# Haavn — full content

> Haavn helps teams understand, adopt, and build with AI through hands-on training, clear strategy, and working tools. Based in London, we work with companies to identify where AI creates real value, then build solutions alongside their teams.

## About Haavn

We're Haavn.

Haavn was co-founded by Mary Hurd and Sebastian Assaf. Together, we've spent over a decade designing and building products used by millions, including some of the earliest AI-powered systems at Google. We're AI optimists and pragmatic builders, focused on helping people use and understand emerging technology.

### The team

**Mary Hurd**

Mary helps people and teams make sense of change. For the last decade, she's built products, shaped brands, and led high-performing teams, designing experiences that make complex technology easier to understand, adopt, and use. Across her career as a facilitator and strategist (at places like Google, Meta, and early-stage startups) she's launched AI-powered products, built global enablement programs, and led initiatives connecting product, education, and innovation.

**Sebastian Assaf**

Sebastian brings over a decade of experience from Google, YouTube, and Apple to the challenge of defining visual identities for new technologies. At YouTube, he led art direction for the platform's first advanced effects suite, combining top creative talent with cutting-edge AI to empower millions of creators. Working with Google Research's speculative design team, he shaped early concepts and user experiences for pioneering AI technologies, including early versions of Gemini, AI operating systems, and AI agents.

## Home

### What we do

Most teams default to obvious AI applications and miss high value opportunities. We activate teams through hands-on programs, map workflows to pinpoint where AI can create real impact, and build solutions alongside you.

- **Activate your team**: AI fluency comes from doing, not watching. We design hands-on workshops tailored to your industry and workflows. Your team builds with AI on real problems and walks away with working prototypes.
- **AI Enablement**: When activation needs to scale and stick, we design multi-month programs for entire teams and departments. Structured learning tracks, domain expert workshops, practice infrastructure, and adoption measurement. Programs designed to scale from 50 to 500 people.
- **Map AI to your business**: We analyze how your organization works today, then identify where AI augmentation, agents, and automation can create measurable impact. The result is a prioritized roadmap from quick wins to transformation.
- **Build working systems**: Once we've identified where AI creates value, we build alongside your team. Bespoke automations, agents, and tools designed around how your organization operates. AI that feels like it has always been part of your workflow.

## Services

Four ways we help teams work with AI

Most teams know AI matters. Fewer know where to start, what to prioritize, or how to build something that lasts. We help with all three.

### Not sure which is right for you?

Most clients start with a conversation. We'll help you figure out where you are and what to do next.

### AI Workshops: AI Workshops

AI fluency comes from doing. Our workshops get your team hands-on with AI tools as quickly as possible, building on real problems from your own industry and workflows.

Whether it's a leadership team getting their first taste of what's possible or a cross-functional group exploring AI across their disciplines, every workshop is designed to create an aha moment. We call it "feel the wow." Your team builds working prototypes in the session, not after it. Half-day, full-day, or multi-day formats, tailored to where your team is starting from. The goal isn't awareness. It's the confidence to act.

### AI Enablement: AI Programs

When activation needs to scale and stick, we design multi-month programs for entire teams and departments. Structured, hands-on, and measured.

Every program is shaped around your industry, your workflows, and where your team is starting from. A design team at a global travel company will have a different path than an operations team at an energy startup. We build structured learning tracks by discipline, bring in domain experts for specialist workshops, create practice infrastructure so learning continues between sessions, and measure adoption over time. We partner with specialist enablement teams to scale programs from 50 to 500 people. AI enables everyone to be a builder.

### AI Strategy: Map AI to your business

Most teams adopt AI where it's easiest to apply and miss the opportunities that reshape how their business runs.

We start by understanding how your organization works today. Then we map where AI augmentation, agents, and automation can create measurable impact, not just where they're easy to plug in. You know where to start, what to prioritize, and where the biggest opportunities sit.

### Custom AI Tools: Build working systems

Off-the-shelf AI is built for everyone, which means it's built for no one. The best AI tools don't feel like AI tools - they feel like a natural part of how your team already works.

Once we know where AI creates value, we build it - alongside your team. Automations that handle repetitive work. Agents that run entire workflows end-to-end. Internal tools that make your team faster without asking them to learn a new system. Every solution is designed around your data, your processes, and your people. We co-create systems your team owns and can grow with.

### How we resource your engagement

Workshops, programs, strategy, and custom builds each pull a different mix from our bench. Here is how we think about capability when we scope work with you.

