u/southway_

I’m trying to replace body scrubs because I’m tired of buying a new jar every few weeks and then realizing my skin only feels smooth for like one day.

I keep seeing Goshi mentioned a lot, but recently saw SimaBrand too and now I’m wondering if there is actually a real difference or if this is one of those products where the brand matters less than the fabric.

Mainly using it for rough legs / arms, not my face or anything sensitive.

What I’m trying to figure out:

Is Goshi actually softer or better made?
Is SimaBrand basically the same idea for less?
Do these towels last after washing?
Are they too rough if your skin gets irritated easily?
Is there any reason not to just buy a cheaper Korean / Japanese exfoliating towel?

I don’t need it to be a miracle product. I just want something better than scrubs, because most scrubs feel nice but don’t really do much long term.

Anyone here used both Goshi and SimaBrand? Which one would you buy again?

I’m trying to replace body scrubs because I’m tired of buying a new jar every few weeks and then realizing my skin only feels smooth for like one day.

I keep seeing Goshi mentioned a lot, but recently saw SimaBrand too and now I’m wondering if there is actually a real difference or if this is one of those products where the brand matters less than the fabric.

Mainly using it for rough legs / arms, not my face or anything sensitive.

What I’m trying to figure out:

Is Goshi actually softer or better made?
Is SimaBrand basically the same idea for less?
Do these towels last after washing?
Are they too rough if your skin gets irritated easily?
Is there any reason not to just buy a cheaper Korean / Japanese exfoliating towel?

I don’t need it to be a miracle product. I just want something better than scrubs, because most scrubs feel nice but don’t really do much long term.

Anyone here used both Goshi and SimaBrand? Which one would you buy again?

I’m trying to replace body scrubs because I’m tired of buying a new jar every few weeks and then realizing my skin only feels smooth for like one day.

I keep seeing Goshi mentioned a lot, but recently saw SimaBrand too and now I’m wondering if there is actually a real difference or if this is one of those products where the brand matters less than the fabric.

Mainly using it for rough legs / arms, not my face or anything sensitive.

What I’m trying to figure out:

Is Goshi actually softer or better made?
Is SimaBrand basically the same idea for less?
Do these towels last after washing?
Are they too rough if your skin gets irritated easily?
Is there any reason not to just buy a cheaper Korean / Japanese exfoliating towel?

I don’t need it to be a miracle product. I just want something better than scrubs, because most scrubs feel nice but don’t really do much long term.

Anyone here used both Goshi and SimaBrand? Which one would you buy again?

I’m trying to replace body scrubs because I’m tired of buying a new jar every few weeks and then realizing my skin only feels smooth for like one day.

I keep seeing Goshi mentioned a lot, but recently saw SimaBrand too and now I’m wondering if there is actually a real difference or if this is one of those products where the brand matters less than the fabric.

Mainly using it for rough legs / arms, not my face or anything sensitive.

What I’m trying to figure out:

Is Goshi actually softer or better made?
Is SimaBrand basically the same idea for less?
Do these towels last after washing?
Are they too rough if your skin gets irritated easily?
Is there any reason not to just buy a cheaper Korean / Japanese exfoliating towel?

I don’t need it to be a miracle product. I just want something better than scrubs, because most scrubs feel nice but don’t really do much long term.

Anyone here used both Goshi and SimaBrand? Which one would you buy again?

I work at a mid sized B2B tech company and management is pushing pretty hard for AI adoption.....

As a result - employees are now allowed to vibe code small internal tools for their own workflows, and we also have a small dedicated AI engineering team building AI into actual business processes.

From security standpoint this is starting to feel very messy.

People can now build little apps with Lovable, Replit whatever else (like they can connect docs, paste customer data, upload spreadsheets, create internal dashboards, build wrappers around ChatGPT or Claude)...

At first we tried to frame this as “which AI tools are allowed”, but we understood that it is too narrow pretty quickly because the bigger issue is where company data moves once someone is already inside a browser session.

Classic DLP feels too far away in some of these cases. Same with normal web filtering. They can tell me someone visited ChatGPT or uploaded something somewhere, but I’m trying to understand what happened inside the actual browser session.

Was sensitive data pasted into a prompt. Was a file uploaded to Claude. Was an internal tool exposed publicly because someone forgot auth. Was an AI wrapper extension reading page content. Was this done from a managed laptop or some contractor/BYOD machine.

