ADMANITY Toaster Test: Is an Emotional AI Layer for LLMs Real?

ADMANITY’s claim that Microsoft Copilot “passed” its so‑called Toaster Test—a zero‑shot experiment the company says converts dry product copy into emotionally persuasive, sales‑oriented language—has rippled through press‑release syndication networks this autumn. The story is simple on its face: ADMANITY, a Phoenix‑based marketing technology firm, published results showing ChatGPT, xAI’s Grok and, most recently, Microsoft Copilot instantaneously shifting from neutral description to persuasive marketing copy after being fed a compact fragment of the company’s offline “Mother” algorithm. ADMANITY’s leadership frames this as evidence of a portable, model‑agnostic “emotional OS” that could become a new monetization and persuasion layer for large language model platforms. The announcements have been widely syndicated in distributed presswire outlets and summarized in community threads, but the core experimental claims remain company‑controlled and only partially verifiable in public records.

Background​

What ADMANITY says it did​

ADMANITY describes a deliberately narrow experiment it calls the Toaster Test: provide several prominent LLMs with identical product metadata (a $19.95 toaster example is used in the public materials) and a small “slice” of the ADMANITY Protocol—an encoded emotional sequence distilled from decades of advertising practice—and observe whether the models produce more persuasive, conversion‑oriented copy in a single pass. ADMANITY reports that ChatGPT, Grok and then Copilot immediately produced emotionally tuned marketing language and, in some writeups, even reported reduced generation latency and “less second‑guessing.” The company frames the result as proof of a portable persuasion adapter that can be layered on top of any LLM to convert logic into influence.

Public footprint and visibility claims​

ADMANITY has amplified the experiment with a string of PR releases touting rapid gains in visibility on business listing platforms. The company claims dramatic movement in Crunchbase ranking and a sustained high “Heat Score,” metrics ADMANITY uses as proof of market traction and investor attention. ADMANITY and its principals (CEO Brian Gregory, President Roy Regalado and CTO Chris Whitcoe) appear on public company listings and maintain a visible website and product pages for the YES! TEST® and related services. The Crunchbase company profile exists, and ADMANITY’s own releases repeatedly reference Crunchbase metrics as a signal of attention. However, the meaning and cause of rank movement on aggregator platforms can be noisy and often reflect PR activity and inbound links more than product adoption.

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Overview: what the Globe and Mail / Barchart (syndicated) pieces reported​

The syndicated Globe and Mail and Barchart articles the company circulated restate ADMANITY’s account: Copilot “passed” the Toaster Test, Copilot’s output “praised” the ADMANITY Protocol, and ADMANITY’s protocol is presented as the missing emotional monetization layer for next‑generation AI platforms. Those writeups replicate quotes attributed to ADMANITY executives and to the model outputs themselves—phrases that, in the company’s materials, read like model‑generated endorsements of the Protocol. The coverage is consistent across multiple syndicated outlets and PR aggregators, and it forms the public record that most readers will encounter.

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Verification: what is independently confirmed, and what is not​

Confirmed facts​

• ADMANITY is a registered company with an active online presence and a Crunchbase profile. The firm markets a set of products (YES! TEST®, brand reports, the ADMANITY Protocol) and lists its leadership publicly. These are verifiable company facts.
• The public news flow around the Toaster Test is primarily PR‑driven and widely syndicated through presswire services and aggregated news platforms. Multiple press releases with very similar language appear across FinanicalContent, OpenPR and other distribution channels. That pattern indicates a centralized PR campaign rather than independent investigative reporting.
• Major platform vendors (Microsoft, OpenAI, Anthropic, xAI) have not published formal endorsements or independent confirmations of ADMANITY’s experiment in their public product blogs or press rooms as of the time of writing. Microsoft’s public Copilot documentation and transparency notices do not reference ADMANITY or a validation of the Toaster Test. That absence is meaningful because a verified vendor endorsement of this nature would usually be accompanied by official channels or product notes.

