Behind the expert

Matt Hammel, co-founder and CEO at AirOps, joined Brandformance Office Hours with Paramark’s Pranav Piyush to unpack a simple operating system: teach first, sell second. 

The conversation covered how to win in AI search and AEO, why information gain matters more than rehashing tips, the rise of content engineering, and how to refresh and reuse content so models and humans keep choosing you.

We apply a similar approach at Paramark, where, before we sell any testing, we teach and educate our customers about why attribution is failing, and how incrementality can be the unlock.

The gist

• Lead with value: Teach in every touch, especially when selling to marketers who are saturated with pitches.

• Publish information gain: Share data and perspectives models cannot find elsewhere.

• Operationalize content: Treat it like a system you can engineer, measure, and reuse across channels.

• Stay fresh: Update, deepen, or prune, so LLMs and AI overviews keep citing you.

How to make teach-first actually work

Start by knowing exactly who you are teaching
Map the user who does the work, the buyer who funds it, and the jobs to be done for both. Build education that helps the practitioner ship and helps the buyer justify budget.

Prioritize information gain
Interview internal experts, mine product or marketplace data, and publish benchmarks or unique POVs. Models reward new facts, not recycled summaries. Even a short founder update that explains how your view changed in the last quarter adds useful signal.

Build the content engineering muscle
Pair taste with systems thinking. An effective owner can pull the right tables, structure context for LLMs, and ship workflows that scale quality. Keep this role close to product, data, and PMM, so interviews, datasets, and approvals move fast.

Squeeze more from what you already have
One webinar can become clips, a newsletter, outbound offers, a short course, an FAQ module, social posts, and product-page proof points. Different channels surface different winners, so give each idea multiple at-bats.

Refresh with intent
Tighten structure and schema for readability, add depth with FAQs, reviews, or new data, and realign pages to current products or pricing. When content is thin or off-strategy, prune it and lift the overall corpus.

Adjust your mental model for AI search
Four shifts matter: generic informational posts underperform, freshness carries more weight, off-site textual mentions now influence answers even without a classic backlink, and the work is more automated and in-house than it used to be.

What Matt said

• “I think, very fundamentally, you should add value in every interaction you have with your customer.”

• “LLMs in certain ways are like Pac-Man… They’re fiends for new information that they don’t have yet.”

• “We try to use every piece of content at least 10 times.”

Why this matters for your search strategy

As AI overviews and chat answers take attention from blue links, brands that publish fresh, proprietary evidence get cited more often and discovered more easily. Teaching earns trust with buyers who are numb to outreach, and engineering your content system creates compounding reach. Treat recency as a feature, measure off-site mentions along with links, and build a repeatable path from first insight to ten distributions.

Practical next steps

• Audit outbound, website, and social. Replace asks with tangible value: data, teardown, office hours, or a template.

• Ship one information-gain asset this month: an internal expert interview, a small benchmark from your data, or a founder update.

• Name an owner for content engineering. Give them access to data, a reuse checklist, and a mandate to enforce quality.

• Set a quarterly refresh for core pages: restructure, add depth, realign to current products, and prune what is thin.

• Track more than links. Monitor textual mentions, brand search, and assisted impact from AI answers while you scale.

Check out the full presentation from the recent Office Hours to get a more in-depth look at AI search strategy, building information-gain content, and the content engineering workflows that make it scale.