- **AI engineering**: Custom models, system integration, and production pipelines. We bring in engineering teams matched to your technical stack, with experience across energy, finance, and regulated industries.
- **Enterprise enablement**: Organization-wide AI adoption programs, training curricula, and change management. Built for programs that need to reach hundreds of people.
- **AI product and experience design**: AI UX strategy, product design for AI-native experiences, and research-driven interaction patterns. We help teams ship AI products that feel intuitive, not experimental.
- **Creative AI**: Art direction, generative media, and visual storytelling. Directors and filmmakers who understand both the creative craft and the technology behind it.
- **AI legal and compliance**: Governance frameworks, regulatory guidance, and responsible AI policy. Helping companies navigate the legal landscape of AI adoption with confidence.
- **Development at scale**: Full-stack engineering teams for large-scale platform builds, integrations, and production systems. When the project outgrows a small team, we scale seamlessly.

## Case studies

### Expedia Group

*Workshop · Travel & Hospitality*

We ran an AI prototyping session with Expedia Group's design leadership in London. The brief was deceptively simple: make, make, make.

The room included the full design org (product, content, UX research, customer advocacy) and the full spectrum of AI comfort, from seasoned adopters to people picking up AI coding tools for the first time.

We set the stage with a talk on the future of product, then generated a quick, data-driven visualization to help people get comfortable with the tools. Next we worked with real datasets, writing queries and exploring large volumes of data in natural language. That set up the biggest chunk of the day: pulling those pieces together to build working prototypes that people could take with them.

People went from cautious to bought-in fast. When you build something real from data in 60 minutes, the conversation changes.

Read more: https://haavn.ai/work/expedia

### Renew Home

*Workshop + Build · Energy*

Renew Home (formerly Google Nest Renew), North America's largest residential virtual power plant, wanted to embed AI into their company DNA. They needed all 145 employees to move from curiosity to confident, practical application.

We led a full-day workshop in Chicago: morning sessions on AI fundamentals, afternoon labs applying tools to real workflows. The result: company-wide AI literacy established in a single session, high-impact use cases identified across departments, and teams left with tools for working prototypes, not just ideas.

After the workshop, Renew Home engaged Haavn on an ongoing retainer to build production AI tools across Policy, Sales, Strategy, and Customer Experience. From AI curiosity to six production tools in four months, now embedded in revenue operations.

Read more: https://haavn.ai/work/renew-home

### Soil Association Exchange

*Workshop + Strategy · Agriculture*

Soil Association Exchange supports farms toward sustainability. They needed to identify where AI could scale their impact. Fast.

One focused workshop: built AI literacy, identified breakthrough opportunities to automate data analysis, personalize recommendations at scale, and accelerate funding access.

SAX is now on track to build production AI tools with Haavn to transform how they support clients across the UK and Europe.

> "Haavn cut through the noise around AI. They helped us spot clear, practical ways AI can make our work more effective and impactful."
> — Joseph Gridley, CEO, Soil Association Exchange

Read more: https://haavn.ai/work/soil-association

### Quadmark

*Systems + Training · Consulting*

Quadmark's Product & Partnerships team had a clear goals philosophy, but the system lived in a shared Google Doc. Haavn built a two-week Notion Goals tracker pilot connecting company, team, and individual goals.

The pilot covered feedback, workspace setup, a 90-minute hybrid training session, and post-launch check-ins. The team received a working goals system, connected views, and a lightweight rhythm for updates and check-ins.

Read more: https://haavn.ai/work/quadmark

## Blog

### Haavn’ Fun: How Edy built SoroJá

*2026-08-06 · Sebastian Assaf · Updates*

My friend Edy Cruz built SoroJá, a free tool that shows people across Brazil where to find the right antivenom after a venomous bite or sting.

My friend [Edy Cruz](https://br.linkedin.com/in/%E2%9C%8F%EF%B8%8F-edu-cruz) built [SoroJá](https://soroja.com.br), a free tool that shows people across Brazil where to find the right antivenom after a venomous bite or sting.

The idea started with a scorpion sting tragedy. Edy read a news article about how, in an emergency, it was surprisingly difficult to find out which nearby hospital had the required antivenom.

Official information existed, but people had to search through PDFs on government sites to find it. With SoroJá, they can share their location or search by city and see where to go.