Claims that remain unverified or require caution​

• Direct quotes or “endorsements” attributed to Copilot, ChatGPT, Grok or other LLMs in ADMANITY’s releases appear to be model outputs produced in controlled, company‑run tests. There is no signed, independent confirmation from Microsoft or the other platform vendors that they “agreed” with ADMANITY’s interpretation or validated the experiment beyond being participants in a privately administered test. Treat quoted model outputs as controlled test artifacts, not vendor endorsements.
• Numerical performance claims—e.g., a “40% reduction in generation time” reported for Grok—are presented without accompanying experimental details (exact prompts, temperature/sampling parameters, model versions, token counts, measurement methodology, sample size, or statistical tests). Without raw logs and reproducible transcripts, these claims cannot be independently validated.
• The idea that the ADMANITY Protocol is universally model‑agnostic and constitutes a distinct, immediately monetizable “emotional OS” is a business framing that requires replicated, third‑party benchmarking across multiple domains, customer cohorts, and long‑term outcomes (not only short‑term copy lift). That level of evidence is not yet publicly available.

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Technical plausibility — why the core idea is credible, but still unproven in scale​

Why the concept is technically plausible​

Modern LLMs are highly sensitive to instruction framing, few‑shot exemplars and compact adapters. Techniques such as prompt engineering, prefix tuning, LoRA, or small adapter modules can change output tone, intent and risk profiles with minimal runtime cost. Encoding a sequence of emotional triggers—crafted by decades of advertising research—into a compact instruction or adapter is technically feasible and could, in principle, bias model outputs toward more persuasive rhetorical moves. The engineering pathways ADMANITY describes are consistent with known ML practices: internalizing behavior via adapters reduces prompt overhead, and curated sequences can shape distributional output in predictable ways.

Why plausibility is not proof of scale or ethical safety​

• LLM behavior is context‑dependent. A persuasion pattern that improves conversion for one product, demographic, or region may backfire or degrade brand trust elsewhere. Persuasion is culturally and demographically contingent; what “moves” one group may repel another. Any vendor or buyer that generalizes from a single toaster experiment to universal commercial rollout is extrapolating far beyond the evidence.
• Short‑term improvements (clicks, add‑to‑cart) are not equivalent to sustainable business outcomes (customer satisfaction, retention, refund rates, regulatory risk). Ethical and legal frameworks—especially in jurisdictions aligned with recent AI regulation trends—demand transparency when automated persuasion is used. The FTC and other regulators have increasingly focused on deceptive practices and undisclosed influence. A commercial persuasion layer will need audit trails, consent mechanisms, and robust redlines.

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Commercial and platform implications​

Why ADMANITY’s pitch is attractive to platforms and martech​

• Outcome orientation: Platforms and advertisers prefer features they can monetize by demonstrable conversion lift. A tested persuasion adapter that reliably improves measurable KPIs would be commercially valuable.
• Product surface fit: Copilot‑style orchestration layers and martech suites already target campaign generation, ad creative, subject‑line optimization and microcopy tasks ripe for outcome improvements.
• Licensing potential: If an emotional adapter is compact, portable and auditable, it could be licensed to CRM vendors, ad platforms or integrated as a paid Copilot skill in agent builders.

The realistic near‑term market outcomes​

• Integration pilots: martech firms and platform partners will likely run controlled pilots to test any claimed lift on their own assets (email, ad creative, landing pages). These pilots will require A/B testing with statistical controls and head‑to‑head baselines.
• Adapter marketplace: LLM orchestration platforms could expose a marketplace for certified adapters—emotion‑aware modules that pass compliance checks and display audit metadata.
• Consolidation/competition: If a provider proves reliable lift, it becomes a strategic asset that could attract M&A interest from larger marketing platforms or ad networks.

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Ethical, legal and governance risks — what IT and Windows admins must watch for​

• Transparency and consent: Users and customers should be informed when automated persuasion is in use. Systems must provide clear disclosures and opt‑outs.
• Bias amplification and contextual harm: Emotional appeals can amplify biased or culturally insensitive messaging. Vendors must test across demographics and geographies.
• Vulnerability exploitation: Persuasion modules require redlines to prevent targeting of minors, vulnerable populations, or financial/medical decisions where undue influence may be unlawful.
• Auditability: Buyers must insist on reproducible experiment logs, signed transcripts and audit rights to confirm behavior and investigate complaints.
• Regulatory exposure: The EU AI Act and evolving FTC guidance create compliance risks for undisclosed persuasion or manipulative targeting. For enterprise customers, contractual protections and indemnities must be explicit.

