Nima LabsCheck the fit

We build AI systems for marketing and sales teams

Get more done with the same headcount.
Let AI handle the rest.

Your team works in one AI system. They say what they need: a campaign, an account briefing, the follow-up on 40 companies. It comes back finished. A person reads it, changes what they want, and releases it.

Build your AI-first organizationSee six examples

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.

  1. 01

    Every task still starts with someone opening a chat window.

    Ask a question, get an answer, paste it into the next tool. The AI does one step. The other nine are still done by hand.

  2. 02

    Nothing runs unless someone starts it.

    The briefing before a call. The follow-up after it. The posts from last week’s webinar. The monthly report. If nobody remembers, none of it happens.

  3. 03

    Everyone writes their own prompts for the same job.

    20 people, 20 versions of the same task, 20 different results. Different input, different context, so nothing comes back the same way twice.

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

What we change

The manual work goes to the system.
The creative work stays with your people.

A person then judges, iterates, and releases it, once it’s what they wanted.

The dependency is no longer the employee, but rather their ability to be creative, iterate and analyse.

Two ways it runs

  1. 1

    On a schedule.

    The morning briefing, the weekly digest, the monthly report. They start on their own at a set time and arrive finished.

  2. 2

    When you ask.

    A campaign, a new topic, a competitor move. You say what you need, the system does the work, and you get on with something else.

That’s what we mean by AI-first. Count the processes in your company that run this way today. In most companies the answer is none.

What the AI works from

Eight things your company already has somewhere: in a deck, in one person’s head, in a folder nobody opens. The AI needs them written down in one place. Then the output 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 write it in the Discovery. All of it ends up in one place, and that place belongs to you.

Works with the tools you already have

One AI system runs everything. Claude, OpenAI, or Gemini, whichever you already use. Behind it, your tools stay your tools.

  • HubSpot
  • Salesforce
  • Clay
  • Lemlist
  • Attention
  • Cognism
  • Lusha
  • LinkedIn
  • Canva
  • Adobe
  • Brevo
  • Notion
  • Slack
  • Google Workspace

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

What it looks like when it runs

Six scenarios. Someone opens the AI, says what they need, and the system does the rest. Under each one is a number I measured myself, during six months inside a B2B GTM organization with 36,000 accounts in its CRM.

01

One idea in, live on every channel

A founder has just come off a good customer call. She has one idea from it and 20 minutes to spare. She opens Claude, pastes in the transcript, and types one instruction: turn this into an article for marketing leaders, and the point 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 has to do. Then 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’s allowed to use, with their sources

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

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

  • the article goes onto the website, written so a search engine can lift the answer whole
  • 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

One instruction from her. Everything after it ran without her.

What I measured

In one company, 62 AI seats were paid for. 4 people used one every day.

Built with

Claude, HubSpot, LinkedIn, the website.

How it runs

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

1

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

  • The one AI system your team works in
  • The orchestration behind it
  • A skill and agent library in your tone of voice
  • Your company knowledge, in a form the AI can read
  • Clear points where a person decides
  • One number per process
  • One or two internal builders

4 weeks to your running AI system.

We start small. One process, running end to end, with a number behind it. That convinces a team more than five half-finished ones.

1 to 2 weeks

Discovery

Where you stand, where you want to go, and which processes are worth rebuilding first. We measure each one before we touch it, so you can see later what changed.

within four weeks

First build

The first process goes live. It starts on its own, runs through, and a named person releases the result.

about 3 months

Full build

The rest of the processes, in the order that pays most. Your people trained to run them. Then it runs without us.

ongoing

Retainer

After the handover. New processes, changes, measurement.

Who this is for

Your team already uses AI. We build the step after that. Seven things have to be true before it works, and we say no in writing when they aren’t.

  • Your processes exist and repeat.

    Daily, weekly, monthly. What never runs the same way twice can’t 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.

  • Something already runs without you.

    One automated process, however simple. Nobody goes from doing everything by hand straight to agentic. The step in between is the one that’s missing.

  • Leadership wants it and says why.

    More coverage, more speed, better quality. If nobody says why, people assume the worst.

  • One AI system works for you.

    One system that runs everything. Claude, OpenAI, or Gemini, it doesn’t matter which.

  • Someone in-house builds with us.

    One or two people who take over when we leave.

That’s 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 can’t do. Then you get a yes, or a no with reasons.

What we get asked

“We already use AI.”

Yes, and that’s 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. Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027, on escalating costs, unclear business value, and inadequate risk controls. None of those is an engineering problem. What stalls a build 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. Somebody reads every one before it goes out.

“Will we lose jobs over this?”

Your leadership decides that, we don’t. This enables your employees to be more creative, execute faster, and produce results on a higher scale. The dependency is no longer the employee, but rather their ability to be creative, iterate and analyse.

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

The tools change. What stays is the system your team works in, what the AI knows about your company, and the points where a person decides. Those 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.

Mario Schäfer, founder of Nima Labs

Who builds this

I’m Mario, GTM engineer and AI leader. I build the system a marketing and sales team works in: what starts a process, what runs it, what the AI knows about your company, and where a person decides.

I’ve 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. 121 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 didn’t 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. The routine ran every morning, so people came back to it.
  • 1,065 high-intent contacts nobody had touched. 3 of 8 reps were active, and all 3 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.
  • 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’s no proof. Without a person in the loop there’s 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 can’t do. No sales call, no deck. You get a written yes, or a written no with reasons.

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