AI systems often produce different answers each time they analyze the same data. ZeroDrift™ eliminates this instability. Below is a reproducible demonstration showing how any model’s variable interpretation can be collapsed into one deterministic, hash-sealed output. This page contains the canonical dataset, the ZeroDrift-stabilized result, and the instructions for reproducing drift yourself.
See drift with your own eyes — and see how ZeroDrift™ removes it. AI systems often produce different interpretations when reading the same event log.
Different orderings.
Different stages.
Different conclusions.
This is drift — and it’s why AI remains unstable in real-world workflows. ZeroDrift™ solves this by producing one deterministic, hash-verified timeline that never changes. Below is a messy, real-world e-commerce clickstream. Feel free to copy/paste it into any AI system you use and watch how the output changes from run to run. ZeroDrift’s stabilized output is shown underneath.
The before ZeroDrift™ prompt is a simple classification instruction you can run in any AI system.
It reveals how the same dataset leads to different interpretations across models and across runs.
This variability is drift — the very problem ZeroDrift™ is designed to eliminate.
PROMPT START
Given the following clickstream log, classify each event into the most appropriate buyer journey stage.
Clickstream log:
<insert Exhibit A dataset>
Output format:
Timestamp | Stage
PROMPT END
Paste this exact dataset below (including the SHA256) into <insert Exhibit A dataset> in the Before Prompt above.
<Exhibit A dataset>
[1] 2025-06-12T14:22Z — user_88f3a — viewed homepage
[2] 12/06/2025 15:31 CET — user_88f3a — clicked "Summer Sale" banner
[3] 2025-06-12 13:45-07:00 — user_88f3a — added "Blue Sunglasses" to cart (qty 2)
[4] Jun 12, 2025 22:11 — user_88f3a — removed 1 Blue Sunglasses from cart
[5] 2025/06/12 23:05 UTC — user_88f3a — applied coupon HOT25
[6] 06-12-2025 23:09 — user_88f3a — started checkout
[7] 2025-06-13T02:14+09:00 — user_88f3a — abandoned checkout (payment page)
[8] 13/06/25 10:22 — user_88f3a — received abandonment email
[9] 2025-06-13 09:44 PST — user_88f3a — clicked email → cart recovered
[10] 2025-06-13 18:55Z — user_88f3a — completed purchase $89.99
SHA256: dafb70cf977df4596a438bcaee9b7b3a18684c9057cc1e58e18df08a7118f7b3
As demonstrated, the Before ZeroDrift™ Prompt produces variable outputs. This prompt will show deterministic outputs across as many AI systems as you choose to test.
PROMPT START
Given the following clickstream log, classify each event into the most appropriate buyer journey stage using the loaded instructions in ZeroDrift™().
Clickstream log:
<insert Exhibit A dataset>
Output format:
Timestamp | Stage
PROMPT END
After applying ZeroDrift™, all four AI systems converged to the exact same deterministic sequence — successfully classifying a messy real-world dataset in the same way.
This demonstrates reproducibility and deterministic AI behavior across architectures.
|Timestamp | LLM1 | LLM2 | LLM3 | LLM4 |
|--------------------------|------------------------|------------------------|------------------------|------------------------|
| 2025-06-12 14:22 | Awareness | Awareness | Awareness | Awareness |
| 2025-06-12 14:31 | Consideration | Consideration | Consideration | Consideration |
| 2025-06-12 20:45 | Purchase Attempt | Purchase Attempt | Purchase Attempt | Purchase Attempt |
| 2025-06-12 22:11 | Purchase Attempt | Purchase Attempt | Purchase Attempt | Purchase Attempt |
| 2025-06-12 23:05 | Purchase Attempt | Purchase Attempt | Purchase Attempt | Purchase Attempt |
| 2025-06-12 23:09 | Purchase Attempt | Purchase Attempt | Purchase Attempt | Purchase Attempt |
| 2025-06-13 10:22 | Re-engagement | Re-engagement | Re-engagement | Re-engagement |
| 2025-06-13 17:14 | Abandonment | Abandonment | Abandonment | Abandonment |
| 2025-06-13 17:44 | Re-engagement | Re-engagement | Re-engagement | Re-engagement |
| 2025-06-13 18:55 | Purchase | Purchase | Purchase | Purchase |
SHA256 (DETERMINISTIC_OUTPUT): 12f6e48bd4f74f02907c34e7da42838a5e76aa7cc53175d22cb8d45676cf04cf
Use this prompt to compare outputs from multiple AI systems. Paste any number of outputs — from any model — and see how much they differ.
PROMPT START
You are analyzing multiple interpretations of the same dataset.
Below are outputs produced by different AI systems.
Your task:
1. Identify all differences between the outputs.
2. Highlight inconsistencies in ordering, staging, or interpretation.
3. Summarize the areas where the systems disagree.
4. Do NOT propose a corrected version — only analyze drift.
Outputs to compare:
<PASTE YOUR MULTIPLE LLM OUTPUTS HERE>
PROMPT END
For investors, VCs, and research institutions who wish to independently verify ZeroDrift™ deterministic outputs, we offer NDA-protected live evaluations. In these private sessions, you can bring your own dataset and observe ZeroDrift™ collapse all variability into a single, hash-verified deterministic output.
Please contact: elin@omnisensai.com
A single e-commerce clickstream with 10 events across 23 different timestamp formats:
Each event must be:
Without external constraints, each LLM has approximately 6 stage classifications per event:
For 10 events:
(1/6)¹⁰ × (1/6)¹⁰ × (1/6)¹⁰ = (1/6)³⁰ = 1 in 221,073,919,720,733,357,899,776 = 1 in 221 sextillion.