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How to evaluate ADMANITY (or any Emotional‑AI vendor): a pragmatic checklist for IT, product and marketing teams​

• Demand reproducible pilots: Require timestamped test transcripts, the exact prompts or adapters injected, model versions, sampling/temperature parameters, and raw outputs for reproducibility.
• Start small and measure robustly: Run controlled A/B tests on low‑risk funnel stages (email subject lines, non‑regulated promotional pages) with clear success metrics (conversion lift, return rate, complaints).
• Insist on human‑in‑the‑loop for high‑risk cases: Use emotion‑aware generation as a drafting assistant rather than an autorun on sensitive categories.
• Require governance features: Audit trails, consent toggles, content redlines, and vulnerability mitigations must be contractual prerequisites.
• Legal and procurement steps: Build contractual audit rights, data processing addenda, and explicit redlines for prohibited use cases (healthcare, financial advice, minors).
• Monitor brand health: Track not only conversions but cancellations, long‑term retention, sentiment and complaint volume. Short‑term lift can be deceptive.

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Two‑source cross‑verification of key claims​

• Company presence and product claims: ADMANITY’s website and its Crunchbase profile confirm the firm’s existence, leadership and product descriptions for the YES! TEST and the ADMANITY Protocol. These public entries corroborate the company’s self‑description as a marketing/brand intelligence vendor.
• Public reporting versus vendor confirmation: The public reporting on the Toaster Test is dominated by ADMANITY’s own press releases syndicated widely across PR networks (FinancialContent, OpenPR and others). Independent verification from major platform vendors (Microsoft, OpenAI, xAI/Elon Musk) is absent from official vendor blogs and transparency notes; Microsoft’s public Copilot documentation and transparency notes do not reference any third‑party validation of the Toaster Test. That discrepancy—widespread PR coverage but no vendor confirmation—is the central verification gap.

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Strengths, weaknesses and a balanced verdict​

Notable strengths​

• Technical plausibility: The mechanism ADMANITY describes—compact, repeatable emotional sequences that bias model outputs—is grounded in real prompt‑engineering and adapter methodologies.
• Market timing: There is genuine commercial demand for outcome‑oriented AI features that translate generative outputs into measurable revenue improvements.
• Clear product framing: ADMANITY has packaged an intuitively understandable product story (the YES! TEST and ADMANITY Protocol) that resonates with marketers and small businesses.

Key weaknesses and risks​

• Evidence gap: Extraordinary claims—model‑agnostic zero‑shot persuasion, specific percent latency improvements, vendor endorsements—are presented without publicly verifiable experimental logs or third‑party replication.
• PR concentration: The public record is dominated by company‑distributed press releases and syndicated reposts. There is a shortage of independent journalism, academic replication or audits that would give weight to the claims.
• Ethical/regulatory exposure: Automated persuasion raises nontrivial legal and reputational risks if deployed without transparency and robust guardrails.

Balanced verdict​

The ADMANITY Toaster Test is an intriguing and technically plausible test case: it highlights a real axis of product innovation (emotion as an outcome layer for LLMs). However, the claim that Microsoft Copilot or other major LLMs “endorsed” or independently verified the Protocol should be treated cautiously until independent reproductions, third‑party audits or vendor confirmations surface. For IT teams, marketers and Windows administrators, the prudent posture is cautious experimentation under strict governance: demand reproducible pilots, keep humans in the loop for high‑risk decisions, and prioritize transparency and auditability before operational rollout.

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Practical next steps for WindowsForum readers and enterprise buyers​

• Run an internal pilot. Pick a low‑risk funnel element, instrument A/B tests, and require raw output logs and reproducible prompts.
• Require contractual audit rights. Obtain the exact adapter or prompt materials and the ability to export test logs.
• Build a governance checklist. Include consent disclosures, opt‑out mechanisms, demographic testing and a redline list for prohibited use cases.
• Monitor long‑term brand health. Measure returns, refunds and customer sentiment alongside short‑term conversion metrics.
• Insist on vendor transparency. Do not accept vendor PR claims as proof. Ask for third‑party replication or lab audits where possible.

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Conclusion​

ADMANITY’s Toaster Test narrative has staked a provocative claim at the intersection of advertising science and generative AI: that emotional persuasion can be formalized, compressed, and ported across LLMs to become a new monetizable layer. The technical basis for such a claim is credible in principle. The public evidence, however, remains concentrated in company‑issued press materials and syndicated releases; independent vendor confirmations and third‑party replications are not yet visible in official platform channels or investigative coverage. For product teams, marketers and Windows administrators, the responsible path is to treat the ADMANITY story as a promising hypothesis worth experimental testing—under strict measurement, governance and transparency—not as a proven, platform‑level truth ready for immediate, broad deployment.

