Nima Labs · Building AI-first organizations

Your team provides the goal and the creativity.
Your company AI simply executes.

AI-first means one entry point. Your team opens one AI system, enters the goal, and the system does the work: research, assets, sequences, publishing, in the tools you already use. Your team checks the result, asks for changes, and releases it.

Build your AI-first organization See what it looks like when it runs

We check the fit first. You get a yes or a reasoned no.

Sound familiar?

AI is rolled out. Everyone is as busy as the day before.

01
Every task still starts with someone opening a chat window.
Ask a question, get an answer, copy it into the next tool. The AI does one step. The other nine are still done by hand.
02
Nothing runs unless someone starts it.
The morning briefing, the follow-up, the repurposing, the weekly report. If nobody remembers, nothing happens.
03
Your people work on prompts instead of on results.
Twenty people, twenty ways of doing it. The output depends on who did it. Nobody gets a finished result to check, because there is none.

Three symptoms, one cause. You bought tools and left the work as it was. Same process, better tools, same result.

95%

of enterprise GenAI pilots produce no measurable P&L impact.

MIT NANDA, The GenAI Divide: State of AI in Business 2025

AI-first is a higher bar than “we use AI”.

Every recurring task starts in an AI system and runs there until a result is ready.

A person then judges, iterates, and releases it, once the result is what they wanted.

Two ways it runs

1
On a schedule.
The daily briefing, the weekly digest, the monthly report. They start on their own at a set time and arrive finished.
2
When you ask.
A campaign idea, a new topic, a competitor move. You enter the goal, the system does the work, and you move on to the next thing.

Count the tasks in your company that run like this today. In most companies, the number is zero.

What the AI works from

Every organization brings these, or we build them first. Without them, the output is generic. With them, it sounds like you and aims at the right people.

01
Positioning
What you stand for, in one sentence everyone repeats.
02
Messaging
The core messages per audience, written down and versioned.
03
Tone of voice
How you sound, with examples and with the phrases you never use.
04
Brand and design guidelines
Logo, colors, type, templates, so every asset looks like yours.
05
Goals and targets
What the next two quarters have to deliver, in numbers.
06
ICP and personas
Who you sell to, who decides, and what they care about.
07
Rules of engagement
Who works which account, how often, on which channel, and when to stop.
08
Playbooks and data model
How each channel runs, and what every field in the CRM means.

If something is missing, we create it in the Discovery. All of it lives in one place: the knowledge base the AI reads from.

Works with the tools you already have

One entry point: one AI system that runs everything. Claude, OpenAI, or Gemini, it does not matter which. Behind it, your tools stay your tools.

We add a new tool only when a process cannot run without it. In most cases, what you have is enough.

What it looks like when it runs

One entry point. A person opens the AI, enters the goal and the context, and the system does the rest. What comes out is what that person wanted. Where the tools allow it, it goes live right away. Six scenarios, all of them built. The numbers under each one come from the build.

One entry point, from idea to live on every channel

Imagine a founder who just had a good customer call. She has one idea from it and 20 minutes to spare. So she opens Claude, pastes in the transcript, and writes a short instruction: turn this into an article for marketing leaders, and the point I want to make is that nobody can defend AI licenses if nobody measured anything before.

The system takes it from there. It works out who the article is for and what it is supposed to do, and it pulls what it needs from the company’s knowledge base:

who the target customers are and what they care about
how the company sounds, and which words it never uses
which facts and numbers it is allowed to use, with their sources

Then it writes the article, a LinkedIn post, and a short section for the newsletter, checks all three against the writing rules, and comes back with the drafts and a note about the one thing it was not sure about.

The founder reads the article, sends it back with two comments, gets a new version, and gives it the green light. From there, everything happens on its own:

the article goes onto the website, with the FAQ block that search engines read
the post is scheduled on LinkedIn
the newsletter section is saved as a draft in the email tool
14 days later, the system asks her to check how the article did

Behind that one door are 21 skills and 6 agents doing the work.

Built withClaude, HubSpot, LinkedIn, the website. Built for our own product, Mint, and before that inside a B2B company with more than 60 AI seats.

How it runs

Seven stations, every process, every time. Station five is a person.

Entry point

Something starts the process: a person with an idea, an event, a signal in the CRM, or the clock. The last two are what changes a company, because nobody has to remember them.

What stays on your side when we leave

One entry point
The orchestration
A skill and agent library in your tone of voice
A machine-readable knowledge base
Clear points where a person decides
One number per process
One or two internal builders

Your company’s AI-first system runs within the first month.

We start small. One part that runs end to end, with its effect on record, convinces a team more than five half-finished ones.

