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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

Bartz v. Anthropic: a $1.5 billion settlement, the largest in U.S. copyright history, over pirated books used to train Claude

$1.5 billion class-action settlement - the largest copyright settlement in U.S. history
Minimum ~$3,000 per work across roughly 465,000-500,000 pirated books
Split summary-judgment ruling: training on books held fair use, but downloading and keeping pirated copies denied fair use
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

Telefónica closes 2025 with 12 Level 4 autonomous-network use cases, and reports AI cut German network faults by 30%

On 18 February 2026 Telefónica said it had closed 2025 with 12 network use cases running at Level 4 autonomy (self-configuring, self-optimising and self-healing without human intervention), delivered jointly across Spain, Germany and Brazil, inside a program of more than 400 AI use cases in production. Two independent trade outlets reproduced the 12-Level-4 figure firsthand, and an independent Omdia analyst brief (05 March 2026) assessed the program and reported the same twelve-use-case figure. Telefónica separately reports, on its own channels, that AI reduced network faults in Germany by 30% and that O2 Telefónica's AI energy features cut antenna-network electricity costs by around ten percent; those two figures rest on first-party reporting only and are recorded here with that limit.
Process Automation & AI-Led Ops
verified

Bartz v. Anthropic: a $1.5 billion settlement, the largest in U.S. copyright history, over pirated books used to train Claude

After Judge William Alsup held in June 2025 that training AI on books was fair use but downloading them from pirate libraries was not, Anthropic settled Bartz v. Anthropic for $1.5 billion - a minimum of about $3,000 for each of roughly half a million pirated books - and a Northern District of California court granted final approval in July 2026, making it the largest copyright settlement in U.S. history.
Process Automation & AI-Led Ops
verified

When an AI denial tool reaches federal court: in Lokken v. UnitedHealth, the breach-of-contract claims over 'nH Predict' survive Medicare preemption

A putative class action in the U.S. District Court for the District of Minnesota, Estate of Gene B. Lokken et al. v. UnitedHealth Group, No. 0:23-cv-03514, alleges UnitedHealthcare used the naviHealth 'nH Predict' AI model to deny post-acute care to elderly Medicare Advantage enrollees, overriding treating physicians. On February 13, 2025, Judge John Tunheim granted in part and denied in part UnitedHealth's motion to dismiss: he dismissed most claims as preempted by the Medicare Act but allowed the breach-of-contract and breach-of-implied-covenant-of-good-faith claims to proceed, on the reasoning that deciding them requires only asking whether UnitedHealth followed its own coverage documents (which say decisions are made by clinical staff and physicians) when it allegedly used AI. Plaintiffs allege the model has a 90% error rate; UnitedHealth denies the tool is used to make coverage determinations. The error rate is a contested allegation, not an adjudicated or independently measured figure.
Process Automation & AI-Led Ops
pending

A German court says you own what your chatbot says: OLG Hamm holds a clinic liable when its AI invented specialist titles

An aesthetic-medicine clinic's website chatbot told visitors its two managing directors were 'specialists in plastic and aesthetic surgery' — a qualification they do not hold, using titles that in two cases do not exist. On 12 May 2026 the Higher Regional Court of Hamm ruled the chatbot's answers were the company's own commercial acts, ordered it to cease and desist under a fine of up to 250,000 euros per violation, and rejected the defence that an autonomous AI is a 'third party' the operator cannot be blamed for. The decision is not final; the court allowed an appeal to Germany's Federal Court of Justice.

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