As AI changes how people discover products online, marketers are rethinking the traditional digital advertising playbook. With AI-generated answers reducing clicks on search results and display ads, brands are looking for new ways to reach customers.

Seattle startup MediaPact wants to capitalize on that shift.

Founded in 2026 by online marketing veteran Lacie Thompson, MediaPact makes finding and signing ad deals quicker, painless, and accountable for both publishers and companies. It has raised $200,000 in a small friends and family round, and recently added companies like BroBible, Gadget Review and Penske Media to the platform.

We caught up with Thompson for GeekWire’s Startup Spotlight to learn more about her one-person startup, how AI helped her build the business despite having no coding experience and what surprised her most about launching in a market she thought she already knew.

In 50 words or less, give us your startup’s elevator pitch?

MediaPact is a marketplace and workflow for flat-fee direct media. Buyers discover publishers, newsletters, and creators, then negotiate terms, sign the IO (insertion order), and pay, all in one place. Seller inventory is standardized to list inventory in a searchable format. It is the direct media buy without the 40-email thread.

What problem are you obsessed with solving?

Flat-fee media is a massive market that still runs on emails, PDFs, calls, bespoke IOs and a Google Sheet named “final_FINAL_v3.”

Nine out of ten publishers I have interviewed described their flat-fee workflow as exactly that: manual email threads, hand-built IOs, invoices they chase for 60 days. Meanwhile, the buyer on the other side of that thread is sitting on budget and cannot find them.

Programmatic solved this for banner ads 15 years ago. Nobody has ever solved it for this type of media: sponsored articles, newsletters, or podcast reads. I am obsessed with making a direct media buy as easy as booking a flight.

What surprised you after talking to customers?

Two things:

Supply is not the problem. I have spent 15 years in this industry, so I can sign publishers all day. Demand is the hard part. Every marketplace founder reads The Cold Start Problem and still thinks they are the exception. I was not the exception.

The buyers are much more broad than I thought. I come from affiliate and performance. Those teams live and die on click-based measurement. While they often purchase flat-fee media, they sometimes avoid the risk of guaranteed placement because of over-scrutinized click-based attribution (especially on a last click).

One hyper-performance-based agency told me flatly that this was not for them. Brand marketers who understand top of funnel growth get it. They are typically at a mid-stage consumer brand that has plateaued on Meta and Google and needs somewhere else to go. Shopper marketers are also very focused on working with partners that can reach their audience, even if they are influencing in-store behavior in ways that are difficult to measure. Said another way, MediaPact is for the marketer who uses art, the marketer who uses science and the marketer who uses both. 

How has AI changed the way you build your company?

Two ways, and the second is a strategic angle for the platform, not just an operational efficiency.

The obvious one: I built and shipped (and am continuing to do so) the entire platform with Claude Code. React, TypeScript, Supabase, Stripe Connect, the whole thing. I have zero experience writing code, managing dev teams, or product management. And now I can ship features to production within less than a day. I don’t say this to boast, but rather to show that this is a structural change in who gets to start what kinds of companies.

AI is eating the click. When ChatGPT answers the question, nobody clicks. And the content is so trusted that conversion happens at 4.4 times the rate. So brands stop competing for rankings and start competing to be inside the source material that the models cite, which is high-authority editorial. That is not just SEO anymore. It is Answer Engine Optimization, and the only way in is to be in the content. MediaPact allows buyers to do this.

What’s one thing people misunderstand about your startup?

That it is for affiliate marketers. My résumé makes people assume rev-share, cookies, and last-click attribution.

It is the opposite. Flat fee, guaranteed placement, signed IO, and automated payment. Sellers get paid for their audience and their authority, not for whatever the attribution model felt like giving them that month. Publishers have been shortchanged by last-click for decades and everyone in our industry knows it.

What’s the toughest decision you’ve made in the past year?

Launching the company and determining the real TAM. 

My network is affiliate. Those are warm calls, fast meetings, and lots of enthusiastic nodding. It would have been very comfortable to build for them. But the customer discovery data pointed toward brand marketers, shopper marketers, and media planning and buying teams—audiences who don’t know me. 

Even though I know this challenge, I’m tackling it by figuring things out as I go, in the same way I did before: by building partnerships and relationships that grant me access to the right opportunities. 

What’s the one piece of advice you give to other entrepreneurs?

