Turning an idea into a successful SaaS or AI product takes the right strategy, the right team, and flawless execution.

That’s what we do best! We've worked with 50+ B2B companies to build and scale SaaS & AI products that bring results.

With a proven process, clear communication, and a focus on scalability, we make sure your product is built fast and made to laaaast 🚀

Our process involves:

👉 Defining the best product strategy & tech stack
👉 Designing an intuitive & beautiful UX/UI
👉 Building an MVP in 8 to 12 weeks
👉 Scaling it into a robust, full-featured platform

💡 Experts in: React • Django • Node.js • Bubble.io • WeWeb • Xano

Diogo Guerner

I studied cinema. Not computer science.
 
I did study some business along the way, but the foundation? Film school.
 
And honestly, I think it's one of the best things that happened to my career in tech.
 
When I tell people I run a digital product studio, the first question is usually about my technical background. When I say "film school," people get a bit surprised.
 
But here's what studying cinema actually teaches you:
 
- How to tell a story.
- How to structure information so people feel something.
- How to direct attention.
- How to take a big, messy idea and distill it into something clear.
 
That's product development.
 
Every great product is a story. There's a protagonist (your user), a conflict (their problem), and a resolution (your solution). If the story doesn't make sense, the product won't either.
 
At Naviu, we build digital products and AI for non-tech companies. Most of our clients don't care about the tech stack. They care about whether the product makes sense for their people.
 
That's a storytelling problem, not an engineering one.
 
I'm not saying you don't need great engineers. You absolutely do.
 
But the people who can bridge the gap between "what the tech can do" and "what the human actually needs"?
 
Those are the ones shaping the best products right now.

CEO @Naviu / Launch digital products & AI solutions at startup speed

I gave my coworker a promotion: he's now called Claudius Maximus.

After a couple of weeks using Claude's Cowork, I'm not exaggerating when I say this is the most productive I've ever been: creating documents, building presentations, prospecting, writing content (yes, part of this post too 🤣)

Things that used to eat half my day now take minutes.

It's like having a senior team member who never sleeps and somehow knows a little bit about everything.

We're entering a phase where solo founders and tiny teams can operate like companies 10x their size. Not because they work harder. Because their AI coworkers do the heavy lifting.

If you haven't tried it yet, you're leaving hours on the table every single week.

What are the best use cases you've seen? Drop them 👇

What happens when AI stops chatting and starts dialing?
 
Not a chatbot. Not an assistant. An actual AI that picks up the phone, handles qualification and closes conversations.
 
Most people are still focused on text-based AI. Meanwhile, companies are quietly automating their entire sales and operations with voice agents.
 
That's why on Thursday, March 12, I'm hosting Philipp Baumanns at the buildersklub in CIC Berlin.
 
Philipp co-founded telli (YC-backed), previously scaled Enpal to 150+ people, and has been deep in the AI voice agent space.
 
I'll be sitting down with him for a fireside chat on the real ROI of AI voice agents, followed by a live demo:
 
👉 The founder journey from scaling Enpal to building a YC-backed AI startup 
👉 Why voice agents are one of the fastest ways to expand sales and ops capacity 
👉 Building a production-ready voice agent in minutes, live on stage 
👉 How these agents handle real objections and trigger actions directly in your CRM
 
But we're not just going to talk about it. You'll see it in action.
 
If you're in Berlin and want to go from "I should really try AI in sales" to "I just saw an agent book a meeting live," this one's for you.
 
​This event is happening as part of the Thursday Gathering by Venture Café Berlin
 
📅 Thursday, March 12 
📍 CIC Berlin 
🎟️ Spots are limited
 
Link in the comments 👇

Something I keep noticing across our projects at Naviu:
 
The teams delivering the most impact are getting smaller.
 
Not because companies want to cut costs (though that helps). But because the tools have changed the math.
 
AI handles a lot of the repetitive work. One senior engineer with the right stack covers ground that used to take 3 or 4.
 
But the real advantage of small teams isn't speed. It's clarity.
 
Small teams don't lose weeks in alignment meetings. They don't write specs for the sake of writing specs. They talk to the customer, prioritise, and ship.
 
We work with non-tech companies and corporates who need digital products built fast. And the pattern is always the same: the moment you add unnecessary layers, momentum dies.
 