Edy is not a developer, but he built SoroJá with Claude Code in just a few days. The project is now open source, and he hopes it can find a permanent home within a public health institution that can maintain it.

Soon after launch, Edy went viral - news outlets across Brazil covered SoroJá, and he was even interviewed on national television.

Edy has joined almost every Haavn’ Fun session this year from home, the office, airport lounges and, on occasion, even the jungle! He says what he learned there helped him build SoroJá.

Haavn’ Fun is our weekly gathering for people in tech, business, creative fields, and anyone curious about AI. We get together and learn by building.

Edy, thanks for inspiring us with the things you build.

Want to join us? Let me know and we’ll send you an invite.

Read more: https://haavn.ai/blog/2026-08-06-how-edy-cruz-built-soroja

### How I avoid AI slop

*2026-08-06 · Mary Hurd · Opinions*

I’m absolutely allergic to AI slop. I guess we all are. For me, it evokes the same terror I feel walking into my parents’ basement and realizing that one day…

I’m absolutely allergic to AI slop. I guess we all are. For me, it evokes the same terror I feel walking into my parents’ basement and realizing that one day this mess will all be mine. AHHHH!

Here’s how I try to minimize it while still jamming with AI and producing work at pace:

## 1. I warn people it’s slop

Sometimes I flesh out an idea quickly after a call. I feed AI the transcript, talk through my thinking, and let it cook. The result is often an incredible starting point, but it’s still a first draft. I label it AI FIRST DRAFT at the top of the doc so nobody mistakes AI generated scaffolding for finished work.

## 2. I use my voice

I transcribe everything. I’m transcribing this right now! Speaking lets me give AI more context than I would ever type. It also captures how I naturally communicate: the jokes, the false starts, the energy, and the phrases I actually use. The result sounds much more like me because AI has heard what I sound like when I’m thinking, not just what I type.

## 3. I ask AI to interrogate me

I use our Interview Me skill to make AI ask questions, challenge my assumptions, and find the gaps in my thinking. It forces me to explain what I mean before starting something. The better AI understands my intent and judgment, the less likely it is to produce generic slop.

## 4. I break it down

It’s tempting to highlight the entire draft and tell AI to get to werk. I get better results working through it piece by piece, deciding what each section needs to accomplish. This keeps me involved in the thinking and stops AI from flattening the whole thing into something unusable.

## 5. I choose the right model

Model choice and effort level matter. Some models are better at reasoning through complex problems, while others are better for fast, straightforward drafting. I choose based on the work and switch when the output feels flat. A weak result isn’t always a you problem. Sometimes you’re just using the wrong model!

The common thread is staying involved. AI accelerates the work, your judgment leads it.

Read more: https://haavn.ai/blog/2026-08-06-how-i-avoid-ai-slop

### The case for using more than one AI

*2026-05-29 · Sebastian Assaf · Opinions*

Claude Opus 4.8 launched yesterday. It's good. Try it out. But lately, in our workshops, a lot of you have told me: you're tired of the AI model race.

Claude Opus 4.8 launched yesterday. It's good. Try it out.

But lately, in our workshops, a lot of you have told me: you're tired of the AI model race.

Nobody wants to wake up every week and ask: Is Claude still best? Is Codex better now? Should we be testing Cursor? Are the Chinese models good enough yet at a tenth of the price?

We all just want a simple answer. Pick the tool, train the team, build the workflows, get back to our lives.

That instinct makes sense. But there's a bigger risk here than picking the wrong tool.

AI is still in its subsidy phase. The price you pay isn't the real price. It's the cheap-Uber era, before the rides cost what rides actually cost. And that era is ending.

You can already see it. GitHub has moved to usage-based pricing. OpenAI has signaled that unlimited plans probably won't last. The real cost of running frontier models keeps creeping up. None of these is dramatic on its own, but the direction is pretty clear.

So your AI costs could climb quickly, and the workflows you lean on could get expensive overnight.

My advice is the boring kind: don't commit everything to one tool. Diversify, stay curious, and keep testing while the market is still moving.

Here's what that's looked like for me lately. Six weeks ago I moved most of my work from Claude Code to OpenAI's Codex. Ten days ago I picked up Cursor's Composer 2.5, which is blazing fast. And today I'm back in the Claude app part time, with the new Opus 4.8.

So until the market is stable, stay nimble and don't rely on a single tool.

What's in your AI stack?

