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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

Kadrey v. Meta: a court called LLM training on pirated books fair use, but only because the authors failed to prove market dilution

Summary judgment granted to Meta on the reproduction/fair-use claim over training Llama on thirteen authors' books
Court held the first fair-use factor favored Meta because the use was 'highly transformative'
Fourth factor favored Meta only because the plaintiffs 'presented no meaningful evidence on market dilution at all'
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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

AI found a new antibiotic: an MIT deep-learning model screened over 107 million molecules and discovered halicin

In Cell (2020), an MIT/Broad team trained a deep neural network to predict antibacterial activity; it discovered halicin from the ~6,000-compound Drug Repurposing Hub, a molecule structurally divergent from conventional antibiotics that killed a broad spectrum of resistant pathogens and cleared C. difficile and pan-resistant A. baumannii in mice, and from a >107-million-molecule ZINC15 screen it returned 23 candidates of which 8 were confirmed antibacterial.
Process Automation & AI-Led Ops
pending

ICO: Royal Free NHS Trust broke data-protection law sharing 1.6 million patient records with Google DeepMind for the Streams kidney-injury app

On 3 July 2017 the UK Information Commissioner's Office ruled that Royal Free London NHS Foundation Trust failed to comply with the Data Protection Act when it handed the records of around 1.6 million patients to Google DeepMind to build and test Streams, an acute-kidney-injury alert app; the regulator found patients would not reasonably have expected this use and required the Trust to sign a remedial undertaking.
Process Automation & AI-Led Ops
verified

Kadrey v. Meta: a court called LLM training on pirated books fair use, but only because the authors failed to prove market dilution

On June 25, 2025, Judge Vince Chhabria granted Meta summary judgment that training its Llama models on thirteen authors' books was fair use, but he wrote that the result turned on the plaintiffs' failure to develop a 'market dilution' record and warned that in most cases training on copyrighted works without permission will likely be infringing.
Process Automation & AI-Led Ops
verified

A federal court held judicial immunity applies even if a judge 'relied wholly on artificial intelligence' to issue a ruling: Phillips v. Parlade, D. Nev., Aug. 12, 2026

On August 12, 2026 the U.S. District Court for the District of Nevada (District Judge Gloria M. Navarro) dismissed Phillips v. Parlade with prejudice, in Case No. 2:25-cv-01464-GMN-NJK. A pro se plaintiff had sued a Nevada state family-court judge, arguing that judicial immunity should not apply because she 'unlawfully delegated her official decision-making duties when she relied wholly on artificial intelligence to issue a judicial ruling, without any discretionary human thought.' The court held that issuing a judicial ruling is a normal judicial function and that immunity applies even if that allegation were true, so the suit could not proceed in federal court. Critically, the court did NOT find that the judge used AI; the immunity holding is a matter of law reached without deciding the AI-use question. Every quote is taken verbatim from the court's own Order.

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