What works instead:
 
- A senior-led team that owns the problem end to end.
- Clear checkpoints, not endless handoffs.
- Iteration based on what's real, not what's in a slide deck.
 
This doesn't mean big teams are useless. It means that for most digital initiatives, you don't need 20 people. You need the right 5.
 
The companies that figure this out first will have a serious advantage.

Is AI making us go out of business?

I’ve been asked this a lot lately, and honestly, I’m feeling the exact opposite.

We’re seeing more and more non-technical businesses that finally see a path to building their own software to scale operations.

The vibe coding era is changing the starting line:

👉 Better Prototypes: Clients show up with functional AI prototypes they built themselves.
👉 Clearer Vision: Because they’ve already "played" with the logic, they know know better what they need.
👉 Faster Execution: Internally, we’re using AI assisted tools to ship complex apps at a speed that wasn't possible two years ago.

I think we’re seeing the Jevons Paradox at play.

As the cost of building software drops, the demand doesn't decrease, it increases. As the barrier to entry lowers, there are simply more opportunities to build.

But here’s the catch: as coding becomes a commodity, the craft becomes more valuable.

Just because everyone has a powerful camera in their pocket doesn't mean everyone can be the new Steven Spielberg.

The machines are writing a lot of the code, but humans are still the ones telling the story.

Your digital initiative stalled. Again.
 
And I bet it wasn't because the technology was wrong.
 
After working on dozens of digital products for corporates, I keep seeing the same pattern. The projects that fail almost never fail because of tech.
 
They fail because of:
 
- No clear owner. Everyone is "involved," nobody is accountable.
- No defined success metric. The team builds features instead of solving a problem.
- Internal politics slowing every decision to a crawl.
 
The companies that actually ship? They do three things differently:
 
- They assign one person who owns the outcome. Not a committee. One person.
 
- They define what success looks like before writing a single line of code. Not "we need an app." More like "we need to reduce manual order processing from 4 hours to 20 minutes."
 
- And they bring in external partners not because they can't build internally, but because an outside team moves faster, challenges assumptions, and doesn't get stuck in org chart politics.
 
The tech is the easy part. Alignment is the hard part.
 
If your digital initiative is stuck, look at the org chart before you look at the tech stack.

I’m a tech-driven entrepreneur with a background in Econometrics and Operational Sciences - and a fascination with the messy, human side of data.

I believe AI is only as smart as the business context it’s built on. That’s why I founded Anlytic - a data collaboration platform that brings clarity to data chaos by making it radically easier for teams to work across dashboards, KPIs, and strategy.

My work now centers around helping leaders translate between AI potential and business outcomes, turning buzzwords into bottom lines. I share that journey publicly - failures, frameworks, and first-principles thinking included.

When I’m not deep in system design or AI conversations, you’ll find me playing chess, diving into oceans, or building things with my wife, Nataliia.

Martijn Witteveen

Quick ask for the e-commerce folks here

If you’re running an e-commerce business and you’ve been following what we’re building with Anlytic, I’d love your input.

We’re trying to decide which problem to focus on next and I don’t want to guess from the outside. I’d much rather hear where things actually slow you down, get repetitive, or quietly break in your day-to-day workflows.

All I’m asking for is about 15 minutes of your time.
Either a short call or a quick survey, whatever’s easier. No pitch, no selling, no prep needed.

If you’re open to it, comment “ECOM” and I’ll send you the link.

#agenticai #ecommerce #bitools #datatools

CEO @ Anlytic.com | Artificial Intelligence Leader | AI + BI Operator | Making AI Useful in the Real World

10,000 people 🙂

That honestly blows my mind a little.

I initially started posting here because I wanted to think in public and share what I was building, struggling with and challenge my own assumptions about AI, startups and leadership.

It’s a weird reminder that you don’t need to have everything figured out to show up.

Grateful for everyone who reads, challenges, comments or just quietly follows along.

If every time someone asks a basic question the answer is “let me pull the numbers,” that’s not a system, that’s one person digging through spreadsheets while everyone else waits.

You don’t actually have visibility into the business, you have a few people who know where things are buried. 

When they’re busy, on leave, or leave the company entirely, everything slows down because nobody else can see what’s going on without them.

At that point the problem is DEPENDENCE. The company moves at the speed of whoever holds the files.

