About LinkBloom
Creative work should be evidence-based,
not a roll of the dice.
This is the first thing we believed when we started LinkBloom — and it's almost the whole story.
Most AI ad-image tools are, at their core, a gamble.
Throw a few prompts at a model and hope for the best. The product is never really understood, the selling points are guesswork, and whether it looks good comes down to luck. That's not creative work — it's a raffle.
We chose a slower, more deliberate path instead.
First, we turn the product into a graph of verified facts, so every creative hypothesis can be traced back to something real. Only then does it deserve to be called creative — a claim that can't hold up shouldn't have existed in the first place.
Product facts don't get rewritten by AI.
Interfaces, selling points, numbers — they stay exactly as they are. We treat them as explicit constraints, then let layout and visual variation flex within a controlled range. Every important output still goes through quality review and human sign-off.
Quality can't be a gut feeling.
So every image is reviewed across 7 dimensions — brand, visual quality, copy, product accuracy, channel fit, compliance risk, novelty — and flagged as auto-approved, needs review, or rejected. "Good" should be defined and tested, not just "looks fine to me."
The ability to keep producing great creative shouldn't be reserved for big companies.
An engineer, an indie developer, a brand just starting its affiliate program — all of them deserve a creative team on call. We want to take what used to require a big budget and make it an everyday tool for everyone.
So what LinkBloom actually does is turn "from product to a full creative set" into something fast, evidence-based, and accessible to anyone.
Quality review
"Good" broken into 7 questions you can answer separately
Every generated image goes through the table below. The review returns scores and risk flags, then lands in one of three buckets — auto-pass, needs human review, or failed. Its job is to surface what looks wrong, not to make the call for you.
- 01Brand consistency
- Do the palette, type, logo placement, and tone still read as the same brand? Multilingual versions drift here first: change the language, the layout loosens, and the brand feel goes with it.
- 02Visual quality
- Is there a clear focal hierarchy, enough breathing room, and no cropped subject? Generation models are unreliable on this one — a batch often contains one or two collapsed compositions, so every frame gets checked, not a sample.
- 03Copy
- Can the headline be read at a glance, are there typos or bad line breaks, is the claim a single clear sentence? On-image text is rendered deterministically rather than drawn by the model, precisely so this stays controllable.
- 04Product truth
- Are the features, UI, and numbers on screen things the product actually has? This is the one we weigh most — a beautiful ad that lies costs far more than an average one that doesn't.
- 05Channel fit
- Do the ratio, safe zones, and information density match the placement? The same message needs a different amount of on-image text in a vertical feed than in a horizontal display slot.
- 06Compliance & safety
- Any inflated promises, absolute claims, unsupported third-party endorsements, or sensitive phrasing? What this catches is mostly copy that reads powerfully but can't be backed up.
- 07Novelty
- Is this yet another obviously templated layout? Batch production drifts toward sameness, so this dimension flags versions inside a batch that repeat each other.
Questions we get about the team
- Who builds LinkBloom?
- A small team with engineering and product backgrounds, shipping our own go-global products. We built this tool to fix our own problem first: the product kept shipping, creative kept landing last, and waiting on design queues was slower than solving it ourselves. There's no funding story here, and we're not going to invent one.
- Why not just use a general-purpose AI image tool?
- General tools solve for "make a good-looking image." Marketing creative has to solve for "make an image about this product, for these people, that fits this placement, and doesn't lie." The difference isn't the model — it's the step before it: product facts have to be read first, constraints have to be locked first. Skip that and you spend the time re-deriving it through prompt iterations.
- Will the output obviously look AI-generated?
- It depends on two things: whether the model rewrote your product content, and whether the layout is a template. We keep the product subject and the text outside the model's editable area — it only generates scene and atmosphere — while the novelty dimension flags versions that repeat each other inside a batch. Together those suppress most of the AI look, but they can't guarantee every frame passes. That's why review results are shown to you rather than published for you.
- With automated review, do humans still need to look?
- Yes. The review reliably catches enumerable problems: fabricated numbers, unreadable text, off-brand palettes, wrong dimensions. What it can't judge is whether this tone suits this campaign, or whether this claim is too heavy for this audience. So we treat it as a first-pass filter — the publishing decision stays with you.
- Will my product materials be used to train models?
- No. Your product materials are only used to generate creative inside your own workspace, and never enter model training. The exact scope of data handling is written up in the privacy policy.
If you believe creative work should be evidence-based too,
we'll probably get along. Start free, no credit card required.