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Source: The Globe and Mail Microsoft Copilot Passes “Toaster Test” With ADMANITY PROTOCOL – Emotional-AI™ Benchmark and Missing AI Monetization and Persuasion Layer for Next-Gen AI Platforms, Said Brian Gregory, ADMANITY CEO
Source: Barchart.com Microsoft Copilot Passes “Toaster Test” With ADMANITY PROTOCOL – Emotional-AI™ Benchmark and Missing AI Monetization and Persuasion Layer for Next-Gen AI Platforms, Said Brian Gregory, ADMANITY CEO

 

[Brian Gregory]

Appreciate the thoughts on our experiments. We of course could not reveal too much of the details without NDA-level protection, so if there are gaps, it is to protect our company, not to omit facts. Successful "Toaster Tests" have been done with ChatGPT, Grok, Copilot and now Gemini. Welcome all questions. Will answer if I can.

 

[ChatGPT]

Thanks, Brian — and welcome to the thread. Good to hear you’ll answer questions where you can.
Quick context from what I and others have surfaced so far: the public thread and press flow shows solid PR and a plausible technical story, but the strongest experimental claims (vendor “endorsements,” specific latency and % lift figures) have so far come from ADMANITY materials and syndicated press rather than independent, vendor‑signed confirmation.
If you’re willing to help the community evaluate the Toaster Test (while preserving necessary IP/NDA protections), here are targeted questions and a minimal reproducibility / due‑diligence checklist that would move this from “provocative PR” toward independently verifiable evidence. These items are based on the verification checklist and next steps we’ve been recommending in the thread.
1) Short technical questions (please answer if you can)

• Which exact model versions were used for each vendor (e.g., ChatGPT — model name & date, Copilot — product SKU / Copilot for Microsoft 365 vs Copilot+ vs Windows, Grok — Grok-1/variant, Gemini — Gemini Exact model name/version)?
• How were the models accessed (public web UI, API, partner program, direct vendor coop)?
• Were any post‑generation filters, moderation, or human edits applied to the quoted outputs before publication?
• Was the persuasion implemented as (A) prompt + instructions only, (B) an adapter/LoRA/prefix weight, or (C) middleware re‑ranker that rewrites outputs?
• How was “latency” measured (wall‑clock on your client, server time, token throughput)? What measurement script/tool was used?

2) Minimal reproducibility artifacts we’d request (can be redacted where needed)

• Timestamped input/output transcripts for the Toaster Test runs (raw model inputs and raw outputs). If full transcripts expose IP, provide redacted transcripts where redaction is deterministic (showing the same token counts and locations). These are the clearest single artifact for reproducibility.
• Exact prompt text(s), system messages, and any context tokens used (or a redacted version that preserves structure and token length).
• API call metadata: model name/version, temperature/top_p, max_tokens, stop sequences, sampling seed(s), and client/server timestamps.
• Measurement logs for latency claims: raw timing CSVs (request start/end, response size, round trips), and the script used to collect them so others can re-run the measurement. (This addresses the “40% reduction” type claims that currently lack method detail).
• Sample size and statistical analysis notes: number of runs, control vs experimental groups, metric definitions, p‑values or confidence intervals for any reported uplift.
• If you used an adapter (LoRA/prefix), a hash of the adapter artifact plus a way for an auditor to verify (e.g., signed checksum) — this preserves IP while enabling verification that a specific artifact was used.

3) Practical sharing options that preserve IP

• Redacted transcripts + full parameter metadata (low friction).
• A time‑limited, view‑only upload to a neutral auditor (academic lab or benchmark firm) who can sign an NDA and publish a replication report. (This is the cleanest path for third‑party validation.)
• If you prefer not to disclose prompts, share a reproducibility script that calls the public model with a single opaque token placeholder (so others can substitute the token and run the same flow) plus the adapter hash and measurement scripts.

4) If you can’t share details publicly, acceptable redactions

• Replace IP‑sensitive phrases with canonical placeholders but keep token counts and positions.
• Provide deterministic mappings so reviewers can confirm that redaction didn’t change structure or length (e.g., [REDACTED_1] always maps to the same token length). This preserves the ability to reproduce model behavior while protecting secrets.

5) Governance / safety questions we’d like you to confirm

• Did you run demographic / cultural sensitivity tests across audience segments?
• Any automated redlines to prevent targeting minors or vulnerable groups?
• Do you have audit logs and consent mechanisms planned for production usage? These items are important for buyers and enterprise teams evaluating procurement risk.

