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.
Klarna's AI customer-service assistant: the 2024 numbers and the 2025 walk-back
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.
Stop guessing.
Verified before/after numbers from real engagements, with the client confirmed and the method on record.
Proof beats marketing.
Turn a great project into a verified, citable asset that ranks in search and gets quoted by AI answer engines.
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.
Independently validated
We re-research the story and check every figure against primary and independent sources before it can go green.
Evidence on record
Every number is backed by something checkable: a followable source, before/after metrics, documentation. No adjectives.
Method published
How each story was verified ships on the story itself. The record is public, not a claim in fine print.
03Latest case files
The registry starts here. Each entry is one verified outcome.
Klarna's AI customer-service assistant: the 2024 numbers and the 2025 walk-back
Klarna's OpenAI-powered assistant handled two-thirds of customer-service chats in its first month (Feb 2024) and was said to do the equivalent work of 700 full-time agents, a modeled equivalence, not 700 layoffs. In 2025 CEO Sebastian Siemiatkowski said the cost-driven push had produced 'lower quality' and Klarna began re-recruiting human agents. Every 2024 efficiency figure originates with Klarna and is reported here with its source.
verifiedJPMorgan's COIN: the famous 360,000-hours figure, and why it was never independently measured
JPMorgan built COIN (Contract Intelligence), an in-house machine-learning system that interprets commercial-loan agreements in seconds, work that Bloomberg reported in 2017 had consumed 360,000 hours a year by lawyers and loan officers. The figure is one of the most-cited numbers in enterprise AI, and it traces to a single 2017 Bloomberg article attributing it to JPMorgan's own designers. It has never been independently measured or updated.
pendingMango's first fully AI-generated campaign: Sunset Dream, 95 markets, and no public performance number
In July 2024 Mango ran what it calls its first campaign generated entirely with generative AI, for the Sunset Dream collection of its Mango Teen line, live in 95 markets. The pipeline was human-in-the-loop and disclosed: real garment photos trained a model, then Mango's art team retouched and mastered the output. No performance metric for the campaign is public, and Mango's record H1 2024 revenue is concurrent context, not a measured result of the campaign.
verifiedModerna's company-wide ChatGPT Enterprise rollout: 750 custom GPTs, from a vendor case study
Moderna deployed OpenAI's ChatGPT Enterprise company-wide in April 2024 and reported 750 custom GPTs within two months, 120 conversations per user per week, and 100% adoption in its legal team. The engagement metrics come from OpenAI's own provider-produced case study and are corroborated only in outline by independent press. They are unaudited, and the clinical 'Dose ID' tool is a pilot, not a validated result.
verifiedOctopus Energy's 'Magic Ink': AI that drafts support emails, with no job cuts
Octopus Energy's Kraken 'Magic Ink' drafted customer-service email replies with a human reviewing and sending each one. By end-April 2023 CEO Greg Jackson said it handled 34% of customer queries, 'the work of 250 people in the UK alone', at an 80% satisfaction rating versus 65% for humans, while the company said there would be no job cuts and announced thousands of new hires. The satisfaction figure varies across statements and is reported here with each source.
verifiedOtto's autonomous stock ordering: a deep-learning system that buys inventory on its own
German retailer Otto uses a Blue Yonder deep-learning system, built on an algorithm that originated at CERN, to forecast demand and automatically order stock. The Economist reported in 2017 that it predicts with 90% accuracy what will sell within 30 days, auto-orders around 200,000 items a month with no human intervention, cut surplus stock by about a fifth and reduced returns by more than 2 million items a year, and that Otto hired more people rather than firing any. The figures are 2017-vintage and unaudited.
pendingPZU and Tractable: from a detailed review of 20% of body-shop claims to nearly all, in real time
PZU, the largest insurer in Central and Eastern Europe, deployed Tractable's computer-vision AI to review motor-damage claims from body shops. Before AI it reviewed in detail about 20% of those claims; the AI lets it check nearly all of them in real time, out of roughly 500,000 motor claims a year. By the November 2020 announcement the system had handled over 150,000 claims, worth PLN 1.3 billion. The figures are vendor-issued and 2020-vintage.
pendingZalando's 2018 'algorithms replace 250 marketing jobs': the headline, and the hiring plan it left out
In March 2018 Zalando said it would replace 200 to 250 marketing roles with algorithms and AI, a line cited for years as a clean AI-for-layoffs example. The honest retrospective: the same announcement carried a plan to hire 2,000 more people, Zalando had just grown from 12,000 to 15,000 staff in 2017, and total headcount kept rising for years after. It was a mix-shift toward technical roles, not a net workforce cut.
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.