Updated June 20, 2026.
Over the last 12 months, across more than $100 million in connected ad spend, the accounts Hyper runs have seen a consistent 54% lift in ROAS and 20% to 27% lower CPMs, about 24% on average.
We built the system that does this on Supabase, and Supabase featured the story. This is how Hyper works, why the operating layer matters as much as the model, and why it's the best AI platform for Meta and Google ads.
The result
Most AI marketing tools stop at advice. Hyper does the work: research the brand, build the campaign, launch it into Meta and Google, watch it, change what breaks, and explain what happened.
The point of that system is performance, so start with the account-level result.
Across the accounts Hyper runs, ROAS came up about 54% over 12 months and CPMs came down 20% to 27%, about 24% on average, versus each account's prior baseline. Those are blended averages across a wide mix of businesses instead of a single best-case screenshot.
The reason the numbers hold is that the same few jobs drive the lift every time.
What drives the lift
The 54% lift comes from a handful of jobs done well and done constantly.
Creative testing with a real feedback loop. Meta rewards a steady supply of genuinely different creative. The win rate on any single ad is low, so the team that tests the most finds the winners first. Hyper watches for fatigue, kills what is tired, and keeps new variations flowing.
Market scans before creative briefs. The agents scan Instagram and TikTok for category trends, Reddit for customer language, and the Meta Ad Library for competitor moves. That gives the creative team a brief grounded in what the market is already saying.
Creative built for production. Hyper doesn't generate ads in a vacuum. It hands the team usable references: the 10 trends worth studying, the competitor concepts worth answering, and the start frames or product shots to build from. The point is quality of tests before variant volume.
Audience signals and customer segmentation. Advantage+ and Andromeda absorbed many manual controls. Hyper works with that reality by shaping signals around customer segments, conversion quality, and account history, which is a major piece of the CPM reduction.
Execution inside the account. A recommendation that waits for a human to action it loses time. Hyper ships the change.
Execution
The difference is simple: instead of building the campaign by hand and moving every setting yourself, you give the job to Hyper and the agent runs it.
It builds and launches across Meta and Google from a chat. It pulls the brand's products, writes and generates creative, sets audiences, budgets, and bids, syncs the campaign to the ad manager, reads performance back out, and optimizes. The build, launch, optimize, and report loop runs in one place.
That end-to-end execution is what most of the category is missing. It is also what the performance numbers depend on.
The harder problem is shipping correct work at scale. A raw frontier model can reason about marketing if you brief it well. It can draft a plan, critique creative, and explain an account. By default, it has no live account state, no platform procedures, no memory of prior account decisions, and no authority to ship the work.
Hyper adds that operating layer.
We spent 12 months building depth into the Meta and Google integrations specifically, through a custom API that we expose as an MCP, a CLI, and an SDK. That parameter-level coverage is what lets the agent build and change campaigns instead of describing what a person should click.
Autonomy
People ask how hands-off it is. The honest answer is: as much as you want.
The agents handle repetitive execution on their own: daily checks, pacing, fatigue swaps, reporting, and account readouts. Decisions that deserve a human, like approving a new angle or a budget jump, come to you with the context already gathered.
You stay in control of strategy. The agent does the work.
That matches where production agents are strongest today. Vercel's agent team describes the highest-likelihood agent projects as repetitive work with measurable outcomes, where the task is dynamic enough for AI and bounded enough to evaluate. Paid ads fit that pattern: every day has new data, and the goals are concrete.
Anthropic's agent engineering writing makes the production lesson even sharper: agents need tools, state, and operational guardrails. The model matters. The system around it decides whether the model can act reliably.
Speed and cost
Two numbers matter here.
You can test about 20x faster because the agent can spin up, launch, and read variations without a person touching the levers between each one. More useful tests, found faster, is most of how the win rate improves.
On the infrastructure side, because Hyper agents query a real database with SQL instead of calling each ad platform's API live, data retrieval is 10 to 100x cheaper. Supabase wrote about this directly in its Hyper customer story: agents write SQL against synced marketing data instead of wrestling with rate-limited platform APIs.
That cost reduction matters because the agent is always checking the account. Daily pacing checks, fatigue reads, creative comparisons, reporting, and customer-segment analysis all require data access. If every read has to go back to each platform API in real time, the system gets slow and expensive. If the agent can query Postgres, it can work like an analyst who already has the warehouse open.