1 to 2 weeks

Discovery

Where you stand, where you want to go, which processes matter most. A baseline for each.

within the first month

First build

The first AI-first part goes live: one entry point, the first processes running the agentic way, with our best practices, a person in the loop, and a number.

about 3 months

Full build

The rest of the processes, in order of impact. Your people trained to own them. Running without us.

ongoing

Retainer

After the handover. New processes, changes, measurement.

Who this is for

We build the jump from assisted to agentic. That needs a certain degree of digitalization, and it needs leadership buy-in.

Your processes exist and repeat.
Daily, weekly, monthly. What never runs the same way twice cannot be handed to a system.
Your data lives in systems.
A CRM, a CMS, a place where the numbers are. Inboxes and spreadsheets are the step before this one.
AI is already in use.
Tools are rolled out, and part of the team works with them every day.
Leadership wants it and says why.
More coverage, more speed, better quality. If nobody says why, people assume the worst.
One entry point works for you.
One AI system that runs everything. Claude, OpenAI, or Gemini, it does not matter which.
Someone in-house builds with us.
One or two people who take over when we leave.

That is why we check three things before we start: leadership is behind the change, the AI decision is made, and we agree on what the first build can and cannot do. Then you get a yes, or a no with reasons.

What we get asked

“We already use AI.”

Yes, and that is the starting point, not the goal. The question is whether one process in your company starts on its own and runs through to a finished result. In most companies, none does.

“We can build this ourselves.”

Possibly. The MIT study found that internal builds fail twice as often as builds with outside help. The reason is rarely engineering. It is the connection to the real systems, and starting without a baseline. If you build it yourself, at least do the Discovery with us.

“Our people have no time for this.”

True. That is why your team’s part is small: interviews during the Discovery, and review time once the first build runs. The building is on us.

“What if the AI makes mistakes?”

It does. That is why a person checks the result before anything gets published, and why someone is named to step in before the first process writes to a live system. The model is built on checked results, not on the hope that nothing goes wrong.

“Will we lose jobs over this?”

Your leadership decides that, we don’t. The data points to more coverage: in most B2B companies there are more accounts, topics, and channels than the team can handle by hand. Where that is not the case, we say so before we start.

“How long does this last before the technology changes again?”

The tools change. The entry point, the knowledge base, and the points where a person decides stay, because they describe how your company works, not which tools it uses. That is where the weight of our work sits.

“Which tools do you need from us?”

A maintained CRM. Write access to your website or content system. An AI platform that can run agents, beyond a chat window. The permissions, and compliance settled. In most cases, what you have is enough.

Who builds this

I’m Mario, GTM engineer and AI leader. I build AI-first companies: the entry point, the skills, the knowledge base, the orchestration, and the change that makes a team trust the tools and the process.

I have built systems like this for four years, since AI became strong enough to carry a process. The last six months from the inside, in a B2B company with more than 60 AI seats and 36,000 accounts in the CRM. 119 cases are documented, each with the problem, the solution, the pitfalls, and the lesson.

Three of them

62 AI seats, 4 in daily use. We did not run another training. We built the first routines with the reps in the room, on their own accounts, and put the logic into the system instead of into prompts. People trust a tool that does something for them every morning.
1,065 high-intent contacts nobody had touched. Three of eight reps were active, and all three were at capacity. So we stopped adding to a full queue. We rebuilt the flow with Clay and HubSpot: contacts get scored, routed to the rep with room, and bad-timing accounts go on a track that brings them back on its own. 254 accounts came back that way.
The AI session for sales and marketing, every two weeks, was handwork every time. Now an agent builds it: what changed, one use case anyone can rebuild, and the open questions from last time. Change management with a routine behind it, instead of a deck.

The patterns repeat. Data quality beats model choice. Connecting the systems costs more than the pilot. Without a baseline there is no proof. Without a person in the loop there is no trust.

Mario Schäfer, Founder, Nima Labs · LinkedIn

Does this fit you?

We check the fit first: leadership behind it, the AI decision made, and a shared view of what the first build can and cannot do. No sales call, no deck. You get a written yes, or a written no with reasons.

Your team provides the goal and the creativity.Your company AI simply executes.

Sound familiar?

Every task still starts with someone opening a chat window.

Nothing runs unless someone starts it.

Your people work on prompts instead of on results.

AI-first is a higher bar than “we use AI”.

On a schedule.

When you ask.

What the AI works from

Positioning

Messaging

Tone of voice

Brand and design guidelines

Goals and targets

ICP and personas

Rules of engagement

Playbooks and data model