Built the way marketing teams actually work.
The best marketers we've worked with all do the same thing. They watch everything: competitors, creators, conversations, the whole category. At a level of attention nobody else in the company has time for.
They turn that into signals. Signals into bets. Bets into decisions. Every cycle, they get sharper. Adology does the watching, so your expertise goes further.
We aren't a dashboard, a generator with a brand voice slider, or a custom GPT pointed at your strategy deck. We watch your live market and turn it into finished work.
Your instincts are already sharp.
Adology gives them the evidence to act on.
/ The shift
Marketing got faster.
The decisions didn't get easier.
You're not behind on production. You're behind on knowing which production decisions are right.
Where we were
A few years ago, the bottleneck
was production.
Briefs took weeks. Concepting was scarce. That world is over. You can put 50 variants on the market by Friday, and generation is effectively free.
Where we are now
More content, more noise, fewer
clear answers.
Which angle, hook, timing? What to scale, what to kill? Ten times the inputs. The same hours in the day.
Where Adology fits
Live market intelligence, running
underneath every call you make.
Adology watches your market, ships that intelligence into your work, and grades itself. So every decision starts from a stronger foundation than your competitors'.
/ The founding story
We spent years wishing
this existed.
"When ChatGPT shipped, I thought competitive intelligence was solved. Then I watched smart marketers still doing the work by hand because generic AI couldn't see what their category was actually doing this week.
We built Adology so the best marketers don't just get faster, they get sharper, because the intelligence underneath them is finally as specific as the work they're trying to do."
James Donner, Founder of Adology
/ The part the rest of the industry skips
This is the part most AI tools skip entirely.
Adology makes specific calls: lead with this angle, kill this concept, expect the category to shift. Each one gets logged. The prediction lands, or it doesn't, and Adology grades itself: where it was sharp, where it was soft, where it was wrong.
It tracks what you act on, what you reject, and how the work performs after it ships. Tuned to your market from day one, it gets sharper about your brand, your category, and your tolerances the longer you use it.
Adology makes a call
A recommendation is made, logged
with a timestamp and the evidence behind it.
The work ships
Adology tracks what was published
and matches it against what it recommended.
The call is graded
Where it was right, where it was soft, where it was wrong. Calibration updates. Next cycle starts smarter.
Most AI tools make recommendations and move on. They never find out if
they were right. So Adology keeps score, on every call it makes, every cycle
it runs. That's what makes the intelligence get better instead of just faster.
/ The team
The team.
Agency operations, machine learning, synthetic audience modeling,
marketing strategy, and content intelligence. We've all felt the problem from different angles, which is why we ended up building Adology.
01
James built, ran, and sold Decoded Advertising agency before starting Adology. Spent years watching great teams work from incomplete intelligence, competitive reads that were three months old, briefs built on instinct rather than evidence, predictions made with no way to track whether they were right. That specific frustration is the reason Adology exists.
James
CO-Founder, CEO
02
Addie brings the synthetic audience capability, calibrated AI audiences that can react to a creative variant before it ships, so teams can pressure-test concepts without burning budget. The ability to know whether an angle is likely to work before you spend a dollar finding out is one of the things that makes Adology's intelligence predictive rather than just descriptive.
Addie
Predictive Intelligence
03
Hal spent six years at Braze, from pre-IPO through its public company phase, and built the first version of its AI agent console. At Adology, he builds the infrastructure the intelligence runs on: the data architecture, the resolution layer, and the compounding systems. The accountability loop was his obsession before it became a product feature.
Hal
Chief Technology Office
04
Tom leads commercial sales at Adology. He comes from the buyer's side of the table: a CMO and agency veteran with multiple exits behind him, who's built and led marketing teams across sports, social, and consumer brands. He knows the pressure Adology's customers are under because he's spent his career under it.
Tom
Commercial Sales Lead
05
Charlene runs marketing strategy, messaging, and go-to-market. She's spent her career building brands and GTM engines from scratch for early-stage startups. Also, the person making sure Adology's own marketing is as intelligence-driven as the product it's selling, which turns out to be a useful forcing function.
Charlene
Chief Marketing Officer
/ Why this matters to us
We started Adology because the best marketers we knew were running on intuition and screenshots. Watching competitor ads
in Slack threads. Drafting reports the dashboard couldn't write. Making predictions with no real way to log or learn from them. The taste was real. The system around it wasn't.
The bet we're making is that you'll run on something different, intelligence that sees the work, makes the call, lives with the
result, and gets sharper every cycle. We're building it for
you. And for the version of us that spent years
wishing it existed.
James, Hal, Addie, Charlene, and Tom
Better marketing
decisions. Starting now.
Free to try. Works in Claude and ChatGPT from day one. No annual
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