Why Hyper is a mixed-model, multimodal platform
The best AI for Meta and Google ads is a platform.
GPT-5.5 and Claude Opus are excellent reasoning models. Lower-cost models are excellent for high-volume work. Vision models can read creative, landing pages, charts, and screenshots. A production ad agent needs all of that, plus memory, account data, platform tools, brand context, and permission to execute.
Hyper is built as a mixed-model, multimodal system for that reason. The platform routes the job to the right model class, attaches the right account context, calls the right marketing tools, and records what happened so the next decision starts from history instead of a blank prompt.
OpenAI lists GPT-5.5 at 5 USD per 1M input tokens and 30 USD per 1M output tokens, with GPT-5.4 mini at 0.75 USD input and 4.50 USD output. Anthropic lists Claude Opus 4.8 and Opus 4.5 at 5 USD input and 25 USD output per MTok, with Claude Haiku 4.5 at 1 USD input and 5 USD output.
Those numbers explain why routing matters. A serious agent uses cheaper models to tag 400 ads, summarize a CSV, or classify search terms. It saves Opus-class reasoning for calls where judgment actually changes the account: budget moves, creative direction, diagnosis, pacing, and tradeoffs on a $2M/month campaign.
| Layer | What it contributes | How Hyper uses it | Production implication |
|---|---|---|---|
| Frontier reasoning models | Long-horizon judgment, planning, hard tradeoffs, senior review | Budget decisions, account strategy, ambiguous performance reads, pushback handling | High-stakes calls get the strongest reasoning available |
| Lower-cost models | Fast classification, extraction, summarization, tagging, first-pass drafts | Creative tagging, query grouping, report prep, anomaly triage, bulk account hygiene | High-volume work stays fast and affordable |
| Vision and multimodal models | Reads screenshots, charts, creative frames, landing pages, and visual ads | Creative QA, hook analysis, landing-page review, competitor creative scans | The agent can reason over what marketers actually look at |
| Retrieval and memory | Prior actions, account history, brand context, creative history, past decisions | Keeps every recommendation tied to what shipped and what happened after | The agent improves from account history instead of restarting from the latest prompt |
| Marketing tools and connectors | Meta, Google, analytics, CRM, search, social, creative, and reporting surfaces | Builds, launches, optimizes, and reports from one operating layer | Reasoning reaches the ad account instead of stopping at advice |
| Hyper platform | Mixed-model routing, multimodal context, account data, tools, skills, memory, and execution | Runs the full loop across Meta and Google ads | $100M+ connected spend, +54% ROAS, 20-27% lower CPMs, 20x faster creative testing |
The more useful benchmark is runtime performance: what the model can see, what it can change, and what it remembers after the campaign moves.
| Benchmark task | Frontier model by API | Frontier model in Copilot | Frontier model in Claude | Frontier models in Hyper |
|---|---|---|---|---|
| Read the account before a budget call | Depends on the retrieval layer your team builds | Works from pasted exports or workspace context | Works from uploaded files, screenshots, and connected context | Reads synced spend, creative, attribution, and prior account decisions |
| Review creative and landing pages | Strong when image and page context are passed in | Useful for docs, briefs, slides, and general review | Strong for long-context analysis, images, and artifacts | Reviews creative with performance history, competitor scans, and brand context attached |
| Ship the change into Meta or Google | Requires custom tools, policy checks, logging, and approvals | Stops at recommendation and draft work | Stops at recommendation and draft work unless tools are configured | Builds, launches, optimizes, and reports through the agent layer |
| Learn from the result next week | Requires custom memory and outcome checks | Keeps conversation and workspace context | Keeps project context where configured | Records actions, decisions, outcomes, and account history for the next run |
The model comparison only matters after the system comparison is clear.
A raw frontier model can critique a campaign if you paste in the right context. A lower-cost model can process a lot of account material cheaply. A multimodal model can read the creative. Hyper combines those model classes inside one agent platform and gives them the data, tools, memory, and operating procedures needed to run ads.
Copilot and Claude are strongest when the work is document-shaped: analyze exports, write briefs, review screenshots, draft reports, and explain tradeoffs. The API path is strongest when a team wants to build its own retrieval, tools, approvals, memory, and outcome checks around frontier models.
