AI Readiness Check
A bounded examination of where AI is used, what it touches, and which duties already apply. You keep the scorecard report.
advisory
I make AI in organisations compliant and production-ready — for companies and for the public sector. Independent of vendors, EU-native from Vienna.
Four ways to work together — from a bounded examination to a standing role. Each ends in a written artifact you keep and own.
A bounded examination of where AI is used, what it touches, and which duties already apply. You keep the scorecard report.
Training for the AI Act literacy duty, plus the evidence package that proves who was trained in what.
Phased work that ends in a decision, not a deck: the roadmap, the target architecture, the governance design, or the FRIA dossier.
A standing technical authority on retainer: architecture and governance decisions, with no vendor commissions.
Almost every organisation now uses AI somewhere. Very few can show what it changed. The distance between those two sentences is the whole job — and since February 2025 the AI Act makes part of closing it a duty rather than a good intention. The deadlines that carry money are already dated.
of organisations already use AI in at least one function
McKinsey State of AI 2026
report a measurable effect on earnings — unchanged year over year
McKinsey State of AI 2026
of enterprise GenAI pilots show no measurable P&L impact
MIT Project NANDA
of agentic-AI projects get cancelled by end-2027 — cost, unclear value, missing risk controls
Gartner, June 2025
maximum AI-Act penalties for prohibited practices — above GDPR’s €20M / 4%
Reg (EU) 2024/1689, Art. 99
deployer duties and the FRIA fall due for Annex III systems — evidence takes 12–18 months
Omnibus Reg (EU) 2026/1744
A documented inventory of AI in your organisation — including the shadow AI your staff already use — with a risk class per use.
AI literacy is mandatory since February 2025; GDPR has applied to AI processing all along. Both are made demonstrable, not just discussed.
Strategy ends in an architecture decision and a first production-style use case with its own eval suite — not another deck.
both entrances end in the same offers — go to the catalogue
Eleven topics, each framed as a decision, with the figures and their sources in place.
open →03Three bounded formats and one standing role — entry prices, deliverables, engagement shape.
open →04Published tools you can open and run, and the builds that collapse the time they take.
open →05Five questions to place your organisation honestly, then the 60-minute first call.
open →Advisory work lives or dies on specificity. This band is written like the assessments I deliver: every topic framed as a decision, every figure sourced in place, every engagement end stated before the first call. Where the rest of kmail.at is the editorial voice, this band is the evidence voice — deliberately sharper, because the people who read it are procurement, Audit, IT-Leitung and Datenschutz — the readers who check.
Regulation (EU) 2024/1689 applies to you if you place AI on the market or deploy it in the EU — regardless of where the provider sits. The duties are staged, and each stage has a date.
I classify your uses (Annex III screening), map deployer duties (Art. 26) and the FRIA (Art. 27) where they are due, and produce the evidence file: inventory, risk classes, literacy records. Where a named tool helps understand the law, it already exists — built by me, open source.
Article 4 puts an AI-literacy duty on providers and deployers: the staff who operate AI systems, and the people affected by them, must reach a sufficient level of understanding. It has applied since 2 February 2025 — and it is the one obligation that reaches every AI use, not only the high-risk ones.
I run the programme role by role — what a manager, a developer and a caseworker each actually need — and produce the artefact an audit asks for: who was trained in what, when, with which materials. The teaching is the smaller half; the documented coverage is the deliverable.
Every AI use that touches personal data runs in parallel through the GDPR: lawful basis, purpose limitation, DPIA where high risk, processor contracts, transfer rules. Non-compliance costs up to €20M or 4% of turnover.
I translate GDPR principles into deployer checklists that survive contact with reality: what to document per use case, when a DPIA is triggered, how AI-Act and GDPR duties overlap and where they differ — so one file answers both regimes.
Sovereignty is not one thing: model weights, inference compute, embeddings of your corpus, and the raw documents each live somewhere — and each location carries a different exposure.
I build the location map first, then the posture: what stays in-house, what leaves to a processor, what is masked before it leaves. The slider model below shows the decision logic I put in your governance file.
everything leaves: raw text, embeddings, context
fits: non-sensitive general work
Fig. F — the sovereignty ladder: five postures between cloud frontier and air gap; the engagement ends with your organisation placed on it.
Open-weight models (llama.cpp ecosystem, quantised, MLX on Apple silicon) now cover most assistant, retrieval and extraction workloads without sending a byte anywhere.
I size the workload to your hardware, pick the model class, and hand over a running stack you operate — with the eval suite that proves it answers well enough for your use, before anyone signs off.
The corpus you already own — files, wikis, archives, PDFs — becomes a queryable knowledge base: chunked, embedded, indexed for retrieval with citations back to the source.