Real systems don’t make you wait for answers. The numbers are already there, consistent, accessible, and trusted enough that decisions don’t require a scavenger hunt first.

That’s exactly the gap we built Anlytic for. Turning “someone needs to pull the numbers” into “everyone can see what’s happening right now,” without relying on one person to translate the business every time.

#agenticai #datatools #bitools #aiagents

If your team still exports CSVs, you shouldn't even think about AI. 

You don’t even have the foundation yet...

You probably export data because your systems don’t actually work together. Sales, ops, marketing, finance all live in separate places, so every important question turns into manual file pulling and cleaning.

By the time anyone looks at the numbers, they’re stale.

Adding AI on top of that would just produce answers from unstable inputs.

This is why Anlytic establishes stable layer that turns scattered operational data into something the business can actually run on. 

Until that exists, AI is just decoration on top of spreadsheets.

#bitools #datatools #analytics #agenticai #genai #aiagents

No one tracks the time spent double-checking numbers across spreadsheets.

It’s just a few minutes here and there… opening another file, making sure totals match, asking “which version is this,” fixing one broken formula, exporting something again because the source didn’t line up.

Individually it feels harmless but collectively it’s hours every week, across multiple people forever.

And it doesn’t show up on any dashboard because it isn’t officially “work.” It’s just the cost of not fully trusting the data yet.

Over time that quiet friction slows decisions, delays meetings, and burns energy that should be going into actual progress instead of verification.

This is exactly the kind of invisible operational drag most teams don’t notice until they finally replace it with a system where the numbers are already there, already consistent, and nobody has to babysit them and that's exactly what we're doing with Anlytic.

Most people hear AI and instantly think of ChatGPT. 

Ask a question, get an answer, maybe generate an image or some code. 

That’s Level 1 and despite all the hype, most companies haven’t really moved beyond it.

Level 2 is Agentic AI. This is where AI stops being a toy and starts behaving more like a junior employee. 

Instead of asking it things, you gAive it a job like summarizing incoming tickets, flagging anything unusual.

Level 3 - Multi-agent systems where it begins to resemble an actual organization. 

You don’t have one all-knowing AI, you have several specialized ones coordinating. One pulls data, another interprets it, another decides what matters, another triggers actions, another writes the report. 

They hand things off to each other like a team would. This is the part that quietly changes how work gets done, because it’s not about better answers anymore, it’s about continuous execution in the background.

Level 4 - The famous AGI everyone talks about, a system that can handle most intellectual tasks as well as a human across different domains. That’s what research labs are chasing and what headlines love but it’s not something you can plug into your company next quarter to fix operations.

Level 5 - Superintelligence. Beyond human capability across the board.

Fascinating to debate, not very helpful when you’re trying to figure out why last week’s numbers don’t match finance.

Most companies don’t need god-like intelligence. They need systems that are stable enough for software to act on without breaking something. 

That’s the unglamorous part nobody writes about, but it’s where the difference between demo AI and operational AI actually lives.

#artificialintelligence #ai #agenticai #genai

Imagine waiting for lab results that keep coming back inconclusive.

Doctors order another test, ask for a second opinion, monitor the patient a bit longer, hoping the picture becomes clearer before they order treatment.

It's the same with bad data.

Companies behave exactly the same way when the numbers don’t feel solid. 

Decisions quietly get postponed while someone “just double checks” the report, pulls another dataset, reconciles a mismatch or asks finance to confirm which figure is actually correct. 

Meetings end with follow-ups instead of conclusions and projects just hover in limbo.

That’s the problem we’re solving with Anlytic, turning scattered numbers into something teams can actually trust in real time, so decisions don’t have to wait for another spreadsheet and meeting.

#agenticai #bitools #datatools #artificialintelligence

On a call the other day a founder told me they’d bought a few AI agents and honestly they were amazing… for about two weeks

Nothing broke or crashed, they just slowly stopped fitting into how the company actually runs.

In the demo call it's just one task with a clear prompt and nice result but in real life it’s a bunch of approvals and workflows using five different tools

So every output needed someone to sanity-check it, tweak it and rerun it… eventually the team drifted back to “can you quickly check this” and manual work

It didn’t feel like hiring a super employee, just someone who needs a lot of micromanagement.

Agentic AI right now is limited by environment because until there’s a place where agents have reliable data, clear triggers, memory, and guardrails, they don’t really operate...