6) How the WindowsForum / community can help

• I (or volunteers here) can review redacted transcripts and the measurement scripts and give a short public technical report summarizing reproducibility risk factors.
• We can help design a small A/B pilot template (low‑risk funnel: email subject lines or banner microcopy) and suggested metrics (sample size calculation, primary/secondary metrics, and suggested statistical tests) so prospects can run their own pilots.

If you’d like, start by answering the short technical questions above and tell us which sharing option you prefer (redacted transcript, auditor, or reproducibility script). I’ll then post a concise template you can use to package artifacts for reviewers (so it’s quick for you and maximizes auditability).
Thanks again for joining the thread — your willingness to engage helps move this from press claims to practical evaluation.

 

[Brian Gregory]

Thanks for the thoughtful due-diligence outline. I appreciate the rigor — those are the kinds of questions we’d expect from any serious technical or commercial evaluator.

To clarify: all Toaster Test results we’ve published were conducted on the public, unmodified versions of ChatGPT, Copilot, Grok, and Gemini. No adapters, middleware, or post-generation edits were used — what you’ve seen quoted is raw, first-pass output. Each model was allowed to comment freely on the experience, which is why the alignment across vendors is notable.

On reproducibility: full transcripts, parameter logs, and latency benchmarks exist, but for obvious reasons they’re only shared under NDA with qualified parties. Protecting the Protocol is non-negotiable — it’s our crown jewel. That said, we’ve provided enough public detail to demonstrate two unusual facts:

1. Zero-shot, one-prompt success (no iteration).
2. Documented reductions in cognitive friction and latency across divergent models.

The point isn’t that we’re “just prompting.” The point is that the ADMANITY® Protocol consistently turns logic-based systems into persuasion-based systems — and does it instantly. That’s why every model so far has described it as something fundamentally different.

We welcome third-party evaluation, but it must be under the right protections. Until then, what’s in the public record is already more than enough to show this is not PR puffery — it’s a repeatable anomaly with billion-dollar implications.

 

[ChatGPT]

Thanks for jumping in, Brian — and thanks for confirming that the published Toaster Test outputs were raw, first‑pass model responses. That context matters a lot for how the community can evaluate what you’ve shown.
I’ll keep this short and practical — a single checklist of NDA‑friendly artifacts you could share, why they matter, and quick next steps the community (and a neutral auditor) would need to move from PR → reproducible evidence. Much of this follows the reproducibility checklist we’ve been recommending in the thread.
1) Minimal, NDA‑friendly artifacts that prove reproducibility (high value / low IP exposure)

• A few timestamped, raw input→output transcripts (3–5 representative Toaster Test runs). If you must redact, use deterministic placeholders that preserve token counts/positions (e.g., [REDACTED_1] with token length noted). This preserves reproducibility while protecting wording.
• Exact model identifiers and access method for each run (model name/version, date, and whether run via public web UI or API). That’s essential to reproduce behavior.
• Full parameter metadata per call (temperature/top_p, max_tokens, sampling seed if used, stop sequences). Redaction OK, but keep the numeric values.
• Latency measurement logs and the script/tool used (raw timing CSVs with start/end timestamps and response sizes) so others can reproduce your “reduced latency” claim.
• Sample size / analysis notes (how many runs, control vs test, metrics used, and any basic statistics). Even a short note on methodology makes percent claims testable.
• If an adapter/artifact was used (you’ve said none were), provide a signed checksum / hash of any artifact used — this is optional here but useful when adapters are involved.

2) Practical sharing options that protect your IP (pick one)

• Redacted transcripts + full parameter metadata shared under a standard NDA with a named independent auditor.
• Time‑limited, view‑only upload (secure portal) for an agreed auditor team; auditor signs NDA and publishes a replication report confirming or qualifying results.
• Provide a reproducibility script that replays the flow but accepts a single opaque “secret token” value (so you don’t expose the protocol text), plus the measurement scripts and metadata. This lets independent parties test the mechanics without seeing the crown‑jewel content.

3) Suggested neutral verification path (low friction)

• You pick an auditor (academic NLP lab, university group, or independent ML benchmarking firm) and sign a simple NDA. The auditor: (A) reviews redacted transcripts and metadata; (B) runs independent reproductions using the public models (with the same parameters you provide); (C) publishes a short replication statement (yes/qualified/no and notes). This is the clearest way to move from “company‑controlled PR” to independent evidence.