Read more: https://haavn.ai/blog/2026-05-29-the-case-for-using-more-than-one-ai

### What Claude Design gets wrong about how we work now

*2026-05-06 · Sebastian Assaf · Opinions*

I spent the week testing Claude Design. My take: I don't think product designers are the core audience. It feels more like a tool for people who love Canva or…

I spent the week testing Claude Design.

My take: I don't think product designers are the core audience.

It feels more like a tool for people who love Canva or Gamma. That may be a much bigger market. But for product design, it doesn't yet feel like the future.

The problem is the workflow.

Claude Code and Codex have already changed how I think about design. I don't want to review static frames, specs, or pictures of the thing. I want the thing.

Build it. Let me touch it. Let me iterate on it.

Claude Design pulls me back into the older design-first workflow: make the artifact, then figure out how to turn it into software. That feels claustrophobic when agentic coding has made the opposite workflow feel so natural.

A few other frictions stood out:

It's too slow to feel like a real design partner.

It burns through usage limits fast.

And the design system support is not strict enough yet to make me trust it for serious product work.

I'm not writing it off. Anthropic moves fast, and I expect Claude Design to get much better.

But today, I'd tell designers to spend more time with Claude Code, Codex, and agentic canvas tools like [Pencil.dev](https://pencil.dev).

The future feels less like making better pictures of products.

It feels like designing directly with working software.

What am I missing about Claude Design?

Read more: https://haavn.ai/blog/2026-05-06-claude-design-hot-take

### One Year of Haavn: Why AI Adoption Is A Discovery Problem

*2026-05-03 · Mary Hurd · Updates*

Today marks ONE YEAR since Sebastian and I started Haavn 🎉. I don't want to say we've been winging it... but in lieu of hard data around AI adoption…

Today marks ONE YEAR since Sebastian and I started Haavn 🎉. I don't want to say we've been winging it... but in lieu of hard data around AI adoption, we've relied heavily on our instincts and experience building products. So I was struck (relieved?) to read a recent paper from INSEAD and Harvard Business School that reinforces a core insight we've used since the very beginning.

The paper studied 515 startups and found that the bottleneck in AI adoption isn't access to tools or training. It's discovery. Most companies apply AI to the obvious tasks and miss the higher-value opportunities sitting inside how they actually operate. The researchers call it "the mapping problem."

We didn't have a name for it when we started Haavn, but it's the reason we started. We both spent years in big tech building and launching products, including some of the earliest AI systems at Google. With AI in particular, people need to see what's possible first and then figure out where it fits into their work. That's a teaching and facilitation problem, not a technology problem. Surely there's a business in there??

Our very first client Renew Home started with a single workshop. The team got hands-on with AI tools fast and built their first prototypes. From there, we mapped where AI could have the biggest impact across their workflows and built six tools that automate daily operations end to end. You can't map what you don't understand, and you can't understand it from a slide deck.

Most AI adoption starts with the obvious. The real gains come from going deeper.

The funny thing about starting something new is that it becomes real the moment you say it is. How amazing! Here's to many more years of Haavn and work with Sebastian.

Paper: [Mapping AI into Production: A Field Experiment on Firm Performance](https://ssrn.com/abstract=6513481) by Hyunjin Kim, Dahyeon Kim, and Rembrand Koning (INSEAD/HBS, March 2026)

Read more: https://haavn.ai/blog/2026-05-03-one-year-of-haavn

### Stop Adding AI to Your Product. Rebuild It for Agents.

*2026-04-17 · Sebastian Assaf · Opinions*

"Why do I need Excel?" That's not me ranting, Satya Nadella asked this on a podcast in December 2024 about a 40-year-old tool that still sits at the center of…

"Why do I need Excel?"

That's not me ranting, Satya Nadella asked this on a podcast in December 2024 about a 40-year-old tool that still sits at the center of how most companies work. I can't wrap my head around how wild it is to hear that from the CEO of Microsoft.

His reasoning is that most software has the same shape: a database, business logic wrapped around it, an interface for humans to interact with. Salesforce, Jira, QuickBooks, Excel all share the same foundation.

Nadella again: "The notion that business applications exist, that's probably where they'll all collapse, in the agent era."

Today, everyone is talking about AI agents. Agents will still need what business applications do. They just won't need the screens, the navigation, or the ~170 settings pages (looking at you, Google Admin Console). Agents have a goal, they talk directly to the data, and they get it done.