Retrieval-first: I build the curation pipeline (what enters, how it is chunked, where it is indexed) and the citation layer. Every answer names its source, or the system does not ship.
Embeddings are how your content becomes machine-matched meaning: model choice, dimension, language coverage, update strategy and index re-builds silently bound retrieval quality.
Model selection against your language mix (German legal/administrative text behaves differently than English marketing pages), re-indexing policy, and the retrieval metrics that tell you the layer is healthy.
Most organisational knowledge is stuck in document form: PDFs, scans, forms. Modern document AI parses layout, tables, formulas and reading order into structured, model-ready text.
I run the real pipeline — Docling-class parsing into structured output, quality checks per batch, then the parsed corpus flows into the knowledge base. Sensitive documents are parsed on local hardware only.
this topic runs as stage 01–02 of the knowledge pipeline — see Fig. C under topic 05.
AI-assisted analysis turns the organisation’s own operational data into trend lines, segment statistics and anomaly flags — with the statistics kept honest (samples, uncertainty, what is not in the data).
Pipelines for recurring analysis: cleaning → feature building → model-assisted interpretation → a written result. The deliverable is the dashboard or report you rerun, not a one-off slide deck.
When strong models are cloud-only, the disciplined route is masking: names, identifiers and sensitive fields are removed or pseudonymised before a prompt leaves, and re-linked in-house on return.
I design the masking pipeline and its rules per data class — what is safely maskable, what must stay local — and wire it so the anonymisation step cannot be skipped under deadline pressure.
The production answer in most DACH organisations is not "cloud or local" — it is a routing layer: private data to local inference, general work to cloud models, with fallbacks and an audit trail of which request went where.
I design the routing policy (classification per data type), the fallback behaviour when either side degrades, and the observability that shows cost, latency and data-exposure per route. The flow below is the decision core.
All regulatory dates as of October 2026: Art. 4 literacy in force since 2 Feb 2025, Art. 50 transparency applied since 2 Aug 2026, Art. 26/27 (Annex III) due 2 Dec 2027 per Omnibus Regulation (EU) 2026/1744. Figures carry their sources in place; the underlying research files with primary links are kept and can be shared on request. This page describes advisory services, not legal advice.
Agentic systems designed around tools-in-a-loop, context engineering and memory — the difference between a demo and a system that runs unsupervised.
Eval suites and LLM-as-judge patterns that turn "it seems good" into a measurable, reviewable quality statement — my own tools grade themselves.
Retrieval over your own corpus: RAG, knowledge graphs and organizational memory — sovereign by design, on infrastructure you control.
Obligation mapping with dates and sources: risk classes, deployer duties (Art. 26), the FRIA (Art. 27), AI literacy (Art. 4) — reconciled with GDPR practice.
Fixed-price, written, procurement-compatible formats. Austrian and EU public bodies have specific doors; I work through the real ones.
Published, open-source tools — from a terminal EU-AI-Act assistant to a local-LLM toolbox — whenever a bounded artifact beats a slideware answer.
Behind it: a career spent at the seam of digital systems and the built world, 26 verified source-based articles, hands-on agent architecture and a published toolchain. The point is not the list — it's that every claim on this page traces to something you can open and read.
Three bounded formats and one standing role. Every engagement ends in a written artifact you keep and can act on without me — that is deliberate. Prices are entry anchors for orientation; the real number follows the scope call.
The entry format and the exit from uncertainty. One bounded examination of where AI is used, what it touches, and which of the EU AI Act and GDPR duties already apply.
You receive: a scorecard report — AI inventory with a risk class per use, shadow-AI findings, action list prioritised by exposure — written so it stands on its own, without me in the room.
public sector: fixed price for administrations, 15 working days
The training itself is the smaller half. The sellable half is the documented coverage: who needs what depth, who was trained, when, with which materials — for obligations in force since February 2025.
You receive: the evidence package — role-specific curriculum, participation records, a policy snippet naming who was trained in what. That artifact is what an audit asks for.
public sector: session rates for open cohorts on request
Strategy that ends in decisions, not decks — a phased roadmap, a target architecture (including the local/sovereign posture), governance with a named owner and a register. Bounded tool builds fit here as an option when a named artifact — a internal assistant, an eval rig, an analysis pipeline — is the deliverable.
You receive: per phase, one fixed artifact — the roadmap, the target architecture decision document, or the governance design. For administrations: the FRIA dossier, ready for supervisory review.
public sector: FRIA package (Art. 27): 2–4 weeks, fixed
For organisations past the first build: a standing technical authority who owns the eval bar, reviews vendor claims, and keeps governance alive as models and duties shift (Art. 26/27 from December 2027).
You receive: steady architecture and governance decisions on retainer — fixed monthly days, documented decisions, no licence resale, no vendor commissions.
public sector: procurement-safe retainer wording on request
Prices exclude VAT and travel. This page describes services, not legal advice; regulatory statements reflect the law as of late 2026, with sources on request.
