Marketers spend half their day looking at data and the other half wondering if it's right.
That single sentence is why Hyper exists — and it's also why we built the whole platform on Supabase. Today Supabase published a customer story about us, so we figured it was a good time to share the full picture from our side: why we picked Postgres as the brain for marketing agents, what we actually shipped on top of it, and what it unlocks for the teams running their marketing on Hyper.
The problem we set out to fix
Modern marketing lives across a dozen platforms — Meta, Google Ads, GA4, Search Console, Shopify, HubSpot, Klaviyo, Stripe, TikTok, LinkedIn. Every one of them has its own schema, its own API quirks, its own sampling rules, its own version of "conversions."
Most teams respond the same way. They buy another dashboard tool, wire up a data warehouse project that takes a quarter to ship, or hire an analyst to manually reconcile numbers every Monday morning. And the core problem never actually gets solved: the person running the marketing still can't trust the number in front of them.
Our bet with Hyper is simple:
If an AI agent is going to run your marketing, it needs to live on top of your actual marketing data — not screenshots, not reports, not a PDF export from last Tuesday.
That means real tables. Real SQL. Real-time updates. Clean joins between ad spend and revenue. And it all has to be boring, battle-tested infrastructure that we don't spend our engineering time babysitting.
That's Postgres. And the fastest way to run serious Postgres with the rest of the stack already attached — auth, storage, realtime, edge functions, pgvector — is Supabase.
Why Supabase became the foundation
When we were prototyping Hyper, we went through the usual shortlist. Run our own RDS. Use a big data warehouse. Glue together a handful of SaaS analytics tools.
Every path kept running into the same wall: we didn't just need a place to store data. We needed a place where agents could read it, write to it, listen to it change, and attach unstructured context to every row — all with the same low-latency guarantees you'd expect from a normal backend.
Supabase gave us that in one package:
- Postgres as the single source of truth for every connected platform
- pgvector so agents can semantically search past campaigns, creatives, briefs and reports
- Realtime so live spend, pacing and anomaly signals stream into the product the second they change
- Row Level Security so every workspace is isolated by default, no extra middleware
- Edge Functions so we can ship platform-specific webhooks and transformations in hours, not sprints
- Storage for creatives, exports, audit artifacts and every generated asset in a workflow
We didn't have to assemble that. It was already there, in one project, running on infrastructure that scales with us instead of against us.
What we actually built on top of it
When you connect a platform to Hyper — Meta, Google Ads, GA4, Search Console, or anything else — we do three things automatically:
- Create the tables. Every platform gets a normalized schema in your Postgres, mapped into a shared model so
campaign,ad_group,ad,keyword,event,orderall speak the same language across sources. - Build the pipelines. Historical backfill plus an ongoing real-time sync, with retries, dedupe, schema evolution and cost-aware fetching handled for you.
- Wire up the agents. The second the data lands, our agents can query it, join across it, and act on it — directly in SQL, directly in your workspace.
The important part is that this isn't a dashboard. It's a data layer.
You can ask an agent, "Why did our CAC jump last week?" and it will literally run the query, pull the breakdown, compare it to the prior 28 days, surface the specific campaigns and keywords that changed, and propose edits across Meta and Google Ads — all using the same Postgres your other tools can read from.
Insight and execution stop being two separate apps. They become the same motion.
Agents with complete context
Most "AI marketing" products are chatbots on top of an analytics API. You ask a question, it makes an API call, summarizes what came back, and that's the interaction.
That breaks the moment you need real judgment. You can't optimize a Performance Max campaign if you can only see today's stats. You can't rewrite a creative brief if you can't see what worked six months ago. You can't decide to shift budget from Google to Meta if the numbers don't reconcile to revenue.
Because Hyper runs on Postgres, every agent has access to:
- Every metric from every connected platform, at row-level granularity
- Every creative, brief, report and conversation, indexed with pgvector for semantic recall
- Every action an agent has previously taken — so it can learn from its own history instead of starting from zero each session
That's how you go from "ChatGPT with a marketing skin" to an agent that can genuinely run your marketing. The context layer is the product.
From "$50M spent on ads" to "what's a pixel"
One of our favorite things about Hyper is how wide the user spectrum is.
We have power users who have spent more than $50 million on paid media and want agents that understand attribution windows, incrementality, and PMax asset group behavior. They use Hyper the way a senior media buyer would — structured campaign reviews, automated bid adjustments, weekly performance digests delivered straight into Slack.
And we have founders and operators who don't know what a pixel is, have never opened Ads Manager in their life, and just want to point Hyper at their Shopify store and say "grow this."
Both groups work. Because the agent is doing the translation — between platforms, between concepts, between "what you said" and "what needs to actually happen in the ad account." The infrastructure doesn't care how sophisticated the prompt is. It cares that the data is correct, live, and addressable.
That's only possible when the underlying platform is as strong as the model layer on top of it.
The team behind the customer story
We'd be stupid not to call out the humans.
Huge thanks to Grant Huston and Prashant Sridharan at Supabase for making this partnership possible, for believing in what we were building before it was obvious, and for being genuinely responsive partners as we've scaled up on the platform. The Supabase team has been one of the best vendor relationships we've had — which is a weird compliment to give a database company, but it's true.
Go read Supabase's write-up of the Hyper customer story. It covers the architecture side in more technical detail than we did here, and it's a genuinely good read if you're building anything agentic on top of Postgres.
What this means if you're running marketing
The short version: if you want an AI agent to actually run your marketing, it has to sit on top of a real data layer — not a wrapper, not a dashboard, not a set of scraped reports.
Hyper is that stack, already assembled, already secure, already live, already running for teams every day. Supabase is the reason we can ship it the way we do.
If you want to see what it looks like when agents have full access to your marketing data, start a free workspace on Hyper — connect Meta or Google Ads and we'll build the tables, pipelines and agents for you in a couple of minutes.
And if you're a Supabase user who also runs marketing: yes, we're building exactly the thing you wish existed.