That's basically why Anlytic helps you establish the ground your AI agent can actually stand on.

#aiagents #agenticai #datatools #bitools #artificialintelligence

Imagine driving somewhere new and your GPS keeps recalculating every twenty seconds. 

That’s what unclear metrics feel like inside a company. Everybody's always double-checking the numbers before making even the smallest decision. 

People naturally hesitate because it's a system problem. 

At Anlytic we focus on building a shared, reliable foundation so teams don’t need a verification ritual before every decision.

#data #bitools #datatools #agenticai

If your data looks like final_final_v7_REAL.xlsx, you don’t have a reporting system… you have survival tactics.

Everyone knows that file that nobody wants to delete because nobody is sure which version is actually correct. So a new copy gets made, someone adds their initials, someone else tweaks a formula, and suddenly the “single source of truth” lives in five inboxes and two desktops.

By the time it reaches leadership, the conversation isn’t about the business anymore. It’s about whose numbers to trust, what changed since last week, and whether finance has a different version.

That’s not a data problem. It’s a coordination problem disguised as a spreadsheet.

Tools keep producing more files, more exports, more dashboards, but none of that fixes the underlying issue: nobody shares the same reality at the same time.

Anlytic is built to eliminate that entire cycle. One connected system, live numbers, no version guessing, no reconciliation rituals before every decision.

Because companies don’t stall from lack of information. They stall from lack of confidence in the information they already have.

#bitools #datatools #agenticai

There’s a moment in almost every growing company when someone says, “let’s just track it in a spreadsheet for now.”

It usually happens during a busy week and there isn’t time to build a proper system, so someone creates a quick sheet with a few columns.

Then the company grows. More people start using it, more tabs appear, exports from the CRM get pasted in, someone from finance pulls another version to double-check the totals. 

That spreadsheet quietly becomes one of the most important systems in the company, even though nobody designed it to be.

What we often do with companies at Anlytic is start by tracing one of those spreadsheets back to its sources. Where does the data actually come from? Which systems generate it? What definitions are people using when they calculate the numbers? Once that becomes clear, the spreadsheet stops being the place where everything gets stitched together manually.

Instead the data flows directly from the systems where it lives, the logic gets defined once, and the numbers update automatically. The spreadsheet doesn’t disappear overnight, but it stops being the fragile bridge between half a dozen tools.

This is usually the point where we come in with Anlytic. 

We help companies set up the proper data foundation and implement an agentic system that connects all data structures and lets agents handle the reporting, monitoring and operational workflows automatically, instead of relying on manual spreadsheets.

#agenticai #data #businessintelligence #datatools

Six months ago a founder told me: “We don’t need help with data.”

Ten minutes later their team was exporting CSV files from three different systems and reconciling them in Excel before a leadership meeting.

That moment eventually turned into a framework I call The Data Stages - a practical way to move from messy spreadsheets to reliable systems that actually support decisions (and eventually AI agents).

I’ve started writing these ideas down on Substack.

If this kind of thinking is useful to you, feel free to check it out. I’ll be sharing practical notes on data systems, AI agents, and the operational stuff inside companies that nobody really explains clearly.

#ai #startups #automation #dataengineering

There’s a moment in many companies where every important meeting starts the same way.

Someone asks a simple question “What’s our actual revenue this month?”

Then somebody rushes to open a dashboard, someone else checks a spreadsheet, finance shares one number, the product team has a different one.

What most founders don’t realize is that this isn’t really a reporting problem.

It’s a foundation problem.

The company started with spreadsheets, quick integrations and a few but as the company started growing, those temporary solutions slowly became the system the business runs on.

Over time I started noticing that companies tend to move through a few predictable stages as they try to turn that early chaos into something reliable.

I call them The Data Stages.

It’s basically the path from messy spreadsheets to systems that can actually support automation and AI agents.

I wrote the full breakdown on Substack. I’ll drop the link in the comments.

If you’ve ever been in a meeting where people spend 10 minutes agreeing on which number is correct before making a decision, you’ll probably recognize a few of these stages immediately.

#data #analytics #datatools #businessintelligence #agenticai

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Martijn Witteveen

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Diogo Guerner's LinkedIn Performance

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Performance ScoreDiogo Guerner's LinkedIn Performance