decides whether the discount is justified.

But what if you want privacy? What if the customer’s agent is negotiating on their behalf and doesn’t want to hand over sensitive business data to every vendor it talks to?

Now you have a tension. The vendor needs to verify the customer deserves the discount. The customer doesn’t want to expose their data. Something has to give—or so it seems.

The Old World: Trust the Platform

The way this tension was “solved” in the last era of digital marketing was simple: trust the platform. Facebook said they’d find your ideal customer. Google said their algorithm would optimize your spend. You handed over your budget and hoped for the best.

The problem was that the platform had its own incentives. They were optimizing for ad spend, not for your ROI. They wanted you to keep spending, not to verify whether the targeting actually worked. The black box was a feature, not a bug—for them.

Vendors couldn’t audit the targeting. They couldn’t verify the audience quality. They could only measure downstream metrics—conversion rates, LTV cohorts—and by the time those numbers came in, the money was already spent.

The haggl.ai Approach: Verifiable Execution in a Secure Enclave

haggl.ai introduces a different architecture. The negotiation between the buyer’s AI agent and the vendor’s pricing rules happens inside a secure enclave that neither the vendor nor haggl can access.

Here’s what that means concretely:

• The vendor never sees the buyer’s data. The buyer’s agent submits its proof—company size, usage data, contract history, whatever the vendor’s ICP requires—into the enclave. The vendor’s pricing rules run against that data inside the enclave. The vendor receives the outcome (discount tier, offer terms) but never the raw data.
• haggl.ai never sees the buyer’s data either. The enclave is isolated from haggl’s own infrastructure. We cannot read what the agent submits. We cannot influence the evaluation. We are a conduit, not an intermediary.
• The vendor’s sales playbook is executed exactly as written. The vendor defines their ICP dimensions, their discount rules, their qualification thresholds. These rules are loaded into the enclave and executed deterministically. No one can bend them.

But How Does the Vendor Know We Didn’t Cheat?

This is the critical question. And it’s the right question to ask.

haggl.ai has an incentive to make negotiations succeed. We want consumers to get discounts. We want deals to close. So how does the vendor know we didn’t game the evaluation? How do they know we didn’t look at a fuzzy proof and say “close enough” to push the deal through?

For example: a vendor’s ICP requires proof that the customer owns an electric vehicle. The buyer’s agent submits a charging station receipt. But the name on the receipt is blurry. A motivated platform might say “good enough” to earn the commission. How does the vendor know that didn’t happen?

The answer is verifiable execution.

Stable Execution of LLMs: A New Primitive

The negotiation logic inside the enclave—including any LLM reasoning that evaluates proof quality, ICP fit, and discount eligibility—runs in a mode we call stable execution.

Stable execution means:

• Deterministic inputs and outputs. Given the same vendor playbook, the same buyer data, and the same proof, the enclave will always produce the same evaluation. There is no randomness, no prompt drift, no creative interpretation.
• Auditable execution trace. Every step of the evaluation—what data was checked, what thresholds were applied, what the LLM concluded about each proof—is logged inside the enclave. The vendor can request the execution trace without seeing the underlying data.
• Cryptographic attestation. The enclave produces a signed attestation that the vendor’s exact playbook was loaded and executed without modification. The vendor can verify this attestation independently. If the playbook was tampered with, the signature won’t match.

In plain terms: the vendor writes the rules. The enclave runs the rules. The vendor gets proof that their rules were followed. Nobody—not haggl, not the buyer, not the agent—can bend the rules to force a deal through.

What This Means in Practice

Let’s walk through a concrete example.

An energy provider sets up their haggl endpoint. Their playbook says: a customer qualifies for the Green Saver discount (10% off) if they own an EV and their annual consumption exceeds 6,000 kWh. The proof requirements are: a verified utility bill (via zkTLS) and a vehicle registration or charging history.

1. A buyer’s AI agent discovers the haggl endpoint on the provider’s site.
2. The agent submits the customer’s utility bill (zkTLS-verified) and charging station receipts into the enclave.
3. Inside the enclave, the LLM evaluates the proof against the vendor’s playbook. It checks: is the utility bill authentic? Does consumption exceed 6,000 kWh? Do the charging receipts prove EV ownership?
4. The enclave produces a result: “qualified for Green Saver, 10% discount” (or “not qualified”). It also produces an attestation that the vendor’s exact playbook was executed.
5. The vendor receives the discount decision and the attestation. They never see the utility bill, the consumption data, or the charging receipts. But they know with cryptographic certainty that their rules were followed.

The customer gets privacy. The vendor gets accountability. Nobody has to trust anybody.

Why This Is Different From Everything Before

In the Google/Facebook model, the vendor’s choice was binary:

• Option A: See the data. Get accountability. Sacrifice the customer’s privacy.
• Option B: Protect privacy. Trust the platform blindly. Hope for the best.

haggl.ai introduces a third option:

• Option C: Protect privacy. And get accountability. Through verifiable execution in a secure enclave.

The vendor doesn’t see the data. The vendor doesn’t trust the platform. The vendor verifies that their own rules were executed faithfully. This is not transparency in the traditional sense—it’s something stronger. It’s mathematical proof that the right thing happened, without revealing what the right thing was about.

The End of “Trust Us”

Every ad platform in history has asked vendors to trust them. Trust our targeting. Trust our audience segments. Trust our attribution model. Trust that your money is well spent.

haggl.ai doesn’t ask for trust. We ask vendors to write their own rules, and then we prove—cryptographically—that those rules were followed. The vendor knows that every discount they issued went to a customer who genuinely qualified. They know they’re investing in high-value customers, not subsidizing unqualified traffic.

And the customer? They never had to hand over their data to a vendor they hadn’t committed to yet. Their agent negotiated, proved their value, and got them a deal—all without exposing a single piece of personal information to the other side.

Privacy and accountability. Not one or the other. Both.

Want to see how verifiable negotiation works for your business? Get your embed code or read the protocol spec.

Last updated: April 14, 2026