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AI-Native Way of Working

AI-native mindset: rethink the way you work.

Making today's work faster is the easy win, and the smallest win. The real question isn't what you can do with AI. It's what AI forces you to rethink. We help you find the parts of your business built on the assumption that intelligence was scarce, and redesign them, before a competitor does it to you.

Get help with your AI-native shift
AI-Native Engineering
Get help with your AI-native shift
AI-Native Engineering
ML6 Berlin October 2025-35

It's not a faster old process.
It's a different process.

    Don't make the mistake of thinking that an AI-native way of working is just about efficiency gains and lower labor costs.

AI-assisted work is you doing things the old way, and occasionally asking a chatbot for help. AI-native work throws out the assumption on which the whole process was built: that intelligence is scarce and expensive. An AI-assisted worker asks "How can AI help me do my job?" An AI-native worker asks "Given what AI can do, what should my job be?"

AI-assistedAI-Native Way of Working
The workflowYou keep the same steps you always had and reach for AI whenever a task gets tedious.You redesign the whole process around what AI can now do, so entire steps get restructured or disappear.
Your roleYou still do the work yourself and treat AI as a helper on the side.You set the goal, context, and quality bar, then direct the AI and agents while you focus on judgment.
ContextYou re-explain the background in every prompt, because nothing you said yesterday carries over.You engineer context once into shared docs, prompts, and skills that your people and agents both reuse.
Order of workYou build first and document it later, if you ever get around to it.You frame the intent and the spec first, then let that drive everything that gets built.
Best practicesQuality and standards get bolted on late at review, and they vary from one person to the next.Your standards are built into the workflow from day one, so everyone works to the same bar.
Where the gain landsA few people get a little faster, but the gains stay local and hard to measure.The whole system speeds up, and the gains compound across teams and show in business results.
14y

AI EXPERTISE

400+

ENTERPRISE AI PROJECTS DELIVERED

9/10

AVERAGE NPS SCORE

140+

AI-NATIVE EXPERTS

12wks

AVERAGE TIME TO VALUE

Most partners tell you you should transform. We rebuilt ourselves first, so you benefit from the insights.

 

    "We'll put that on the IT roadmap for next year." Well—not in our house! ;-)

We tested AI-native working on our own delivery, found exactly where today's tools break, and rebuilt around them. We're now on our third wave of building AI-native apps. So you don't get a slide about transformation; you get the playbook that already survived contact with reality. And this isn't just an Engineering thing. Our Sales, Finance, HR, Marketing and Operations teams are building their own tailored, governed, and secure apps in days, not months. All tailored to our specific organizational DNA. 

ml6-ai-native-evolution_v4

 

  • Wave 1: Assisted

    Proving it works. We started doing GenAI production in 2019. We learned where the value was real and where it was only hype.

  • Wave 2: Integrated

    Weaving it into delivery: a two-speed engineering model and a shared context layer, so speed doesn't come with silent debt.

  • Wave 3: Now

    Redesign the work itself: agents, orchestration, and a default-to-AI operating model. This is the playbook we bring to you.

    AI-Native Engineering
ML6 vibecoding

Validate the idea before you build it.

AI lets us prototype the real, working experience in days, not months. You lock the concept while it's still cheap to change, so engineering budget only starts once the direction is proven. Earlier feedback, faster time to market, far less rework.

  • Design discovery

    We map the user journeys and pin down the core AI requirements with you, in collaborative workshops rather than a requirements doc thrown over the wall.

  • AI prototyping

    We auto-build high-fidelity, interactive prototypes so you can validate the logic and experience in real time by clicking through the real thing rather than a static mockup. The result? Near-zero engineering spend until the concept is clocked.

  • Implementation

    Only once the concept is locked do we translate the validated prototype into clean, production-grade code. No throwaway work, no guesswork.

CD. iTunes. Spotify.
Which one are you?

Every incumbent that digitised its old model instead of redesigning it got replaced by someone who started from what the technology made possible. The 4 risks that can happen right now. 

hand-holding-phone-displaying-music-service-logo-2026-03-17-04-00-32-utc
  • Pilot sprawl

    Dozens of experiments, no north star, no compounding advantage. Busy, but going nowhere in particular.

  • Speed on hidden debt

    AI coding buys velocity while maintainability falls, security controls erode quietly, and juniors stop learning the architecture.

  • Fragmented knowledge

    The number-one scaling barrier for 60%+ of firms. Isolated tools, loose-cannon sandboxes, no shared context.

  • People push back

    Most AI investments fail for organisational reasons, not technical ones: distrust, fear for jobs, weak change management.

Going at alone / Generic consultantsWith ML6 and deep AI engineering
ResultsYou run scattered pilots that never compound.You get a north star that turns every bet into a compounding advantage.
SpeedYou buy speed and inherit hidden technical debt.You ship faster on a baseline that keeps quality and security intact.
ApproachYou get a strategy deck and a handshake.You get a playbook we've already run on ourselves, three waves deep.
ContextYour tooling fragments across every team.You get one context layer your people and your agents both read.
Change managementYour people quietly resist the change.Your people feel empowered to build solutions for their frustrations.

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Nick Limère 4
Nick Limère AI Advisory Lead

Let's define your AI-native shift, and build it.

Twelve weeks to your first tangible results. Let's find the parts of your operating model built on scarce intelligence, and redesign them before a competitor does.

Let's schedule a talk
Nick Limère 4
Nick Limère AI Advisory Lead