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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

Bank of Korea (Issue Note 2026-12): AI adoption cut work time 3.8% but the productivity gain is near zero

Average work-time reduction from AI adoption
No AI adoption→3.8 percent, about 1.5 hours per week
Relationship between time savings and actual output growth
Expected productivity gain→Essentially zero (implied potential gain ~1.0 percent)
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

AlphaFold: independently ranked the top method in the CASP14 blind assessment (summed z-score 244 vs 91 for second place), scaled to 214M+ predicted structures, and won the 2024 Nobel Prize in Chemistry

In CASP14 (2020), a blind protein-structure-prediction assessment run by independent academic organizers, Google DeepMind's AlphaFold was ranked first by a wide margin — a summed z-score of 244.02 against 90.82 for the second-placed group — and reported a median score of 92.4 GDT across all targets. Its predictions, released through the EMBL-EBI-hosted AlphaFold Protein Structure Database, now cover over 214 million protein sequences, and in 2024 Demis Hassabis and John Jumper won the Nobel Prize in Chemistry 'for protein structure prediction'.
Process Automation & AI-Led Ops
verified

Bank of Korea (Issue Note 2026-12): AI adoption cut work time 3.8% but the productivity gain is near zero

In a June 2026 Issue Note the Bank of Korea found that generative-AI adoption reduced average work time by 3.8 percent, about 1.5 hours per week, yet the relationship between those time savings and actual output growth was essentially zero, implying a potential productivity gain of only about 1.0 percent.
Process Automation & AI-Led Ops
verified

Court of Rome annuls the Garante's €15M ChatGPT fine against OpenAI on one-stop-shop jurisdiction (R.G. 4785/2025, 2026)

In November 2024 Italy's data-protection authority fined OpenAI €15,000,000 over ChatGPT and ordered a first-of-its-kind six-month public information campaign; on 18 March 2026 the Court of Rome annulled the whole decision, ruling the Garante had lost jurisdiction under the GDPR one-stop-shop once OpenAI's Irish establishment became its lead authority before the final decision. This case file records the figures from the Garante's own press release the annulment quoted verbatim from the signed Rome judgment (retrieved this session), and the docket number and reasoning-publication date corroborated by two independent legal-trade sources.
Process Automation & AI-Led Ops
verified

The AI shopping app that ran on people: DOJ and SEC charge Nate's founder over an 'effectively zero percent' automation rate

On 9 April 2025 the U.S. Attorney for the Southern District of New York indicted Albert Saniger, founder and former CEO of the shopping app Nate, for defrauding investors about its AI; the indictment alleges that while Nate claimed to complete online purchases with AI 'without human intervention', the app's actual automation rate 'was effectively zero percent' and hundreds of contractors in a Philippines call center completed the purchases by hand. The SEC filed a parallel civil action the same day, alleging Saniger raised over $42 million on the AI claims.

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