AI Visibility Optimization · Recommendation Engine

Every signal in. One ranked list out.

The platform watches your AI visibility from every angle, and each view turns up something to fix. The Recommendation Engine pulls all of it together — audit issues, citation gaps, competitor gaps, prompt gaps — weighs them on one scale, and hands you a single, impact-ranked list of what to do next.

See Your Priorities Book a Demo

7-day free trial, cancel anytime. Your ranked list in about a minute.

Audit issuesCitation gapsCompetitor gapsPrompt gaps

Ranked list

1 What's the fastest way to add enterprise SSO to my B2B SaaS app? Quick win

2 How can businesses improve their compliance posture with robust SSO? Content

3 What are the pros and cons of using Forgerock for SSO? Big bet

GrackerAI dashboard showing AEO and GEO citation tracking across ChatGPT, Perplexity, Claude, and Gemini

You do not have a knowing problem. You have a deciding problem.

Every tool tells you something is wrong. None of them tells you what to do first.

Monitoring, scores, competitor data, citation maps, audits. Each hands you a dashboard and a dozen things you could do. But you have a small team and a hundred possible actions, and no honest way to compare a schema fix against a review-site placement against a new comparison page.

Problem one

Many signals, no single answer

Every feature is right about its own corner of the problem. Stacked on top of each other, they add up to noise, not a plan you can act on Monday morning.

Problem two

The tools do not talk

The audit, the citation map and the competitor view each optimize for themselves. Nothing weighs them against each other, so there is no one shared priority.

Problem three

Impact and effort are invisible

Some fixes are quick wins. Some are months of work for little gain. Without a shared model you cannot tell which is which until you have already spent the time.

One brain that takes every signal from across the platform and returns a single, impact-ranked list of what to do next.

The brain of the platform

Six streams of findings become one list

Everything the Intelligence side discovers flows into one place. The engine takes those very different findings, scores them on a single scale, removes the overlap, and orders what remains — so what reaches you is not six dashboards but one decision.

Six kinds of finding in. One ordered plan out.

AUD Technical audit issues

CIT Citation source gaps

CMP Competitor visibility gaps

PRO Prompt coverage gaps

SCO Visibility score drops

RAD Brand radar signals

LIST One impact-ranked action plan

The hard part we solved

These findings are not comparable. A schema error, a missing review-site listing, and a prompt a rival owns are different in kind, so you cannot simply pile them into one list and call the top item the priority. To rank them honestly, the engine converts each into a single common currency: the estimated lift to your AI visibility if you fix it. That estimate blends the demand behind the finding, your current position, and how much the lever actually moves answers.

Only once everything speaks the same unit can a technical fix be weighed against a content piece or a placement on equal terms. Getting that conversion right, across signals that look nothing alike, is the whole job — and it is what turns a stack of alerts into a decision you can trust.

Impact against effort

The quick wins rise. The big projects wait their turn.

A high-impact fix is not automatically the right next move if it takes a quarter to ship. So every recommendation carries two estimates, not one: how much it would lift your visibility, and how much work it is — whether that is a quick content edit, an engineering change, or a placement to earn.

Plot the two together and the order becomes obvious. The changes that move you most for the least effort sit at the top. The expensive projects still appear, but where they belong — not at the front by accident.

Every fix scored on impact and on effort
Quick wins surfaced ahead of long projects
Big bets kept visible, ranked on their real return

The list you work from

One plan, deduped and in order, with the reason attached

Several findings often point at the same underlying fix, and some fixes depend on others. The engine groups related signals into a single recommendation and sequences them sensibly — so you are never told to publish a page before the page can even be crawled.

Every item carries its reason and its source. You always know why something is on the list, where it came from, and what it will move — which makes the plan easy to trust and easy to hand to whoever owns it.

Overlapping findings merged into one recommendation
Dependencies respected, so fixes happen in a workable order
Each item tagged with its source feature and its reason

GrackerAI Recommendations list of actions to improve AI visibility

A living list, not a report

It re-ranks itself, and learns from what worked

A static plan goes stale the moment the market moves. As monitoring, radar and the audits surface new findings, the list re-orders itself — so what you open next week reflects the world as it is next week, not a snapshot from a kickoff call.

And when you complete a recommendation, the engine watches what happens to your visibility and feeds that back. Its sense of what actually works gets sharper over time, so the advice keeps improving the longer you use it.

New findings re-rank the list automatically
Completed fixes are measured against real results
The scoring sharpens as the engine sees what moves you

How it compares

A pile of dashboards is information. A ranked list is a decision.

Separate tools and dashboards	Recommendation Engine
Each reports its own findings	Combines every signal into one list
No way to compare across them	Scores everything in one common unit
Shows problems, not priorities	Ranks by impact and effort
Repeats the same fix in five places	Merges related findings into one action
A static export	Re-ranks as new findings arrive
Never learns from outcomes	Sharpens from what actually worked

In action

What a single ranked list changes

Two situations where the engine turns a scattered backlog into a clear next move.

Run a lean team

When it is just you and one other person, you cannot afford to work on the wrong thing

Open the list and take the top item, already chosen for impact and effort.
Ship it, whether it is a quick fix or a brief for content.
Mark it done and let the list re-rank around what is left.
Repeat, always working on the highest-leverage thing available.

Report to leadership

When you have to defend where the team's time went, the engine gives you the reasoning, not just the result.

Show the ranked plan and the impact estimate behind each item.
Point to which signal each recommendation came from.
Track completed fixes against the visibility lift they produced.
Make the case for the next quarter with evidence, not opinion.

"GrackerAI helped us establish market presence and reach the right buyers. A brilliant approach to growth."

+69% AI visibility

+41% business impact

Andy Agarwal, Head of GTM, Kveeky

What it means for your team

Stop staring at dashboards. Start at the top of the list.

The hardest part of AI search was never finding problems. It was knowing which one to solve first with the time you actually have. The Recommendation Engine answers that every day, so your team spends its hours on the few changes that move you most.

Every signal from across the platform, gathered into one list

Impact-ranked the highest-leverage fix surfaced first

Effort-aware quick wins separated from long projects

Always live re-ranks as new findings arrive and fixes land

Connected to

It decides what to do. Here is what feeds and executes it.

Content Engine

When a recommendation calls for content, it flows straight into the engine that writes it to be cited.

See Content Engine

Technical AEO Audit

Every technical issue the audit finds becomes a ranked fix on your list.

See Technical AEO Audit

LLM Citations

Source placement gaps arrive here as specific, ranked opportunities to earn.

See LLM Citations

Every signal in. One ranked list out.