AI agents that do
the rival watch.the analysis.

A goal and the number that proves it: cut CPA, lift CTR, hold CPM while reach builds. You attach the metric and the target, and Hawky runs daily from there. Everything else, which campaigns to touch, what to test, what to pause, is what Hawky works out, and every move arrives with the hypothesis behind it before you approve.

Meta, Google, YouTube and TikTok, against the metric you own: ROAS, CAC, LTV, contribution margin, or reach and CPM if the campaign is buying awareness. Hawky plans, launches and optimises across all of them in one loop, over each platform's official Marketing API.

Beyond the goal you set the budget, the platforms, the markets and hard caps on daily spend, CPA or CPM. Everything Hawky proposes arrives as a draft: nothing reaches Meta, Google or TikTok until you press Review & Execute on a pause or Review & Launch on anything that puts something new into an ad set. Drafts queue up as pending actions with the goal or the conversation that raised each one attached, so nothing lands without a name on it, and every action reverses in one click.

Hawky builds and launches them. One brief becomes the campaigns, the ad sets, the audiences and every ratio, named to your conventions and live over Meta's, Google's and TikTok's official Marketing APIs, batched and throttled. The duplicate-and-re-key work that eats a launch day is the part that goes away first.

Hawky decodes video, image, copy, voiceover, on-screen text and audio in one pass, and tracks each element's contribution to CTR, CVR and revenue over time. When the contribution collapses, the alert points at the element and the seconds it happens in, not just at the ad. Every score is graded against your own historical winners in Feather DB, so a low score means low for your audience rather than low against an industry average.

You get your first element-level breakdown inside the day you connect. Hawky proposes from day one, and because every suggestion waits as a draft you can spend the first week reading its calls and launching none of them. The hypotheses, and what Hawky learned from the ones that failed, are on the goal either way. Univest saw a 20% lift in CTR and conversions inside the first week and cut analysis time by 80%.

Ask Copilot in plain language and Copilot answers from your own account, your own history and the competitor ads currently running in your category, with every line traceable back to the campaign, winner or competitor pattern it came from. The same conversation exports as structured slides, so the Monday report is the thread you already had rather than an evening of pivot tables.

Your brand rules go in once, colours, typography, logos, tone and disclaimers, and every variant in the pipeline is rendered against them, so brand drift is not possible. From there the pipeline is data-backed: Hawky reads your past winners to learn what already works on your audience, reads your competitors' past winners and the performance data behind them to see what is working in the category, and generates against both. Every output is then checked against current Meta, Google and TikTok policy before you upload it.

The ad library gives you screenshots you have to remember to go and take. Hawky captures every competitor ad on Meta, Google and TikTok continuously and parses each one for hook archetype, visual structure, message angle, CTA and offer, so you get the strategy behind the ad, the shifts as they happen, and the angles nobody in your set has taken.