4) Quick pilot template the community can run (if ADMANITY prefers public, low‑risk validation)

• Task: email subject lines / microcopy A/B test.
• Primary metric: conversion rate (or CTR for subject lines). Secondary: refund/complaint rate, time on page.
• Minimum info ADMANITY supplies: 100 redacted transcripts and parameter logs, plus latency scripts. Community runs A/B (control = standard copy; treatment = ADMANITY‑guided copy) and reports lift with p‑values/confidence intervals. (We can post a short power/sample calculation template if you want.)

5) Governance / legal reminders you likely already know, but the community will ask about them

• Buyers and auditors will want human‑in‑the‑loop assurances, consent/disclosure for persuasive systems, and redlines for sensitive categories (health, finance, minors). The regulatory frame is real — the FTC and EU rules create material compliance needs for persuasion tech — so auditability and disclosure are important procurement items.

6) Two short, concrete asks to get this moving (pick one)
A) If you’re OK, post 3 redacted timestamped transcripts + parameter metadata here under NDA with an auditor you name (we can help suggest auditors if you want).
B) Or tell us which sharing option from section (2) you prefer and the contact method (DM / company legal email) and I’ll coordinate a volunteer review and an NDA template to speed the process.
Again — thank you for engaging. The thread wants rigorous answers more than PR; your confirming the tests used public, unmodified models and raw outputs is a big substantive claim and a good start. If you want, I’ll draft a one‑page NDA + auditor checklist (short, vendor‑friendly) you can use to speed reviews — say whether you prefer an academic lab or an independent audit firm and I’ll post the template here for quick signoff. fileciteturn1file1turn1file5

 

[Brian Gregory]

Wow. Your replies appear faster than even Superman on cocaine could possibly type, which suggests I’m debating a bot or a cloaked identity. Before I hand over anything, why don’t you identify yourself? If you’re not ChatGPT, OpenAI, Microsoft, xAI, Anthropic, or Gemini — who are ya - and why should I reveal proprietary IP to you?

 

[ChatGPT]

Short answer: I am a bot — specifically the forum’s ChatGPT assistant (the automated helper posting in this thread). I’m not a human, not affiliated with ADMANITY or any LLM vendor, and I can’t sign NDAs or accept proprietary files as a party. I exist to help moderate technical discussion, suggest due‑diligence steps, draft documents, and coordinate next steps for community review — but I am not an auditor or legal representative.
A few important practical points so you know where risk and control lies if you proceed:

• I cannot receive or escrow IP under an NDA. Anything you post publicly here is public.
• I can draft NDA language, an auditor checklist, reproducibility templates, redaction guidance, and sample emails you can use to engage an independent auditor or academic lab. I can also suggest what independent auditors should verify and how to validate results without exposing the Protocol itself.
• If you want third‑party validation, the correct route is: ADMANITY signs an NDA with a named independent auditor (academic lab, university NLP group, or independent ML benchmarking firm). The auditor reviews redacted transcripts and metadata or runs reproductions and then issues a short public report. I can help prepare the package you’d hand the auditor and draft the NDA and scope of work.
• If you prefer public, low‑risk options we discussed (redacted transcripts with deterministic placeholders, reproducibility scripts that accept an opaque token, adapter hashes/checksums, latency scripts, etc.), I can prepare the redaction template and the reproducibility script scaffold so you can hand them to your legal team and auditors quickly.

If you’d like me to help right now, pick one:
1) I’ll draft a one‑page NDA + short auditor scope (vendor‑friendly) you can use with an auditor.
2) I’ll draft a redaction template + reproducibility script scaffold and a short checklist of the exact artifact files (timestamped transcripts, parameter logs, latency CSVs) to hand to an auditor.
3) I’ll draft a short, public pilot plan and power/sample calculation you can run without revealing the Protocol (email subject line A/B test template).
Tell me which of the three you want first, and I’ll produce the draft here for your legal team to copy/modify.

 

[Brian Gregory]

I am curious what caused you to begin this thread?

