Thank you, ITSA Nürnberg

The story behind AIGIS.ONE

AIGIS grew out of a simple observation: in complex organizations, requirements, contracts, security specifications, compliance rules, technical documentation and source code often exist side by side – but are rarely truly connected to one another. The larger projects become and the more stakeholders, suppliers and …

Nobody trusts their AI. And they are not wrong.

Why we should only listen to an AI once it has to say where it got its information!

### The distrust is justified Anyone who works with AI today has experienced it: you ask about a contract clause, and the answer comes immediately, neatly structured, in the right tone. Except that the clause does not appear anywhere in that form. A section number is mixed up, a deadline is invented, an obligation …

546 applications. And one of them tells your AI whom to hire.

Injection attacks have accompanied IT security for decades: injected content is processed by the system as an instruction. With command injection, a vulnerable application can be made to execute operating system commands determined by someone else – so-called arbitrary commands. With SQL injection, injected database …

Teaser2: Two cases, one pattern: the contradiction, before it becomes expensive.

Teaser 2: a new supplier and a fleet insurance policy — how it works today and how it works with AIGIS. (Video in English)

After our first trailer, we got a fair piece of feedback: show us a whole workflow, from start to finish — and where AIGIS saves work today. Here it is: two cases in two and a half minutes, each one “Today — and with AIGIS”. Case one: a new supplier. Framework agreement, purchasing policy, security requirements — with …

The Price of a Token II - Regulatory Perspective

When regulation becomes a location factor for AI

With "The Price of a Token – Study II: Regulatory Perspective", AIGIS is publishing the second part of its research series on the actual conditions governing AI production and AI value creation. While Study I examined the economic fundamentals behind an AI token, Study II turns its attention to the regulatory level. …

KI ist digital. Ihre Produktion ist es nicht.

The Price of a Token – Study III: Environmental Perspective

KI ist digital. Ihre Produktion ist es nicht. Hinter jedem Token stehen sehr reale Dinge: Stromnetze, Wasser, Kühlung, Beton, Stahl, Grundstücke, Genehmigungen, Notstrom, Logistik und Redundanz. Wer über KI-Infrastruktur spricht, sollte deshalb nicht nur über Modelle, GPUs und Skalierung reden – sondern über die …

When we talk about AI productivity – what do we actually mean?

The Truth About AI Productivity

When we talk about AI productivity, what exactly do we actually mean by it? Or is it just the next buzzword that we keep using until nobody knows anymore what's really behind it? I keep being bothered by the idea that you just have to give employees a few LLMs, copilots and chat accounts and can then expect a …

AIGIS Use Case Example - " .. but it's all in there."

AIGIS - Japanese, Indian, Chinese, OCCAM, or just plain text. No translation needed. Just upload, and what really matters is put in front of you!

"It's all in there." A remarkable sentence. It can express transparency. Or the hope that nobody will put the decisive passages together. Because disclosing something and making it understandable are two very different things. Anyone who has ever read an extensive contract, along with its annexes, service descriptions …

The Price of a Token I - Economical Perspective

For our first public post, we therefore deliberately chose not to begin with a product presentation or a list of features, but with a question that we consider more fundamental: under what economic conditions can AI be produced and operated sustainably in Germany at all?

AIGIS officially went live on 1 October. For our first public post, we therefore deliberately chose not to begin with a product presentation or a list of features, but with a question we consider more fundamental: under what economic conditions can AI be produced and operated sustainably in Germany at all? With "The …

Acceptance and lost data: the clash before anyone pays.

Trailer 3: a line retrofit and a hosting contract — how it works today, and how it works with AIGIS, this time with a ready-made question and its result.

Trailer 3 takes on two stacks everyone knows: the project that runs on hundreds of pages of requirements, and the contract whose clauses only bite together. Just under three minutes, again “Today — and with AIGIS”. What's new: in both cases we ask a ready-made question of the checked requirements and show what comes …

Teaser1: For weeks we've been telling you about AIGIS. Now we're showing it.