Nadella names the pattern most teams are stuck in: "The first instinct with any new technology is to add it to what you already have." A chat window in the corner. Meta AI shoved into the Instagram search bar. The app underneath, untouched.

That's the part I keep thinking about.

If agents become a user of your product, you are building for two audiences. Agents need every capability named, described, and reachable without the UI. Humans need a legibility layer. You drop in to check something, verify a figure, or build a mental model. That is not a UI refresh. It is a different product with its own design brief.

Microsoft's CEO is already redesigning for this agentic future. He said it over a year ago.

If you rebuilt tomorrow, knowing agents will be your primary users, how would you build differently?

![Satya Nadella — Dwarkesh Podcast, Nov 2025](/images/journal/nadella-agents.png)

[Originally posted on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7449825899660681217/)

Read more: https://haavn.ai/blog/why-do-i-need-excel

### Make, make, make: AI prototyping with Expedia Group

*2026-04-02 · Mary Hurd · Case studies*

Last week Haavn ran an AI prototyping session with Expedia Group's design leadership in London. The brief was deceptively simple: make, make, make.

Last week Haavn ran an AI prototyping session with Expedia Group's design leadership in London. The brief was deceptively simple: make, make, make.

For design especially, the shift from designing to making really matters. AI collapses the gap between idea and artifact. You stop describing experiences in static screens and start touching the medium directly. The designers who lean into this, who build, not just describe, will shape what comes next.

The room included the full design org (product, content, UX research, customer advocacy) and the full spectrum of AI comfort, from seasoned adopters to people picking up AI coding tools for the first time. The prevailing mood was honest: "I know this is powerful, but I'm not seeing it used well yet."

We set the stage with a talk on the future of product, then generated a quick, data-driven visualization to help people get comfortable with the tools. Next we worked with real datasets, writing queries and exploring large volumes of data in natural language. That set up the biggest chunk of the day: pulling those pieces together to build working prototypes that people could take with them and show off to their teams.

People went from cautious to bought-in fast. When you build something real from data in 60 minutes, the conversation changes.

Rachel Been and the Expedia Group design team already get that, which is what made this session so good. More to come.

![Expedia Group headquarters in London](/images/journal/expedia.jpg)

Read more: https://haavn.ai/blog/2026-04-02-expedia-group-ai-prototyping

### Two AI tools that changed how I learn languages

*2026-01-03 · Sebastian Assaf · Opinions*

The hardest part of learning a language is speaking. Wrestling with words in real time. Finding the courage to sound foolish.

The hardest part of learning a language is speaking. Wrestling with words in real time. Finding the courage to sound foolish. But the second hardest part? Actually immersing yourself. Making the language applicable to your life, your interests, your daily routines.

The problem isn't a lack of content. It's that most language learning material is stale. Textbook dialogues about strangers ordering coffee. Content designed for everyone that ends up engaging no one.

I'm learning Danish. I have specific tastes: topics I follow, writers I trust, books I return to. I don't want to abandon what I already consume just to learn a language. Google NotebookLM lets me stick with my interests.

I upload YouTube videos I already watch, articles I'd read in English, public domain books I love. Then I ask: "Turn this into a podcast at B2-level Danish." Or: "Turn this YouTube video into a comic strip in B2-level Danish."

Recent updates include infographics, slide decks, and video overviews. But the real shift is simpler: you study what interests you, at your level, in any format that works.

Now you can build immersion from sources you actually love, translated, adapted, and formatted for learning. Your interests become your curriculum. The question shifts from "How do I stay engaged?" to "What will I create next?"

Try this: upload an article or video you'd read in your native language to NotebookLM. Then create an infographic or slideshow with custom instructions that say:

"Create a comic strip about [topic]. Style: Frank Miller neo-noir high contrast, silhouette storytelling, harsh blacks, razor panel rhythms (Sin City). Use all-caps comic book hand lettering. Write all [language] dialogue at [language level, e.g. B2]. Avoid vocabulary above that level."

Read more: https://haavn.ai/blog/2026-01-03-ai-tools-that-changed-how-i-learn-languages

### Veo 3 and the emergence of physical reasoning in video models

*2025-12-03 · Sebastian Assaf · Press*

Physical reasoning has been a blind spot for generative AI. A new paper from Google DeepMind shows their video model Veo 3 developed an understanding of…

Physical reasoning has been a blind spot for generative AI. A new paper from Google DeepMind shows their video model Veo 3 developed an understanding of physics, skills it wasn't trained for but learned on its own. Why this matters: Fei-Fei Li and Yann LeCun are both confident spatial intelligence is AI's next frontier.