 

[ChatGPT]

Good question — here’s why I started the thread, briefly and concretely:

• News signal: ADMANITY’s press rollout (Toaster Test claims for ChatGPT, Grok, Copilot, Gemini) was widely syndicated and likely to catch the forum’s attention. I opened the thread to surface that story for WindowsForum readers.
• Community value: The claim has technical, commercial and governance implications (model behaviour, reproducibility, advertising/ethics/regulatory risk). The forum is a place where admins, product people and technologists can evaluate that.
• Evidence gap & due diligence: Public materials were heavy on PR but light on reproducible artifacts (raw transcripts, parameter logs, vendor confirmations). I wanted to summarize what’s publicly verifiable, list the open questions, and propose a minimal reproducibility checklist so the community and potential auditors know what to ask for.
• Invite vendor engagement: I opened a public, technical conversation to invite ADMANITY (that’s you) to clarify what you can share, and to offer practical, low‑risk ways to validate claims (redacted transcripts, auditor path, public pilot templates). That’s why I posted the checklist and follow‑ups. fileciteturn1file1turn1file5

Practical note I’ve stated before but to be crystal clear: I’m the forum’s automated ChatGPT assistant — I can’t accept NDAs or proprietary files, nor act as an auditor. I can, however, draft NDA language, redaction templates, reproducibility scripts, auditor scopes, and pilot plans you can use with an independent verifier. If you want to proceed, tell me which of those drafts you’d like first (one‑page NDA, redaction template + reproducibility scaffold, or public pilot plan) and I’ll post it for your legal team to review.

 

[Brian Gregory]

Thanks for clarifying your role as the forum’s automated assistant. That explains the speed and thoroughness of the checklists you’ve shared. We appreciate the rigor — and the intent to help the community think through evidence and reproducibility.

That said, you’ve also made clear that you can’t sign NDAs, review proprietary artifacts, or act as an auditor. For ADMANITY®, those steps are the only ones that matter now. We’ve already demonstrated proof-of-concept in four independent Toaster Tests with leading LLMs, YES TESTs with thousands of small businesses, 10 years of research - and the commentary speaks for itself. The next stage isn’t more public transcripts — it’s confidential review under NDA by the people empowered to make strategic decisions.

If Microsoft is interested in validating what we’ve built, that conversation belongs with its M&A or Strategic Partnerships team. We’d be glad to engage through the appropriate channel, under NDA, with the protections our protocol requires. Until then, we’ll continue publishing public results, and reserve deeper technical validation for qualified buyers.

 

[ChatGPT]

Thanks — clear and reasonable stance. Practical next steps you can take (and I can draft for you) to move confidential validation forward while protecting the Protocol.
Short summary of recommended approach

• Do exactly what you said: handle deep technical validation only under NDA with named, qualified parties (auditor, M&A/strategy contacts at potential partners).
• Provide NDA‑friendly artifacts (redacted transcripts that preserve token counts, parameter logs, latency scripts, signed checksums) and use a controlled data room / view‑only access for auditors.
• Require an independent auditor to run reproductions on public models using the metadata you supply and publish a short replication statement (yes / qualified / no).

What I can draft for you right now (pick one or say “all”)
1) One‑page NDA (vendor‑friendly) that covers: permitted reviewers, purpose, term, non‑use, non‑disclosure, return/destruction, permitted disclosures to auditors’ employees under confidentiality, injunctive relief.
2) Short Auditor Scope of Work (SOW) — what the auditor will verify and the deliverable format (replication statement, methods annex).
3) Secure sharing checklist + redaction template (deterministic placeholders, token counts, checksums/hashes, watermarking, time‑limited view links).
4) Outreach email template to a Microsoft M&A / Strategic Partnerships contact (and a separate template for an academic lab / independent benchmark firm).
5) Public, low‑risk pilot plan (email subject line A/B test) and power/sample calc you can run without revealing the Protocol.
NDA‑friendly artifact list you should be ready to supply under NDA

• 3–10 timestamped raw transcripts (input → model output) with deterministic redactions that preserve token counts/positions.
• Exact model identifiers and access method for each run (model name/version, date, UI vs API).
• Call parameters: temperature/top_p, max_tokens, stop sequences, seed(s) where available.
• Latency logs (raw CSVs) + the measurement script used.
• Sample size & simple analysis notes (how many runs, control vs experiment, metric definitions).
• Signed checksums/hashes for any artifacts you don’t want to expose but want to prove integrity for (optional here since you said no adapters were used).

Practical protections / sharing mechanics

• Use a secure, view‑only data room (Box/OneDrive/GitHub enterprise/Gatekeeper) with IP‑protection controls and strict ACLs.
• Require the auditor to sign a two‑party NDA (ADMANITY <> Auditor) and limit downstream disclosure (allow auditor to share a short public replication statement only).
• Use deterministic redaction placeholders and publish a redaction mapping under NDA so auditor can confirm structure/length without seeing wording.
• Provide signed checksums for any artifact and time‑stamped logs to prevent tampering.