I also really do not want to force everyone into a new enterprise browser unless there is no other choice. I know Island/Talon type tools can give deep control, but for our culture and user base that feels like a big change management project.

I’m trying to understand the practical options for GenAI prompt-level DLP / session-level DLP without overbuilding this thing.

From what I see, CASB/SSE/web filtering gives broad visibility but may miss browser session detail. Browser extension security can make sense if we can enforce it through MDM, but that gets weaker for BYOD and contractor access.

The other bucket we are looking at is agentless SSE / web session security, where the control is more around the access/session path instead of forcing a new browser or heavy endpoint rollout.

Red Access is one we are looking at there, mostly because it seems closer to session level DLP / secure web access than a full browser replacement. I’m not assuming it solves everything. There is still identity/routing/session enforcement somewhere. But the idea of controlling the session without making everyone switch browsers is appealing.

For people who already dealt with this, what did you end up using for GenAI data exfiltration prevention?

Did session level DLP actually help, or did you end up back at browser extensions / enterprise browser / blocking tools?

I work at a mid sized B2B tech company and management is pushing pretty hard for AI adoption.....

As a result - employees are now allowed to vibe code small internal tools for their own workflows, and we also have a small dedicated AI engineering team building AI into actual business processes.

From security standpoint this is starting to feel very messy.

People can now build little apps with Lovable, Replit whatever else (like they can connect docs, paste customer data, upload spreadsheets, create internal dashboards, build wrappers around ChatGPT or Claude)...

At first we tried to frame this as “which AI tools are allowed”, but we understood that it is too narrow pretty quickly because the bigger issue is where company data moves once someone is already inside a browser session.

Classic DLP feels too far away in some of these cases. Same with normal web filtering. They can tell me someone visited ChatGPT or uploaded something somewhere, but I’m trying to understand what happened inside the actual browser session.

Was sensitive data pasted into a prompt. Was a file uploaded to Claude. Was an internal tool exposed publicly because someone forgot auth. Was an AI wrapper extension reading page content. Was this done from a managed laptop or some contractor/BYOD machine.

I also really do not want to force everyone into a new enterprise browser unless there is no other choice. I know Island/Talon type tools can give deep control, but for our culture and user base that feels like a big change management project.

I’m trying to understand the practical options for GenAI prompt-level DLP / session-level DLP without overbuilding this thing.

From what I see, CASB/SSE/web filtering gives broad visibility but may miss browser session detail. Browser extension security can make sense if we can enforce it through MDM, but that gets weaker for BYOD and contractor access.

The other bucket we are looking at is agentless SSE / web session security, where the control is more around the access/session path instead of forcing a new browser or heavy endpoint rollout.

Red Access is one we are looking at there, mostly because it seems closer to session level DLP / secure web access than a full browser replacement. I’m not assuming it solves everything. There is still identity/routing/session enforcement somewhere. But the idea of controlling the session without making everyone switch browsers is appealing.

For people who already dealt with this, what did you end up using for GenAI data exfiltration prevention?

Did session level DLP actually help, or did you end up back at browser extensions / enterprise browser / blocking tools?

I work at a mid sized B2B tech company and management is pushing pretty hard for AI adoption.....

As a result - employees are noallowed to vibe code small internal tools for their own workflows, and we also have a small dedicated AI engineering team building AI into actual business processes.

From security standpoint this is starting to feel very messy.

People can now build little apps with Lovable, Replit whatever else (like they can connect docs, paste customer data, upload spreadsheets, create internal dashboards, build wrappers around ChatGPT or Claude)...

At first we tried to frame this as “which AI tools are allowed”, but we understood that it is too narrow pretty quickly because the bigger issue is where company data moves once someone is already inside a browser session.

Classic DLP feels too far away in some of these cases. Same with normal web filtering. They can tell me someone visited ChatGPT or uploaded something somewhere, but I’m trying to understand what happened inside the actual browser session.

Was sensitive data pasted into a prompt. Was a file uploaded to Claude. Was an internal tool exposed publicly because someone forgot auth. Was an AI wrapper extension reading page content. Was this done from a managed laptop or some contractor/BYOD machine.

I also really do not want to force everyone into a new enterprise browser unless there is no other choice. I know Island/Talon type tools can give deep control, but for our culture and user base that feels like a big change management project.