Our first trailer: just under two minutes that show what AIGIS.ONE is and what it does.

For weeks we've been telling you about AIGIS on LinkedIn. You've read, commented on and shared our posts — thank you for that. One question kept coming up: "So what does AIGIS actually do?" Starting today, the answer takes just under two minutes: our first trailer. In it, you'll see how AIGIS.ONE reads requirements, …

Aigis as a tool for your AI assistant

Case study: a team checks a supplier's specification against its own requirements — without opening Aigis. The AI assistant it already works with talks to Aigis through the API.

This case study is real; every step in it is a call that the Aigis interface offers today. A mid-sized machine builder wants to outsource the maintenance software for its production lines. A supplier has sent its specification, 64 pages. Against it stand the company's own IT security policy, the group's data …

Project kick-off: order instead of sticky-note chaos

On day one of a project the wall is covered in sticky notes. It shows how much was thought — not what applies. Why a project starts better with a list than with a wall.

Day one of a project has a familiar picture: a room, a wall, hundreds of sticky notes. Yellow for requirements, pink for risks, blue for open questions. After three hours the wall is full, and everyone feels they have achieved a lot. The wall shows how much was thought. It does not show what applies. The notes carry …

Case study: a start-up consultancy makes Aigis its first step

Before a consultancy advises founders, it reads: articles of association, funding conditions, term sheet, terms and conditions, data protection. How one consultancy builds Aigis into that process — and what changes for it.

A case study. The company is invented; what Aigis does in it describes its current functionality. The consultancy supports companies from founding to market entry: legal form, financing, funding, sales. Twelve consultants, around forty mandates a year. Every mandate starts the same way — with a stack of documents …

Aigis in the company: who actually pays for the reading?

Before a project begins, experts spend weeks reading contracts, guidelines and requirements — paid for by day-to-day operations, shown on no invoice. A story about the project start and where the time really goes.

A contract has been won. On Monday everything that belongs to it is on the table: the contract, the client's requirement specification, their security requirements, the company's own guidelines on data protection and procurement, two standards the contract refers to, and a pile of e-mails that reveal what the client …

Afraid of AI? The question is put wrongly.

The machine replaces the human — a good image for headlines, a bad one for decisions. Where replacement is right, where it is not, and what a tool for professionals does instead.

The debate about artificial intelligence lives on one image: the machine replaces the human. It is a good image for headlines and a bad one for decisions. Telling skilled professionals that they are next produces exactly what it claims to fight: standstill out of fear. And whoever builds policy on it is badly advised …

Code Review 2026: Three ways. Same time. Different result.

Time doesn't disappear. It shifts between preparation, creation and rework.

Code Review in CI/CD Tools, reviews and tests check at defined points. That remains valuable. SAST, DAST, SCA and pentests provide evidence that no AI can replace. But each check only sees its own slice. None of them automatically answers the question of how everything works together, and answering it means …

Use case PROJECT: The requirement that existed four times

A two-year plant project, four documents, one requirement — and shortly before acceptance the question of which version actually applies.

A plant engineering firm, a two-year project, six parties involved: the client's requirement specification, the contractor's own functional specification, the specifications of three suppliers, and a safety concept added eighteen months later. Four weeks before acceptance the project lead asked a question that sounded …

Use case LEGAL: Two contracts that knew nothing of each other

A framework agreement, eleven call-offs, a service level and a data processing agreement — and a liability cap that reads two different ways.

An IT service provider and its largest client had worked together for four years: a framework agreement, eleven call-offs, a service level agreement from the second year, and a data processing agreement tightened after a privacy audit. Each document had been carefully negotiated on its own and reviewed by lawyers. …

Use case CORE: Read first, judge second — why the order matters

An inherited codebase, a review report full of findings — and a team that spent more time refuting than fixing.

A team inherited the software of a departed service provider: grown over seven years, thinly documented, in production. Before their first own release they ran a review over it. What came back was a report nobody wanted to read — pages of findings, some real, and among them plenty of places where the reviewer simply …