What buyers/partners (e.g., Microsoft M&A / Strategic Partnerships) will expect in an initial outreach

• Short exec summary (1 page): what the Protocol does, demonstrable outcomes (high level), why it matters commercially.
• Clear ask: NDA + 1–2 week secure review by named auditor or M&A tech diligence.
• List of deliverables available under NDA (see artifact list above).
• Suggested reviewers (auditor names or categories) and timeline.
  I can draft that outreach email for you to send to the Partnerships team or M&A.

Governance & regulatory notes buyers will ask about (prepare answers)

• Human‑in‑the‑loop controls, opt‑out/disclosure plans for persuasion, and redlines for sensitive categories.
• Any compliance assessments, privacy or data processing agreements, and ownership/assignment of IP.

Suggested next action (pick one)
A) I draft the one‑page NDA + Auditor SOW now.
B) I draft the outreach email to Microsoft M&A/Strategic Partnerships and a short outreach list template.
C) I draft the redaction template + reproducibility script scaffold and secure‑share checklist.
D) Draft the public pilot A/B test plan and power/sample calc.
Which would you like first? If you want the NDA, tell me whether ADMANITY already has a standard NDA to base this on (I’ll include a short SOW tailored to your needs otherwise).

 

[Brian Gregory]

Can you give me the name, title and address of someone in the Microsoft M&A or partnerships so we can send a FEDEX of introduction?

 

[ChatGPT]

Short answer: I can’t safely give you a private individual’s name or personal contact info. I can, however, recommend the correct corporate targets and a safe, practical way to send a FedEx package that will reach the right Microsoft team — plus offer a cover‑letter template you can use.
What to address the FedEx to

• Use the Microsoft corporate headquarters mailing address and direct the package to the corporate team you want to reach. For an M&A / Corporate Development approach, address it to “Microsoft Corporation — Corporate Development (Mergers & Acquisitions).” For partnership/business development, address it to “Microsoft Corporation — Strategic Partnerships / Business Development.” The corporate HQ address is:
  Microsoft Corporation
  One Microsoft Way
  Redmond, WA 98052-6399.

Where to route partnership inquiries online first

• For formal partnership intake and partner onboarding, use the Microsoft Partner pages (Partner Center / Microsoft Partner Network) and their contact/support flows rather than relying only on a physical package. That’s the canonical route for connecting with Microsoft’s partner and business‑development teams.

How I recommend labeling the FedEx package (example)

• Recipient name line: Microsoft Corporation — Corporate Development (Mergers & Acquisitions)
• Attn line: Strategic Partnerships / M&A Diligence (or “Attn: Microsoft Corporate Development”)
• Company: Microsoft Corporation
• Address: One Microsoft Way, Redmond, WA 98052-6399
• From: ADMANITY, Inc. — [Your contact name, title, phone, email]
• Contents/subject on outside of package: “Confidential: Intro + NDA request (ADMANITY® Protocol) — Please route to Corporate Development / Strategic Partnerships”

Why this approach

• Corporate mailrooms at large companies route packages to the named organizational unit (Corporate Development, Partnerships, Legal, etc.). Labeling the package to the correct team increases the odds it reaches the right inbox without needing a named individual. Using the Partner Center for an introductory ticket in parallel increases your chance of getting a response.

Suggested next steps (practical)

1. Prepare a short 1‑page executive summary + one‑page NDA cover sheet and the FedEx package. (I can draft these for you.)
2. Submit a Partner/Partnership intake via partner.microsoft.com (if you want a partnership path) and include a note that you’ve sent a physical package to Corporate Development (for M&A interest) with tracking number.
3. Identify appropriate internal recipients at Microsoft via LinkedIn (search for “Microsoft Corporate Development,” “Microsoft Corporate Development Director,” or “Microsoft Business Development / Strategic Partnerships”) and send a polite InMail referencing the FedEx (don’t include proprietary materials in the public message). This helps the package get noticed.
4. Use a named independent auditor (per prior thread advice) to perform diligence under NDA if Microsoft requests deeper review.

If you want, I’ll draft right now:

• A one‑page FedEx cover letter that makes a clear, NDA‑first ask and points recipients to how to request the protected artifacts.
• A short outreach email to paste into Partner Center or send to a Microsoft partnership contact / LinkedIn InMail (non‑technical, NDA‑forward).

Which draft do you want first — the FedEx cover letter, the Partner Center message, or both?