Liam

Just asked Tom, CMO at @incident_io (Series B), what it's actually been like to work with us on SEO and AEO.

The results: in the first four months organic meetings booked grew 22%, their AI share of voice is up 62% and top 3 Google rankings are up 97%.

Tom runs a lean marketing team, so he needed a partner that could own the work and keep adapting as AI search changed.

As he put it himself: "it's not as easy as publishing more content...if you want to just go buy a tool like AirOps and churn out a bunch stuff, great, go for it...Liam and team will help you understand the strategy inside of it, not just the tactical execution."

Here's what that strategy looked like:

(1) Turned "we're probably behind" into a competitive map

We tracked incident against competitors across ChatGPT, Perplexity and Google's AI answers. This exposed the exact buyer questions they were losing and where the most valuable gaps were

Without reliable and significant measurement you do not have an AEO strategy, it’s just SEO with a fancy name.

(2) Covered the whole buying decision

We prioritised MOFU and BOFU content such as competitor comparisons, category alternatives, migration guides and buyer guides to capture as much commercial intent as possible and create the positive brand associations we wanted embedded inside AI answers.

(3) Engineered every page for two retrieval systems

We used CITABLE, our framework for Google and LLM retrieval. Everything managed by our editorial team end-to-end to ensure content was on-brand, relevant and differentiated.

(4) Built off-page consensus for brand authority

Models look beyond a company's claims when deciding what to trust. We found the conversations shaping category consensus on platforms like Reddit and made sure incident was included.

The impact:

- Increased organic meetings by 22%
- Increased Google top 3 keywords by 97%
- Average Google position moved from page 2 to page 1

//

About Discovered Labs: we're a team of AI/ML engineers and marketers that help generate 10,000s of conversions every month from organic search through end-to-end SEO/AEO services, working with companies like Granola, Composio, Instantly, incident and others.

If you'd like to learn more about our work, click here: discoveredlabs.com

If you have any Qs about what's working in AI search right now, feel free to connect & send a DM

ah shit here we go again

So confused by this

In 20 years will all that remains of the SaaS industry be Salesforce and Ramp?

Big milestone for us as an ai-native services company: we've shipped our own company harness called Pascal

Like pretty much everyone in our bubble of the internet, we've tried to lean into AI as much as possible

First, it started with Claude and ChatGPT via the web interface, throwing .md files into projects and using structured prompts

Then it was running Claude Code and Codex locally, creating agents that used several skills and rules to run entire workflows

Then it was...chaos

The early gains were insane and so worth it. But you reach a certain point where the chaos outweighs the benefits

When everyone is using Claude/Codex in single-player mode, some team members are AI wizards while others are not, and everyone has their own flavour of how skills work, it creates a lot of divergence and chaos

Struggles with adoption, standardisation, data validation and observability quickly become bottlenecks

For us to scale as a company we can't rely on heroics where one individual can create high-quality work with AI but others suffer, and we don't want to simplify things so much where we're working with basic markdown files that create no edges

We've graduated most of our core workflows into a proprietary cloud platform, effectively working just as typical software does, but every team member still has access to our agents and company MCP so they can run workflows locally

Now we have observability so we know when an agent does something wrong, we're constantly building custom evals to standardise quality, we've got data validation in place, we've built a context layer across the entire company, and we can use whatever models we want incl. OSS models to avoid dependence on frontier models

It's been insanely difficult to get to this point, it requires a complete rethink of how companies & people have worked since mass-adoption of the internet around 30 years ago. Everything - people, systems, org structure, standards, culture - needs to be thought about differently if you want to scale this across an org of dozens of people

(and just to be clear: these workflows are responsible for generating tens of millions ($$) in revenue, so there's a lot of complexity involved)

Very proud of the team on this one. All done while being bootstrapped, and unlike many other things in business, working from a blank playbook with little-to-no historical precedent

in celebration of one of our client case studies being ripped on X, here is a completely different client who's currently getting almost 300k signups per month from organic

you can copy the case study but not the alpha

god bless