Ask for help. The key, though, is that you have to give help, you must be someone people want to help and that isn’t just granted—it’s earned over years. I naturally think of asking my network for help: my friends, my family, and my advisors. But now you can also ask Claude (or your preferred AI) for help. While it’s definitely not the same, knowing when to ask whom or what for help is probably the most powerful needle-mover. 

We’ll know our company has made it when… 

I’m the most proud when I know the platform has benefited someone. Usually when that’s the case, they want to tell their friends about it. That part of the growth cycle is always the most fun for me because I have the luxury of getting out of hustle mode and into innovation mode, pushing beyond the beta, dreaming big and taking things beyond my current scope. 

When sellers tell brands “just send it through MediaPact” without me anywhere in the conversation, that will be a milestone. The day the marketplace works without the founder in the middle is the day it is actually a marketplace.

As AI workloads drive soaring cloud bills, more companies are weighing whether to move computing out of public clouds and into their own data centers. But building and operating AI infrastructure is far more complicated than simply buying servers — networking has become one of the biggest technical hurdles.

That’s the opportunity Seattle startup Hedgehog is chasing.

Founded in 2022 by CEO Marc Austin, a Cisco networking veteran, Hedgehog develops open-source software designed to make private AI data centers operate more like hyperscale clouds. It has raised $11 million in seed funding, with plans to raise a series A financing round.

We caught up with Austin for the return of GeekWire’s Startup Spotlight to learn more about the 20-person company, the AI networking boom and what surprised him most about building a startup in one of tech’s fastest-moving markets.

In 50 words or less, give us your elevator pitch?

Hedgehog is open-source software that makes AI networking simple. AI clouds and enterprises use it to run GPU networks the way hyperscalers do — deployed in hours instead of months, operated by DevOps teams instead of armies of network engineers, on open hardware with no vendor lock-in.

What problem are you obsessed with solving?

Time to GPU value. A GPU cluster is the most expensive asset most companies will ever buy, and every day it sits idle waiting on the network is money burning. That wait is rarely the hardware — it’s the fabric: weeks or months of scarce network engineers hand-designing, cabling, tuning, and validating it across proprietary CLIs and locked-in vendor gear.

Meanwhile the people told to “own the network” usually aren’t network engineers at all — they’re platform and DevOps teams. We’re obsessed with collapsing that timeline: declare your network like intent in Kubernetes and go from racked GPUs to inference in hours instead of months — on open hardware, no lock-in, no room full of specialists. Cloud-grade networking without hyperscaler headcount.

What surprised you after talking to customers?

How rarely the buyer is a network engineer. It’s platform and DevOps teams, often at AI clouds who just took delivery of thousands of GPUs who are told “you own the network now.” They don’t want to learn BGP; they want a network that behaves like the rest of their cloud-native stack. The other surprise: they don’t just want to run the network, they want to sell it by carving up capacity for their own customers, like a cloud provider does.

How has AI changed the way you build your company?

Twice over.

Our product exists because AI broke traditional networking. Training and inference traffic melts networks designed for web apps.

And AI changed how we build: we use it heavily across engineering, testing, and go-to-market, which lets a small team continuously test every supported device and configuration in our lab and ship with hyperscaler-grade rigor. AI raised the bar for what a startup-sized team can deliver.

What’s one thing people misunderstand about your startup?

That “open source” means hobbyist. The opposite is true: openness is the enterprise feature. Our customers can audit every line of code that runs their fabric, extend it, and never get locked in. Nearly every competitor markets “open networking” while shipping a proprietary controller. Hedgehog is the only one that actually publishes the repo.

What’s the toughest decision you’ve made in the past year?

Betting entirely on Ethernet. We decided open, standards-based Ethernet would win AI networking and put everything behind it. Watching the industry’s largest AI operators now standardize on that same approach makes us feel good about the call — but saying no was hard.

What’s the one piece of advice you give to other entrepreneurs?

Pick the wave, not just the surfboard.

Product decisions are recoverable; betting against a structural industry shift isn’t. Find the standard, the architecture, or the buyer behavior that’s inevitable, align everything to it early, and be patient while the market catches up to your bet.

We’ll know our company has made it when…

Networking is boring again. When a platform engineer stands up a multi-tenant GPU cloud and the network is just a few lines of declared intent that nobody thinks twice about. When “network like a hyperscaler” describes every AI cloud, not just the giants running on Hedgehog, then we will have made it!