SEO SaaS Panel: Building Tools in the AI Era

I joined Patrick Stox and Ben Senescu for the first panel on the Unscripted SEO Podcast. Jeremy Rivera asked how AI has changed the work of building and marketing SEO software.

We had three different experiences to discuss. Patrick was building his own tools after working at Ahrefs and IBM. Ben was running OpenSEO alone. I was preparing OppAlerts for release after years of engineering work.

The conversation covered development, customer feedback, promotion and AI recommendations. Jeremy published his full recap on UnscriptedSEO.com. These are the points I would return to when deciding what to build and how to help people use it.

Faster development still requires review

Patrick described a direct change in what he could build. His previous coding experience was mainly Python and HTML. AI made projects possible that he could not have completed before. At the time of the panel, he said his site had about 104 free tools.

Ben described what that meant for a company with one person. He handled development, support and marketing himself. Coding agents helped him do work he did not think would have been possible six months earlier.

My experience included a substantial correction. I write C and C++ for data processing, but front-end development is a weaker area for me. AI helped with that work. It also made database decisions that contradicted my instructions. I had to reorganize the database as a result.

That experience affects how I review generated code. Finishing a feature is only part of the work. I still need to check whether the implementation follows the decisions I made for the application.

Choose features that people will use

Ben raised a problem that comes with faster development. Contributors can submit features to OpenSEO, and a feature can work most of the time without being ready to include. Accepting every contribution would make the product larger without necessarily making it more useful.

I was making a similar decision for OppAlerts. My databases supported more than 100 ways to find opportunities, including guest posts and link directories. I needed to reduce that selection. I also wanted the customer feedback Ben was already getting.

Patrick added a qualification from his time at Ahrefs. Customer requests matter, but they do not determine the whole product. He argued that following requests alone would have kept Ahrefs a low-cost link tool.

He described projects that were discontinued because their use did not justify their maintenance. One question he asked was:

“will this actually be used enough?”

Development capacity and revenue also affected those decisions. That was useful context for my own work. Being able to build a feature does not answer whether I should maintain it.

Find out who the product helps

OpenSEO’s users differed from the audience Ben first expected. He thought SEO professionals would use the open source project to build their own tools. Instead, he said most customers were entrepreneurs doing SEO for the first time.

That finding helped explain his advice to release early. People can respond to a working product in ways they cannot respond to an idea. They also show whether the explanation of the product makes sense.

Ben connected those two forms of feedback:

“the marketing informs what you build and vice versa.”

Patrick advised people to choose a subject they care about enough to keep working on. Ben agreed. His own interest in open source helped him notice the absence of an SEO tool he wanted to use.

I added that some features need to be tried before people can judge them. A developer must also be prepared to remove a feature when use does not justify keeping it.

Make the product easy to explain and share

Patrick planned to introduce his tools through people who knew his work. He also described visuals and scorecards designed for sharing. His articles would introduce readers to free tools, which could then introduce them to paid products.

Ben explained why open source mattered to his promotion. People could use OpenSEO as the starting point for their own work. Early Reddit responses showed that some people preferred using it to writing similar software themselves.

He was also revising the product description based on how people talked about it. A person should be able to explain to a friend why the tool is useful. That requires clear language about what the product does.

My approach was education. Engineering experience helps me question explanations of AI behavior that depend mainly on observation. I wanted to help marketers connect their tests to an understanding of the technology.

Jeremy described his own use of interviews. A conversation could become an article, a clip or a documented process. The value came from specific experience that the participants could explain.

Help new products appear in AI recommendations

Ben asked what he should do during the next month to get OpenSEO recommended by ChatGPT or Claude. His intended customer was a beginner asking which SEO tool to use.

Patrick separated information in model training from pages retrieved for an answer. A new product may take time to become established in training data. Existing pages about SEO tools offered a more immediate place to seek an accurate description of OpenSEO.

His advice was to examine those lists and seek a mention that explained OpenSEO’s open source availability. That detail could matter to someone who wanted software they could modify or use at a lower cost.

I suggested GitHub’s lists of open source and marketing tools. I also suggested contacting publishers who cover free tools or small business marketing. Ben could ask for inclusion in an existing list or offer an article that explained OpenSEO alongside other relevant products.

Patrick added that coding tools can look for data services while helping someone build an application. He suggested directories of available tools and services as another place to investigate. A free allowance could help people try a service before paying.

Later, I returned to the distinction between training and retrieval. How much current information should be stored in a model? How much should come from search and other tools? I raised those as open questions about where marketers should concentrate their work.

Work with the teams that affect the customer

Jeremy asked whether the role of an SEO needed to change. Patrick said he had already worked across content, analytics, paid marketing, data quality and website design. His experience extended well beyond technical SEO.

He gave accessibility teams as a specific example of useful cooperation inside large companies. Working with other teams could help people agree on changes and get them implemented.

That matched the direction I described for OppAlerts. I wanted the software to help people working in PR, product, reputation monitoring and competitor research. AI search visibility involves information affected by all of those functions.

Ben described a related task in his own company: specifying what each AI-assisted role should do. That included documenting the brand, the facts to use and the expected behavior. He encouraged marketers to consider how they could help the business beyond a single job description.

Study prompts and question short-term tactics

Ben questioned services that promised results through Reddit spam or backlink exchanges. He wanted new business owners to concentrate on what customers search for and how to explain their product.

Patrick warned that a tactic can produce short-term results while creating problems later. He discussed spam enforcement and the possibility of lasting consequences in newer systems. That was a warning about future behavior, not a verified account of how every AI system handles spam.

My recommendation came from training agency analysts. I used to tell them:

“just do keyword research.”

Repeated work across many websites helped analysts understand the relationship between keywords and intent. I suggested spending similar time testing prompts. Change the wording and examine how the response changes.

I discussed that approach in my earlier interview with Jeremy. In the hotel experiment, changing the car named in the prompt changed the recommendations. During this panel, I also described how saved memory could introduce context from another conversation.

Ben encouraged people to try coding agents for SEO research and planning again if their first experience had been disappointing. His recommendation was to spend an afternoon or a day using them on real work. My own priority was similar: make time to test, inspect the results and understand what changed before deciding what to do next.

The panelists

• Patrick StoxIndependent consultant, previously Ahrefs and IBM, r/TechSEO moderator. patrickstox.com
• Ben WillsFounder of OppAlerts, previously ontolo.
• Ben SenescuFounder of OpenSEO, the open source alternative to Semrush and Ahrefs. openseo.so
• Jeremy RiveraHost of the Unscripted SEO Podcast. Jeremy Rivera, SEO Consultant: Links, Authority, and Content That Compounds

All media & appearances