Client-identifying case studies are published only with written approval; the pattern cases above describe the engagement shape without client data.
Published, open tools — not concepts. Everything here is installable or runs at kmail.at right now.
terminal AI coding agent
citeable EU-AI-Act terminal assistant — 180 recitals, 113 articles, grounded local RAG
author, version, validate ISO 19650 BIM execution plans
image→ASCII art, fully in the browser
Mermaid code → live graph render
the full llama.cpp toolbox as a desktop GUI, MLX on Apple silicon
browser-side hardware detection, 104 open-weight models scored
Two kinds of time saving exist, and mixing them is how AI projects end in disappointment. Assistance speeds a task you still perform — measured honestly, a quarter to a half of the invested time. Delegation hands a whole bounded task to a tool and collapses it — hours of an afternoon to minutes. That second regime is where “up to 90%” belongs, and the honest way to claim it is to build the tool and measure it. That is what these engagements do.
time on realistic consulting tasks with AI — and +40% graded quality
Dell'Acqua et al. 2023 · 758 consultants, RCT
invested time on professional writing tasks, quality up, not down
Noy & Zhang · Science 381 (2023) · n = 453
completion time on a real coding task with an AI pair programmer
Peng et al. 2023 · 95 developers, RCT
manual transcription cost per hour of audio, collapsed to machine minutes
industry transcription-time guidance (Rev)
The catalogue is deliberately exhaustive — every line below is a build pattern, not a software SKU: the artifact differs per organisation, the loop (Fig. J) does not. Bounded, versioned, owned, measured — each one ships with the number that says what it actually saved.
contracts, briefs, SOPs, policies, technical documentation — drafted from your templates and your corpus, reviewed by a human, version-controlled
slide decks generated from a written outline or a data pull — brand tokens applied, speaker notes included, regenerate on every source change
transcript → decisions, action items with owners, the formal minutes your governance requires — produced before everyone leaves the room
weekly ops reports, management summaries, monitoring narratives — assembled from live metrics, delivered on schedule, no compile-by-hand
structured extraction from sites and portals — price lists, tenders, registries — deduplicated, differential against the last run, delivered as data
multi-step processes across systems wired as n8n-class pipelines — trigger, transform, notify — with retries and an audit trail
ingest → clean → validate → load jobs that rerun forever; schema drift caught at the gate, not in the meeting after
the repetitive glue between humans and systems: routing, reminders, follow-ups, filing — quietly eating the 15-minute tasks
executive and team dashboards computed from your own operational data — refreshed automatically, one definition per metric, no numbers fought over
first drafts of recurring correspondence — status updates, invoices cover notes, tender replies — in your tone, from your facts
the harness that grades every other build: golden sets, regression runs, LLM-as-judge with a reviewable scorecard
PDFs, scans and forms → machine-readable structured data (Docling-class) — feeding the reports, dashboards and knowledge bases above
retrieval over your own corpus with citations to the source artifact — the answer names its evidence, or it does not ship
reviewed code generation for the boring half of the backlog: migrations, boilerplate, glue, tests — PRs, not vibes
rosters, resource plans, capacity scenarios — generated as draft decisions, decided by humans, recalculated in seconds when reality moves
log and alert triage reduced to what changed and who should care — noise collapsed before it reaches a phone
invoice extraction, matching, booking-prep and the forms around it — the paperwork layer of ERP, tamed
market, tender and competitor monitoring compiled from public sources into a written brief on a schedule — sources cited in place
The honest arithmetic: assistance buys a quarter to a half; delegation of a bounded task buys far more, and some tasks collapse almost entirely. Nobody honest promises 90% on every task — the engagement's first deliverable is the measured list of yours where it holds.
Five questions, two minutes, an honest reading of your organisation's AI position. Answering "we cannot say" more than once is itself a finding.
01Where does AI already touch your organisation?
Tools staff use on their own count too — shadow AI is the rule, not the exception.
02Can you state which of your AI uses fall under the EU AI Act — and at what risk class?
Prohibited, high-risk (Annex III), general-purpose, or out of scope.
03Have your staff received documented AI literacy training? (Art. 4 — in force since 2 February 2025.)
The Act sets no fixed curriculum, but the obligation and its documentation are live now.
04When an AI system errs or leaks data, what happens?
GDPR applies to AI processing of personal data; deployer duties under Art. 26 apply from 2 December 2027.
05Who owns AI governance in your organisation?
One named owner beats a committee without one.
A 60-minute call is the right first move for both segments: companies bring their inventory questions, administrations bring their duties. You leave with an assessment of where you stand and a concrete, priced next step — whether or not it is one of mine.