Hyper is built for account-shaped work. The same class of model wakes up inside the ad system, with live data, creative context, platform tools, and the memory of what happened last time.
That is the comparison worth caring about. Can the system move from signal to decision to shipped account change, then remember what happened? Hyper can.
Twelve months across verticals
We have run this for 12 months across a real spread of businesses: restaurants, dentists, courier services, caterers, chiropractors, ecommerce brands from small shops to large catalogs, lead-generation businesses, and AI search work.
The cleanest proof is the head-to-head test. We ran Hyper on 50% of a marketing agency's client social and SEO accounts and left the other 50% with their human team during the same window. Hyper made the execution decisions on its half. The agency benchmark case study shows what happened.
The ecommerce case study walks through a single DTC brand spending about $2M/month on Meta. The original case study shows the operational side: one team freed up 29 hours a week.
Who uses Hyper
The same loop runs for very different businesses.
Marketing agencies
Agencies run Hyper across client accounts, with one operator covering work that used to require a larger team. In our 50/50 benchmark, the Hyper-run half lifted ROAS 61% against the agency's own human-run baseline.
Ecommerce brands, small to large
Hyper works for first-product shops and larger DTC brands. The ecommerce case study covers a brand spending $2M/month on Meta, where 54% more ROAS and 28% lower CPMs changed the economics of the account.
Local service businesses
Restaurants, dentists, courier services, caterers, chiropractors, and other local operators often have no marketing team at all. For them, Hyper is the operator watching the account every day.
In-house marketers and founders
Lean in-house teams and founders need an operator they can direct. One team freed up 29 hours a week after moving reporting and campaign setup into Hyper.
How your data is handled
Every account on Hyper is anonymized. We don't train models on your data, and we don't hand it to anyone.
We use aggregate, anonymized signals from across the network to make the system smarter for everyone, the same way ad platforms learn from aggregate performance. You can opt out, and nothing changes about how your own agent works.
Built on Supabase
The data layer is the reason the system works, and a lot of it runs on Supabase.
Hyper syncs every connected platform into Postgres. That turns scattered ad and analytics data into one queryable source of truth, so agents can write SQL against it instead of calling platform APIs live.
Supabase captured the point in its customer story: "Agents are great at writing SQL. It's way more efficient. A literal 100x cost reduction compared with if the agent were to go out and try to get that data."
Security lives there too. Every customer gets isolated data, with Row Level Security enforced at the database layer. One workspace can't see another workspace's data. On top of that we use Postgres as the source of truth, pgvector for agent memory, Realtime for live signals, Edge Functions, and Storage.
Supabase wrote the full story here: Hyper builds AI marketing agents on Supabase.
Where Hyper fits
If you want an AI that runs your Meta and Google ads, and you want the performance to show up where it counts, that's what Hyper is.
Hyper set up two custom agents for us. We used to spend 20+ hours a week on that. Jimmy Smith, Founder, Slice of Pie Marketing
It was running in under 5 minutes. Conor Drake, CMO, Confidence Media Partners
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Frequently Asked Questions
Frequently asked questions
Q: What results does Hyper deliver on Meta and Google ads?
Across the accounts Hyper runs, over 12 months and more than $100 million in connected ad spend, ROAS rose about 54% and CPMs fell 20% to 27% (about 24% on average) versus each account's prior baseline. The lift comes from faster creative testing, better briefs, customer segmentation, and agents executing changes inside the account.
Q: How is Hyper different from GPT-5.5 or Claude Opus?
GPT-5.5 and Claude Opus are frontier models. Hyper uses model intelligence inside a marketing operating layer with account data, Meta and Google tools, memory, marketing skills, and permission to execute. The model can reason. Hyper can run the account.
Q: Does Hyper use lower-cost models too?
Yes. Lower-cost models are useful for high-volume work like classification, extraction, summarization, and draft generation. Hyper routes work by task type, using stronger models where judgment matters and cheaper models where speed and volume matter.
Q: Why does Supabase matter to Hyper?
Supabase gives Hyper a queryable data layer. Instead of calling every ad platform API live, agents query synced Postgres data with SQL. That makes data retrieval 10 to 100x cheaper and gives agents a consistent source of truth for performance, reporting, and account history.
Q: How much does Hyper cost?
Hyper has a 49-day trial, then 49 USD/month. That includes the agent layer for running marketing work across connected tools.