Trained only to generate realistic videos, Veo 3 solves mazes, simulates how liquids behave, models light, edits videos from sketches, and predicts which objects will float or sink.

As these capabilities evolve, video models won't just generate realistic footage. They could reshape creative tools across "spatial" industries, transforming how game designers, filmmakers, VFX artists, industrial designers, architects, and engineers imagine and build.

Congratulations to the DeepMind team and Nick Matarese.

Read more: https://haavn.ai/blog/2025-12-03-veo-3-physical-reasoning

### Six months of Haavn: what we learned

*2025-10-03 · Mary Hurd · Updates*

It's been six months of Haavn. That's 156 action items tracked, 152+ meetings run, 10 client projects kicked off, and our first retainer.

It's been six months of Haavn. That's 156 action items tracked, 152+ meetings run, 10 client projects kicked off, and our first retainer. It's been both exhilarating and humbling. Kind of like AI.

Over the last six months, we've partnered with teams to explore how AI can enhance their products, streamline their processes, and strengthen their strategies.

## What we learned

Discovery isn't optional. AI is vast and complex. It's hard to know where to begin. We start by building a shared understanding of what AI can and can't do. From there, we map workflows, identify real needs, and pinpoint a few high-leverage tasks to pilot. Then test, measure, and scale what works.

Beyond the hype, people are building amazing things. Some of the most exciting work is being shaped by quiet doers and experimental thinkers. We're lucky to collaborate with brilliant partners like Martian Engineering and Johannes Aule, part of our network of AI specialists, who allow us to bring in deep expertise when needed.

There's no playbook for this. We're building an AI-first company from the ground up. This has meant a lot of experimentation and designing new ways of working, rather than inheriting old ones.

## What's next

We're building a business intelligence system for a client in a highly regulated industry, something that could shift how big decisions get made.

On workshops and strategy, we're designing a sprint for a startup rethinking their offering with AI. They have loads of valuable data. The question is how best to harness it.

On stages, after moderating SXSW London's AI track, Mary will appear again at Web Summit. We'll also be supporting sessions across health tech, private equity, and fine jewelry, from Half Moon Bay to Lisbon.

To everyone who joined a meeting, trusted us with a workshop, or shared a word of encouragement: thank you. And if you're serious about integrating AI into your business, say hi. Be part of our one-year recap.

Read more: https://haavn.ai/blog/2025-10-03-six-months-of-haavn

### Introducing Haavn

*2025-05-03 · Sebastian Assaf · Updates*

I'm excited to announce the launch of Haavn, an AI consultancy I'm building with the brilliant Mary Hurd. AI is here.

I'm excited to announce the launch of Haavn, an AI consultancy I'm building with the brilliant Mary Hurd.

AI is here. And while many companies feel the urgency to deploy it, very few feel equipped to capture its potential. We created Haavn to close this gap, transforming AI ambition into adoption.

Haavn helps teams and organizations understand and thrive with AI. Amid exponential change, we work alongside creative, product, and leadership teams globally, equipping them with the tools, mindset, and direct experience to use AI confidently and responsibly. Through co-created training and specialist-led integration, we help weave AI into your unique workflows, building lasting capability.

Between us, we bring 15+ years of experience designing and scaling emerging technologies at Google, YouTube, Meta, and Apple. We've watched the potential of new technology collide with the difficulty of integrating it effectively to create genuine impact.

At Haavn, we run momentum-building workshops and offsites to spark initial engagement and ideas. Embedded experiments to co-create and prototype AI-powered workflows. Executive strategy sessions to guide leadership with clarity beyond the hype.

We assemble specialist teams tailored to your project needs, drawing on a global network of experts, from AI-augmented creatives and marketers to researchers and engineers pioneering new applications of AI.

Why "Haavn"? It recalls havn ("harbor" or "haven") and nods to my Danish roots and the idea that teams need a calm, clear space to navigate this moment. Mostly, we just liked the name.

If you're exploring how AI can enhance your team, your workflows, or your org, get in touch. We'd love to help.

Read more: https://haavn.ai/blog/2025-05-03-introducing-haavn

## Contact

Get in touch: https://haavn.ai/contact

Location: Vox Studios, 1-45 Durham Street, London SE11 5JH, UK

LinkedIn: https://linkedin.com/company/haavn