I’m trying to understand the practical options for GenAI prompt-level DLP / session-level DLP without overbuilding this thing.

From what I see, CASB/SSE/web filtering gives broad visibility but may miss browser session detail. Browser extension security can make sense if we can enforce it through MDM, but that gets weaker for BYOD and contractor access.

The other bucket we are looking at is agentless SSE / web session security, where the control is more around the access/session path instead of forcing a new browser or heavy endpoint rollout.

Red Access is one we are looking at there, mostly because it seems closer to session level DLP / secure web access than a full browser replacement. I’m not assuming it solves everything. There is still identity/routing/session enforcement somewhere. But the idea of controlling the session without making everyone switch browsers is appealing.

For people who already dealt with this, what did you end up using for GenAI data exfiltration prevention?

Did session level DLP actually help, or did you end up back at browser extensions / enterprise browser / blocking tools?

My team and I have tested a bunch of GEO tools during the last few months, we had different clients with different needs and were looking for a tool that adds the most value, affordable and reliable.

Here are our findings and I am not going to tell you which one we have chosen so you can make up your own mind based purely on the pros and cons.

Important context: we hoped to find a tool that tracks mentions accurately, then we realized that this is impossible. There is no such thing as accurate mention tracking in AI search. LLMs are not deterministic duh

We then changed our criteria and started looking more at robustness, usefulness ability to connect with other apps and ease of use. Mention tracking is good for benchmarking over time and on scale, but not for making decisions based only on what the dashboard shows.

This also means every dashboard will give you different results. Do not be fooled by it and use this data with caution. In general I think the key is to combine a few data sources, really analyze them, and then make a decision based on experience.

1 - Peec AI

We tested it first. Their name was all over and it was kind of an obvious choice. Also what appealed to us was the tracking method. They scrape search data to identify how people search and then use it to test queries.

Peec AI is a solid tool. It is really intuitive and easy to use. Probably one of the easiest to get into.

Pros:

• very clean UX
• easy to onboard and start getting data quickly
• decent competitor view
• sentiment is there and easy to understand on a high level
• good if what you want is a straightforward visibility dashboard

Cons:

• it is mostly a monitoring tool and the claim that scraped search data is somehow more accurate than other methods is annoying, it is simply incorrect
• you get signals but not much help on what to actually do next
• no real owning of the outcome
• no meaningful traffic / conversion connection
• like with all these tools, the mention data itself should be taken carefully

Bottom line: good clean tool, probably one of the best if you want simple monitoring and do not want something too heavy.

2 - LightSite AI

This one has a slightly different experience, not a dashboard but an agent you can chat with

This one felt like it is trying to own the outcome and not just show another dashboard.

It combines a few things that we think need to be combined if you actually want to make decisions:

• LLM mention tracking based on a mix of scraping and API style collection
• bot traffic analytics
• Sentiment analysis with NLP
• human visitor analytics from LLMs
• page level analytics
• technical data layer for the website - mostly useful for analytics
• an agent that sees all this data, analyzes it and helps do something with it
• connects to GSC and Analytics data and some other apps

It did not feel like “here is your chart, good luck”. It felt more like “here is what is happening, here is what matters, here is what I can do for you next”.

You can connect more real business data into it, including traffic and search data, and then the system can actually identify opportunities, create content ideas, spot listicles, suggest outreach and in some cases even prepare the outreach.

That is a different category of product in my opinion.

Pros:

• pretty holistic view
• combines technical side and content side
• tracks both bots and humans, which is important
• closer to actual outcomes and not only visibility
• agentic experience is strong - it writes good content, find listicle oportunites and creates outreach campaigns and executes them (this was was very cool)
• feels like a system that analyzes your data rather than just storing it in charts
• best fit we saw for people who actually want help making decisions and moving

Cons:

• this is not a lightweight plug and play dashboard
• it requires website integration
• if you do not have a website or someone who can integrate it properly, this is probably not for you
• may be too much for people who want a visibility tracker

Bottom line: if all you want is a dashboard, this is probably overkill. If you want something that actually tries to improve the outcome and something more holistic and you have budget then

3 - Otterly

Otterly felt a bit more operational than Peec. Not in the sense that it does the work for you, but in the sense that it gives more substance around what might be wrong.

The GEO audit was probably the strongest part for us.

