We build brands the algorithm can't ignore.

A boutique hotel with extraordinary guest reviews and an occupancy problem.

The property is one of the most beautiful luxury hotels on Martha's Vineyard — but the business was almost entirely seasonal. Memorial Day to Labor Day was sold out at premium rates. The other nine months: 30–40% occupancy, heavy reliance on OTAs, margins compressed by commission fees.

Ownership wanted year-round revenue without diluting the brand. Most "shoulder season" plays — discounts, promo packages, OTA pushes — would have done exactly that.

Repositioned the hotel as a year-round destination, not a summer one.

We started with brand work. Reframed the property's story around what the island actually is in the off-season: quiet, cinematic, restorative — a different luxury, not a lesser one. Commissioned new photography that captured the island in October fog and February light, not just July sunshine.

Then rebuilt the booking site on a custom stack with a direct-booking incentive program designed to pull guests off OTAs. Layered in paid search and Meta with creative that targeted high-net-worth households in NYC, Boston, and DC for shoulder-season weekends. Built a Klaviyo lifecycle program for past guests with hyper-segmented offers — wine country weekends in October, writers' retreats in February, anniversary stays in March.

Year two, we leaned in: partnerships with high-end editorial, a referral program for past guests, and a pricing strategy that protected the brand while filling rooms.

Fully booked — every season.

By month 14, shoulder-season occupancy crossed 80%. Today the hotel is consistently booked 9 months out, including off-peak weekends. OTA dependence dropped from 60% of bookings to 22%.

Invisible to AI search in a category their buyers were already querying.

Their target buyers — finance and ops leaders at mid-market companies — increasingly start their research on ChatGPT and Perplexity. When we ran the baseline audit, the brand had a 0% citation rate on category-defining queries. Three of their direct competitors were appearing as recommended vendors. Theirs wasn't even mentioned.

Worse, the brand had decent classical SEO — they ranked on Google. But Google rankings and AI citations are different games. Different sources, different signals, different optimization strategies.

Rebuilt the authority graph that AI engines actually retrieve from.

This is the work we now run on every GEO engagement, powered by our platform InfluenceLayer:

Mapped the citation landscape. Used InfluenceLayer to identify the 40+ specific sources AI engines were pulling from when answering buyer questions in this category — Reddit threads, G2 comparisons, Wikipedia, YouTube reviews, niche industry blogs, Substack newsletters.

Built presence in those sources. Coordinated content production, contributor partnerships, and community engagement across the highest-signal sources. Made sure the brand's positioning, differentiators, and category framing were available in the places models cite.

Restructured owned content for retrieval. Existing site content was rewritten with explicit Q&A formatting, structured data, and clear comparative framing — designed for LLM excerpting, not just human reading.

Tracked everything weekly. InfluenceLayer monitors citation share, sentiment, and competitive position across every major AI engine. We meet weekly to read the data and adjust.

From invisible to category answer in 8 months.

A category leader with a website that didn't act like one.

The company had decades of operational excellence and a strong reputation among existing buyers — but for new buyer acquisition, they relied almost entirely on outbound sales and trade shows. Cost per qualified lead was steep, the sales team was the only growth lever, and the website was effectively a brochure.

Buyers in their category had already shifted to digital research. Procurement teams, plant managers, and engineers were Googling specifications, comparing alternatives, and shortlisting vendors before ever talking to sales — but the company wasn't showing up for those queries.

Built a content engine that matched how buyers actually research.

We treated the engagement as a 24-month investment, not a quarterly campaign. The work had three layers:

Technical foundation. Migrated off the legacy CMS to a modern stack. Fixed crawl issues, schema, internal linking, page speed. The site went from a 38 PageSpeed score to 94.

Programmatic content at scale. Built thousands of pages around the long-tail of how buyers actually search — by spec, by use case, by application, by industry vertical, by alternative comparisons. Real engineering content written by real subject matter experts, not LLM slop.

Conversion plumbing. Wired the site for full attribution. Tied organic sessions to qualified pipeline through the CRM. Made the marketing team's contribution to revenue legible — for the first time — to the public-co board.

Organic became the largest pipeline source by month 18.

The work has been cited in two earnings calls. The organic channel now generates more qualified pipeline than the entire outbound sales team — at a fraction of the cost.

A repeatable playbook for the $5M to $30M jump.

Most DTC brands hit a wall around $5–8M ARR. Paid acquisition starts breaking — CACs creep up, ROAS compresses, the founder-led creative that worked at $2M stops scaling. The brand needs to professionalize without losing the soul that got it here.

We've taken a dozen+ DTC brands across this gap, in categories from skincare to apparel to specialty food. Different products, same playbook.

Three levers, in this order. Every time.

1. Reposition before scaling spend. Most brands at this stage are positioned for the early-adopter customer who already gets it. To break out, the brand needs sharper positioning for a wider audience without alienating the loyal base. We do this work first — usually a 4-6 week sprint — because pouring more spend into fuzzy positioning compounds the problem.

2. Rebuild creative as a system, not a campaign. Founder-led creative doesn't scale because it can't be tested at volume. We build creative production systems — weekly UGC drops, modular ad templates, channel-native content for Meta, TikTok, YouTube. The goal is 30+ ad variants live at any time.

3. Layer lifecycle on top of paid. Klaviyo flows, segmentation, win-back programs, post-purchase upsells. By the time we're done, retention contributes 35–45% of revenue — which is what makes the paid math work at scale.

Compounding outcomes, quarter over quarter.

The headline result above came from a skincare brand that went from $7M to $30M+ in 11 months. We've shipped similar arcs across apparel, specialty food, home goods, and wellness — different categories, same compounding playbook.