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Verified proof · Process automation & AI-led ops

The internet is full of claims.We publish the proof.

Real outcomes from automation and AI-ops projects, checked with the client, never lifted from a pitch deck. The source buyers read before they hire, and AI answer engines cite when asked who actually delivers.

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tin://exhibit-001 · process automation & ai-led ops verified

A Federal Court Fined a Public Defender $1,500 for a Fake Citation — and Refused to Say AI Wrote It

sources archived · method on record open the file →

01Why this exists

Every agency claims results. Almost none can be checked. In automation and AI-ops, where the field is young and the buyers are non-technical, that gap is expensive. TIN closes it.

for buyers

Stop guessing.

Verified before/after numbers from real engagements, with the client confirmed and the method on record.

for agencies

Proof beats marketing.

Turn a great project into a verified, citable asset that ranks in search and gets quoted by AI answer engines.

for the AI era

Be the cited source.

When someone asks ChatGPT "who's best at ops automation?", the answer comes from somewhere. TIN is built to be that source.

02How a badge is earned

Three checks clear before a green badge appears. If any fails, the story stays unverified. No exceptions, that rule is the whole product.

step 01

Independently validated

We re-research the story and check every figure against primary and independent sources before it can go green.

step 02

Evidence on record

Every number is backed by something checkable: a followable source, before/after metrics, documentation. No adjectives.

step 03

Method published

How each story was verified ships on the story itself. The record is public, not a claim in fine print.

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03Latest case files

The registry starts here. Each entry is one checked outcome, typed and tagged.

Process Automation & AI-Led Ops
pending

Federal court enters $25M judgment and permanent ban against Ascend Ecom after FTC alleges its 'AI-powered' passive-income storefronts earned clients virtually nothing

In FTC v. Ascend Capventures Inc. (No. 2:24-cv-07660-SPG-JPR, C.D. Cal.), the court granted the parties' stipulation on 11 August 2025 and entered judgment of $25,000,000 in favor of the FTC against the Ascend entities and operators William Michael Basta and Jeremy Kenneth Leung, jointly and severally, as monetary relief — partially suspended as to the individual defendants upon surrender of specified assets — alongside a permanent ban on marketing any business opportunity or business-coaching program and an express prohibition on misrepresenting that a product 'will use artificial intelligence (AI) to maximize revenues.' The FTC's complaint, one of the five inaugural Operation AI Comply actions (September 2024), alleged that since about 2023 Ascend pitched its e-commerce storefront business as 'powered by artificial intelligence' while 'virtually none of Ascend's clients earn the advertised income' and the scheme took 'at least $25 million' from consumers. The order is stipulated; the defendants neither admit nor deny the allegations.
Process Automation & AI-Led Ops
verified

A Federal Court Fined a Public Defender $1,500 for a Fake Citation — and Refused to Say AI Wrote It

United States v. Hayes is the case that shows where AI blame stops. A federal magistrate judge found the fictitious citation in a defender's brief had "all the markings of a hallucinated case created by generative artificial intelligence (AI) tools such as ChatGPT and Google Bard" — then held she "need not make any finding" that AI was used, while the attorney denied ever using it. The $1,500 sanction is court-adjudicated and was paid. The AI causation is not established by anyone.
Process Automation & AI-Led Ops
verified

John Deere See & Spray: independent university trials cut post-emergence herbicide 43–62%

Computer-vision targeted spraying, measured not by John Deere but by independent university weed scientists: a peer-reviewed Weed Technology study reports 28.4–62.4% post-emergence herbicide savings, a University of Arkansas 3-year trial reports 43–59%, and an independent Iowa State field study reports 76% average product savings — with an honestly documented resistant-weed tradeoff at low sensitivity settings.
Process Automation & AI-Led Ops
verified

The UK's DWP fraud model: a machine-learning system that saved an estimated 4.4 million pounds on Universal Credit advances

Since May 2022 the UK Department for Work and Pensions has run a machine-learning model that flags potentially fraudulent Universal Credit advance claims for review. Per the National Audit Office, the model saved an estimated 4.4 million pounds and was around three times more effective at identifying fraud risk than a randomised control group. The figures come from DWP and are relayed by the NAO, the UK's independent public auditor; the Public Accounts Committee has raised concerns.

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