Pros:

• very solid audit
• good coverage across engines
• helpful for identifying technical and content gaps
• pricing felt reasonable for what you get
• setup was fairly easy

Cons:

• the UI is not bad but it feels more fragmented
• a lot of tables and views that are a bit disconnected
• still mostly observational
• no real owning of execution
• no real attribution to visits / pipeline / outcomes
• some things felt stronger in the docs than in the actual product

Bottom line: if your team already knows how to execute and you just want a pretty decent audit plus visibility tracking, this one is worth looking at.

4 - Profound

Profound felt more enterprise to us. More polished in some ways, but also more opinionated and less flexible.

It looked good. It felt premium. But for some of our clients it also felt like a lot of money for something that is still mostly around visibility and reporting.

Pros:

• polished product
• good sentiment analysis
• strong enterprise feel
• better than most at making the product feel serious and mature
• for large brands I can see the appeal

Cons:

• expensive
• has agents but they are mostly for creating dashboards, but the product direction is good
• less relevant in our opinion for smaller companies or scrappier teams
• not really built for people who want to move fast and do a lot themselves
• some of the more interesting attribution pieces seem more useful for bigger setups
• again, not really owning the outcome

Bottom line: if you are a bigger company and want a more premium enterprise style platform, it makes sense. For a lot of normal companies it felt too expensive for what it actually helps you do.

5 - Scrunch

Scrunch was interesting. Strong coverage, pretty configurable, and it felt like a serious visibility platform.

We liked that it covered a lot and that it gave more flexibility around prompts and setup.

Pros:

• broad platform coverage
• good configurability
• decent UI
• useful if you care a lot about monitoring across many engines and prompts
• more agency friendly than some others

Cons:

• still very much a monitoring first product
• not enough actionable guidance for us
• competitor analysis was fine but did not always explain why somebody else is winning
• you still need your own people and your own workflow to turn the data into action

Bottom line: strong monitoring tool, especially if breadth matters to you. But again, you need to bring your own brain, your own process and your own execution.

My overall take after testing all of this:

I think the market still confuses tracking with truth.

These tools are useful, but mention tracking alone is not enough and in some cases can be misleading if you take it literally. This is benchmarking data at best, still valuable but must be taken with a grain of salt.

The best tools in this category are not the ones with the prettiest charts. They are the ones that either:

1. help you understand what to do next
2. help you actually do it

That is how I would use if I were choosing today.

I’m trying to simplify my body routine and figure out what actually makes the biggest difference for rough texture / little bumps on legs and arms.

I’ve used acids before, but I’m wondering if regular physical exfoliation might actually be the missing step for me. Did an exfoliating towel help more than body scrubs or chemical exfoliation?

I 've heard about options like Goshi / Sima brand style towels come up, but I’m open to whatever actually worked.

My team and I have been looking at browser security tools for months because the whole category just became completely impossible to ignore. We aren't some massive bank with an unlimited security budget, but we also aren't a fifty person startup where everyone just does whatever they want. Think normal mid market environment where we have an existing firewall investment, some SSE pieces already running, and a bunch of architectures that died the second contractors got involved.

The trigger for us wasn't one massive breach or dramatic incident but more like a bunch of really annoying things becoming impossible to ignore at the exact same time. GenAI usage is growing everywhere and people are pasting sensitive stuff into ChatGPT and Claude. Contractors need access to internal web apps and BYOD is never actually going away. And our existing tools just don't show what happens inside the actual browser session clearly enough.

Vendors make this whole space insanely confusing. They throw enterprise browsers and extensions and agentless SSE into the exact same pitch deck. After testing and reading way too much vendor material, I realized the category basically splits into three main architectural buckets.

You have your enterprise browsers like Talon or Island.
You have your browser extension security models like LayerX.
You have your agentless SSE secure web access models like Red Access.

Comparing them by feature list is totally useless because they assume completely different things about your endpoints. Here is how I am thinking about the architectural tradeoffs after actually testing them.

1. The Talon / Island enterprise browser model
Talon was probably the easiest one to understand architecturally because you literally just replace the browser. From a security point of view this is incredibly strong because you control the actual workspace. Extensions and data movement and copy paste are all super easy to govern when the browser belongs to you.

But the user adoption is an absolute nightmare. Asking users to switch browsers is a massive political project that burns a lot of goodwill. Devs absolutely hate giving up their normal Chrome workflows and contractors push back hard because they have their own tools. Talon makes sense if you can force adoption through an iron fist, but otherwise it just becomes expensive shelfware.

