We've run Google Ads for 15 years. We built the AI copilot behind them.
DataMind finds where you're losing money:
junk search terms, dead geos, broken bidding
And turns it back into sales.
Every fix priced in $/month. Applied in one click.
Every number is computed, not guessed — zero hallucinated figures.
Actual findings, straight from the DataMind interface.
| parking fees at eastvale [BROAD] | 98% alien |
| eastvale airport parking rates [BROAD] | 84% alien |
| park on parking [PHRASE] | 99% alien |
Switch them to a stricter match type instead of pausing — the keyword itself converts, its expansions don’t.
Take control of your search term quality
Find my extra salesFree · 4 minutes · 183 checks| India | $412 · 0 conv |
| Singapore | $236 · 0 conv |
| Colombia | $181 · 0 conv |
Switch location targeting to “Presence” and exclude the three countries — undo in one click.
Focus your spend on the geographies that convert
Find my extra salesFree · 4 minutes · 183 checksTarget tROAS 1.08 · achieved 1.03 · hits budget cap. 5 landing pages flagged “underperforming” ($2,270) — all inside this one campaign.
Fix the target and the cap first — page losses can’t be tackled while the strategy cycles through cheap traffic.
Make your bidding settings work for you
Find my extra salesFree · 4 minutes · 183 checksAd 793900213405 (84% of group spend): CPA $18.76 · Ad 678895774932 (78%): CPA $18.64 · Ad 785807759518 (22%): CPA $18.07
Consider pausing the flagged ads — the group keeps rotating spend into them at above-campaign cost.
Shape your campaign structure around your keywords and messaging
Find my extra salesFree · 4 minutes · 183 checksStructure (ad groups/ads) loss $946 over the period · efficiency 0.75× of account average · disapproved ads limit delivery. Paused on 13.07 — $589/mo of relief already collected.
Already paused — keep it that way until the disapprovals are fixed; the account is better off without it.
Check your ads and assets
Find my extra salesFree · 4 minutes · 183 checksThe numbers are real — this is what actually leaked. Only client, campaign and place names are changed.
Every one of these accounts looked fine in the dashboard.
Leaking anyway.
Your dashboard just lied to you. Here's why these numbers can't.
A live audit generating — not a chatbot typing.
Zero hallucinated numbers, by design. Every number comes from a deterministic engine — Bennett decomposition even splits growth into “budget moved” vs “campaigns actually changed” — and every identity is verified by runtime sum-checks. The LLM physically can't cite a figure that isn't in the data: a validator rejects the answer.
Clinical-trial-grade statistics. Eyeballing 40,000 search terms produces hundreds of false “problems.” Every term runs through Bayesian shrinkage and FDR control — only truly confirmed losers survive, each with a counterfactual attached: “remove these — account ROAS improves by X.”
One culprit, not 17 fix-lists. The AI cross-references every cut of your account and finds the overlaps: when one campaign shows up in semantics, geo, and landing-page losses at once, you fix that campaign — not three lists of symptoms.
Dead campaigns don't give advice. The engine tracks every campaign's lifecycle: stopped ones never enter recommendations, and conclusions freeze after recent settings changes — “wait for the June 23 budget change to play out first.” The savings we promise are savings you can actually collect.
It speaks your goal's language. If your target is CPA, every formula in the report runs in CPA optics — a double gate makes a mixed-metric report physically impossible to save, and a leak validator catches even the word “revenue” in a CPA narrative.
You'll never have to read them. Your part is the first screen: what's leaking, what fixing it is worth, apply in one click.
But underneath every recommendation sits a nerd-level report — Bennett decompositions, Simpson's paradox checks, counterfactuals, statistical confirmations — proving why each fix made the list. So when your specialist, your agency, or your CFO asks “says who?” — you hand them 77 pages of “says the math.”
Sign in with your work email, then one button: Connect Google Account. The connection is transparent — you'll see it in Connections and can remove it in one click.
DataMind pulls every goal from your account and you mark the one that matters — purchase, lead, booking. Your goals can be named anything, even “cart in Albanian”. You match them to your funnel once, and the system knows what actually counts as a result.
One click. The last 60 days of data, about 4 minutes — and you get the list: what's leaking, what fixing it is worth in $/month, and an Apply button next to each fix.
