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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

JPMorgan Chase scaled its in-house AI assistant, LLM Suite, from zero to 200,000 employees in eight months

Employees onboarded to LLM Suite
0 (pre-launch, summer 2024)→~200,000 within ~8 months
Self-reported productivity gain per user (disputed; quoted inconsistently across outlets)
manual drafting and document analysis→no single figure: 3-6 hrs/week, 'an hour here and three hours there', or 10-20% on coding
AI use cases in production
n/a→450+
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.

Read the full verification standard →

03Latest case files

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

Process Automation & AI-Led Ops
pending

The 'jagged frontier' experiment: GPT-4 let 758 BCG consultants finish 12.2% more tasks 25.1% faster, yet made them 19% less likely to be right on a task outside AI's reach

In a pre-registered field experiment published in Organization Science, 758 Boston Consulting Group consultants using GPT-4 completed 12.2% more tasks 25.1% faster with higher quality on 18 tasks inside AI's 'frontier', but on one complex task chosen to sit outside it, consultants using AI were 19% less likely to reach the correct answer.
Process Automation & AI-Led Ops
verified

JPMorgan Chase scaled its in-house AI assistant, LLM Suite, from zero to 200,000 employees in eight months

JPMorgan Chase's proprietary generative-AI tool, LLM Suite, went from zero to roughly 200,000 onboarded employees within about eight months of its summer-2024 launch, a figure the bank states itself and two independent trade outlets report, alongside more than 450 AI use cases in production; the bank's own productivity estimates are self-reported and quoted inconsistently across outlets.
Process Automation & AI-Led Ops
verified

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.

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Proof beats marketing.

Bring a real engagement to the dojo and turn it into a verified, citable asset, or read what's already on record.

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