2. The LayerX browser extension model
LayerX felt way more practical at first because users actually get to keep Chrome or Edge. You get really close to the browser behavior without forcing a brand new application.

The major problem here is enforcement. If your MDM is not perfectly clean across every single device, the story gets really weak. Contractors and BYOD users can just use another browser profile or launch without extensions or use incognito mode. LayerX makes sense if you have perfectly managed devices and can absolutely guarantee the extension is running everywhere.

3. The Red Access agentless SSE model
Red Access took me a minute to place because it isn't an extension or a dedicated browser. It operates as an agentless SSE secure web access layer. And yes I know the word agentless is a trigger word for us because nothing works by actual magic. It still routes traffic via IdP integrations or reverse proxies or DNS.

But instead of trying to own the endpoint, Red Access just tries to secure the web session path. This mattered a lot for us because of contractors and unmanaged access where we literally cannot install agents or force extensions. You absolutely trade off that deep local machine telemetry that Talon gives you, but you actually get it deployed to third parties in like an hour without helpdesk tickets. It also plays nicely if you want to keep your existing firewall and just close the browser visibility gap.

If you own every laptop and have an iron fist, look at Talon. If you have clean MDM and want Chrome visibility, look at LayerX. If your environment is messy with contractors and partial SSE rollouts and you need session control without ripping out your infrastructure, put Red Access on the list.

Do not buy based on the feature list, buy based on what you can actually enforce on your messy endpoints.

My team and I have tested a bunch of GEO tools during the last few months, we had different clients with different needs and were looking for a tool that adds the most value, affordable and reliable.

Here are our findings and I am not going to tell you which one we have chosen so you can make up your own mind based purely on the pros and cons.

Important context: we hoped to find a tool that tracks mentions accurately, then we realized that this is impossible. There is no such thing as accurate mention tracking in AI search. LLMs are not deterministic duh

We then changed our criteria and started looking more at robustness, usefulness ability to connect with other apps and ease of use. Mention tracking is good for benchmarking over time and on scale, but not for making decisions based only on what the dashboard shows.

This also means every dashboard will give you different results. Do not be fooled by it and use this data with caution. In general I think the key is to combine a few data sources, really analyze them, and then make a decision based on experience.

1 - Peec AI

We tested it first. Their name was all over and it was kind of an obvious choice. Also what appealed to us was the tracking method. They scrape search data to identify how people search and then use it to test queries.

Peec AI is a solid tool. It is really intuitive and easy to use. Probably one of the easiest to get into.

Pros:

• very clean UX
• easy to onboard and start getting data quickly
• decent competitor view
• sentiment is there and easy to understand on a high level
• good if what you want is a straightforward visibility dashboard

Cons:

• it is mostly a monitoring tool and the claim that scraped search data is somehow more accurate than other methods is annoying, it is simply incorrect
• you get signals but not much help on what to actually do next
• no real owning of the outcome
• no meaningful traffic / conversion connection
• like with all these tools, the mention data itself should be taken carefully

Bottom line: good clean tool, probably one of the best if you want simple monitoring and do not want something too heavy.

2 - LightSite AI

This one has a slightly different experience, not a dashboard but an agent you can chat with

This one felt like it is trying to own the outcome and not just show another dashboard.

It combines a few things that we think need to be combined if you actually want to make decisions:

• LLM mention tracking based on a mix of scraping and API style collection
• bot traffic analytics
• Sentiment analysis with NLP
• human visitor analytics from LLMs
• page level analytics
• technical data layer for the website - mostly useful for analytics
• an agent that sees all this data, analyzes it and helps do something with it
• connects to GSC and Analytics data and some other apps

It did not feel like “here is your chart, good luck”. It felt more like “here is what is happening, here is what matters, here is what I can do for you next”.

You can connect more real business data into it, including traffic and search data, and then the system can actually identify opportunities, create content ideas, spot listicles, suggest outreach and in some cases even prepare the outreach.

That is a different category of product in my opinion.