Your Mondays, before DataMind:
Consider them done.
| Google Ads interface | Dashboards & alert tools | Claude / ChatGPT via MCP | LLM-wrapper startups | DataMind | |
|---|---|---|---|---|---|
| Shows | Recommendations shaped by Google's own interests | Plenty of false alarms — and none of the trends hidden inside averages | An answer to what you asked | Risky hypotheses built on data nobody re-checked |
|
| Your part | Hours of exports and pivot tables | You still dig through the data | Write prompts, then double-check the answers | Copy-paste advice into your account by hand | Review the fixes, click Apply |
| Math | Sorts by spend, not by distance to target | Crude thresholds — can't tell noise from calibration from a real trend | The LLM does its own arithmetic on raw rows — a different answer every run | Same thing, dressed as a product; sooner or later it quotes a figure that isn't in the data | Deterministic engine computes; AI analyzes; validators check every figure |
| Methodology | A 15-year-old interface | You still do the sifting yourself | Quality = your prompt. It answers what you ask — and nothing you didn't | General knowledge, no rails | 183+ checks from 400+ pages of written rules, written by specialists with 15+ years of running accounts |
| Month over month | — | Static rules, same blind spots | Ask twice, get two different answers | Ask twice, get two different answers | The same 183+ checks every run — results you can track and compare over time |
| Budget planning | One campaign at a time; 100 exports nobody aggregates | — | — | — | Whole portfolio, mixed scenarios: “+10% here, −20% there, switch this off” |
Even before the AI touches anything, DataMind replaces the part of your job you hate: exporting, VLOOKUP-ing, and rebuilding the same spreadsheets every week. Every cut of your account — campaigns, semantics, geo, landing pages, ads and assets — is already aggregated, grouped by theme, and sortable. And every row is tagged: dynamics (“ROAS Increase,” “CTR Campaign Decrease”), assessment (“Good Campaign,” “Off Plan”), red flags. The kind of markup you'd spend hours building by hand — already on the screen.
| Campaign | Country/Territory | Cost | Impr. | CTR | CPC |
|---|---|---|---|---|---|
USA_PMAX_Interests Performance Max | United States Near PlanNORAM · First Tier economy | $65,598.62 $40,404 > $25,195 (−38%) Major CountryDecrease | 378,392 193,332 > 185,060 (−4%) | 9.50% 10.11% > 8.85% (−12%) AverageDecrease | $1.83 $2.07 > $1.54 (−26%) AverageDecrease |
WEST_Search_Mixed Search | United States Near PlanNORAM · First Tier economy | $43,661.17 $21,954 > $21,708 (−1%) Major CountryFlat | 136,860 66,809 > 70,051 (+5%) | 10.92% 10.96% > 10.87% (−1%) | $2.92 $3.00 > $2.85 (−5%) |
A campaign in your top 5 by spend — tagged “Poor Campaign.” Money leader, results loser. Visible in one glance at the bubble chart.
Half the budget sitting in unclustered semantics marked “Off Plan” — spend flowing into keywords nobody grouped, nobody watches.
A campaign where cost jumped +117% while CTR slid −12.7% — flagged automatically, before you'd ever have noticed it in the interface.
Smart Bidding sets the prices you pay. Performance Max picks its own audiences and pages, then reports back in aggregates you can't question. After 15 years of running paid media, we built the AI that sits on your side of the table — and checks theirs.
One machine spends the money.
The other checks the receipts.
Agencies and most platforms charge a percentage of your media spend — scale your budget, and you pay more for the same work. DataMind is a flat subscription. Your spend is your business.
The first audit typically finds more monthly waste than DataMind costs for a whole year.
Run it on your own account and compare: what the report says is actually happening, versus what you think is happening. It takes about four minutes. If everything checks out — great, you'll sleep better tonight. If it doesn't, you just found something worth far more than four minutes of your time.
Find my extra sales4 minutes · 183 checks · disconnect anytime in one click
An LLM on its own is a raw stone: brilliant in flashes, unreliable exactly when the money is on the line. What turns it into something you can take into a board meeting is the setting cut around it — deterministic math, 400+ pages of audit rules, and seven layers of validation, written by people who have spent 15 years running paid media for brands you already know.
DataMind was built inside Affect Group, a performance marketing agency that has been running paid media for 15 years — for PepsiCo, Mercedes-Benz, Mastercard, Durex and Peet's Coffee, alongside hundreds of smaller accounts. Every check in the engine started as something we needed ourselves, on a real account, with real money moving through it.
We are the official performance marketing partner of the Shorty Awards in the US — the awards often called the “Oscars of the advertising world.” Our team also sits as head judges at the Effie Awards and the Shorty Awards: we spend part of every year deciding what qualifies as world-class work, which is a useful habit when you are writing the rules an audit engine follows.
Our blog and expert analyses are read by more than a million people worldwide, and our benchmarks get quoted when the industry press needs a number it can stand behind.
With ChatGPT entering the advertising space, EMARKETER published an analysis on pricing — and referenced Affect Group as an expert source for Meta Ads CPM benchmarks.
We speak at international forums and conferences, run a podcast with expert guests for the Shorty Awards (300k views), and write for Medium and Better Marketing, a platform with over 135,000 subscribers. The methodology inside DataMind isn't a secret sauce — it's the same thinking we publish under our own name, argue about in public, and get judged on.
Short breakdowns from inside real ad accounts — what actually moves the numbers, no jargon. A couple of minutes a week.