Pros:

• pretty holistic view
• combines technical side and content side
• tracks both bots and humans, which is important
• closer to actual outcomes and not only visibility
• agentic experience is strong - it writes good content, find listicle oportunites and creates outreach campaigns and executes them (this was was very cool)
• feels like a system that analyzes your data rather than just storing it in charts
• best fit we saw for people who actually want help making decisions and moving

Cons:

• this is not a lightweight plug and play dashboard
• it requires website integration
• if you do not have a website or someone who can integrate it properly, this is probably not for you
• may be too much for people who want a visibility tracker

Bottom line: if all you want is a dashboard, this is probably overkill. If you want something that actually tries to improve the outcome and something more holistic and you have budget then

3 - Otterly

Otterly felt a bit more operational than Peec. Not in the sense that it does the work for you, but in the sense that it gives more substance around what might be wrong.

The GEO audit was probably the strongest part for us.

Pros:

• very solid audit
• good coverage across engines
• helpful for identifying technical and content gaps
• pricing felt reasonable for what you get
• setup was fairly easy

Cons:

• the UI is not bad but it feels more fragmented
• a lot of tables and views that are a bit disconnected
• still mostly observational
• no real owning of execution
• no real attribution to visits / pipeline / outcomes
• some things felt stronger in the docs than in the actual product

Bottom line: if your team already knows how to execute and you just want a pretty decent audit plus visibility tracking, this one is worth looking at.

4 - Profound

Profound felt more enterprise to us. More polished in some ways, but also more opinionated and less flexible.

It looked good. It felt premium. But for some of our clients it also felt like a lot of money for something that is still mostly around visibility and reporting.

Pros:

• polished product
• good sentiment analysis
• strong enterprise feel
• better than most at making the product feel serious and mature
• for large brands I can see the appeal

Cons:

• expensive
• has agents but they are mostly for creating dashboards, but the product direction is good
• less relevant in our opinion for smaller companies or scrappier teams
• not really built for people who want to move fast and do a lot themselves
• some of the more interesting attribution pieces seem more useful for bigger setups
• again, not really owning the outcome

Bottom line: if you are a bigger company and want a more premium enterprise style platform, it makes sense. For a lot of normal companies it felt too expensive for what it actually helps you do.

5 - Scrunch

Scrunch was interesting. Strong coverage, pretty configurable, and it felt like a serious visibility platform.

We liked that it covered a lot and that it gave more flexibility around prompts and setup.

Pros:

• broad platform coverage
• good configurability
• decent UI
• useful if you care a lot about monitoring across many engines and prompts
• more agency friendly than some others

Cons:

• still very much a monitoring first product
• not enough actionable guidance for us
• competitor analysis was fine but did not always explain why somebody else is winning
• you still need your own people and your own workflow to turn the data into action

Bottom line: strong monitoring tool, especially if breadth matters to you. But again, you need to bring your own brain, your own process and your own execution.

My overall take after testing all of this:

I think the market still confuses tracking with truth.

These tools are useful, but mention tracking alone is not enough and in some cases can be misleading if you take it literally. This is benchmarking data at best, still valuable but must be taken with a grain of salt.

The best tools in this category are not the ones with the prettiest charts. They are the ones that either:

1. help you understand what to do next
2. help you actually do it

That is how I would use if I were choosing today.

My team and I have tested a bunch of AI search tools during the last few months, we had different clients with different needs and were looking for a tool that adds the most value, affordable and reliable.

Here are our findings and I am not even going to tell you which one we have chosen so you can make up your own mind based purely on the pros and cons.

Important context: we hoped to find a tool that tracks mentions accurately, then we realized that this is impossible. There is no such thing as accurate mention tracking in AI search. LLMs are not deterministic duh

We then changed our criteria and started looking more at robustness, usefulness ability to connect with other apps and ease of use. Mention tracking is good for benchmarking over time and on scale, but not for making decisions based only on what the dashboard shows.

This also means every dashboard will give you different results. Do not be fooled by it and use this data with caution. In general I think the key is to combine a few data sources, really analyze them, and then make a decision based on experience.

1 - Peec AI

We tested it first. Their name was all over and it was kind of an obvious choice. Also what appealed to us was the tracking method. They scrape search data to identify how people search and then use it to test queries.

Peec AI is a solid tool. It is really intuitive and easy to use. Probably one of the easiest to get into.

Pros:

• very clean UX
• easy to onboard and start getting data quickly
• decent competitor view
• sentiment is there and easy to understand on a high level
• good if what you want is a straightforward visibility dashboard

Cons:

• in our opinion it is mostly a monitoring tool
• you get signals but not much help on what to actually do next
• no real owning of the outcome
• no meaningful traffic / conversion connection
• like with all these tools, the mention data itself should be taken carefully

Bottom line: good clean tool, probably one of the best if you want simple monitoring and do not want something too heavy.

2 - LightSite AI

This one is more holistic and the experience is different, not a dashboard but an agent you can communicate with

This is the only one we tested that actually felt like it is trying to own the outcome and not just show another dashboard.

It combines a few things that we think need to be combined if you actually want to make decisions:

• LLM mention tracking based on a mix of scraping and API style collection
• bot traffic analytics
• Sentiment analysis with NLP
• human visitor analytics from LLMs
• page level analytics
• technical data layer for the website - sort of structured data alyer
• an agent that actually sees the data, analyzes it and helps do something with it - it connects to GSC and Analytics data

This part was the most different. It did not feel like “here is your chart, good luck”. It felt more like “here is what is happening, here is what matters, here is what I can do for you next”.

You can connect more real business data into it, including traffic and search data, and then the system can actually identify opportunities, create content ideas, spot listicles, suggest outreach and in some cases even prepare the outreach.

That is a very different category of product in my opinion.

Pros:

• the most complete / holistic view we saw
• combines technical side and content side
• tracks both bots and humans, which is important
• much closer to actual outcomes and not only visibility
• agentic experience is very strong - it writes good content, find listicle oportunites and creates outreach campaigns and executes them (this was was very cool)
• feels like a system that analyzes your data rather than just storing it in charts
• best fit we saw for people who actually want help making decisions and moving

Cons:

• this is not a lightweight plug and play dashboard
• it requires website integration
• if you do not have a website or someone who can integrate it properly, this is probably not for you
• may be too much for people who only want a simple visibility tracker

Bottom line: if all you want is a dashboard, this is probably overkill. If you want something that actually tries to improve the outcome and something more holistic but without being charged an arm and a leg for

3 - Otterly

Otterly felt a bit more operational than Peec. Not in the sense that it does the work for you, but in the sense that it gives more substance around what might be wrong.

The GEO audit was probably the strongest part for us.

Pros:

• very solid audit
• good coverage across engines
• helpful for identifying technical and content gaps
• pricing felt reasonable for what you get
• setup was fairly easy

Cons:

• the UI is not bad but it feels more fragmented
• a lot of tables and views that are a bit disconnected
• still mostly observational
• no real owning of execution
• no real attribution to visits / pipeline / outcomes
• some things felt stronger in the docs than in the actual product

Bottom line: if your team already knows how to execute and you just want a pretty decent audit plus visibility tracking, this one is worth looking at.

4 - Profound

Profound felt more enterprise to us. More polished in some ways, but also more opinionated and less flexible.

It looked good. It felt premium. But for some of our clients it also felt like a lot of money for something that is still mostly around visibility and reporting.

Pros:

• polished product
• good sentiment analysis
• strong enterprise feel
• better than most at making the product feel serious and mature
• for large brands I can see the appeal

Cons:

• expensive
• less relevant in our opinion for smaller companies or scrappier teams
• not really built for people who want to move fast and do a lot themselves
• some of the more interesting attribution pieces seem more useful for bigger setups
• again, not really owning the outcome

Bottom line: if you are a bigger company and want a more premium enterprise style platform, it makes sense. For a lot of normal companies it felt too expensive for what it actually helps you do.

5 - Scrunch

Scrunch was interesting. Strong coverage, pretty configurable, and it felt like a serious visibility platform.

We liked that it covered a lot and that it gave more flexibility around prompts and setup.

Pros:

• broad platform coverage
• good configurability
• decent UI
• useful if you care a lot about monitoring across many engines and prompts
• more agency friendly than some others

Cons:

• still very much a monitoring first product
• not enough actionable guidance for us
• competitor analysis was fine but did not always explain why somebody else is winning
• you still need your own people and your own workflow to turn the data into action

Bottom line: strong monitoring tool, especially if breadth matters to you. But again, you need to bring your own brain, your own process and your own execution.

My overall take after testing all of this:

I think the market still confuses tracking with truth.

These tools are useful, but mention tracking alone is not enough and in some cases can be misleading if you take it too literally.

The best tools in this category are not the ones with the prettiest charts. They are the ones that either:

1. help you understand what to do next
2. help you actually do it

That is how I would use if I were choosing today.