Kayako is an AI-native customer support platform. It helps mid-market SaaS companies scale support capacity without scaling headcount — processing ~50K tickets per month across 50+ customers at 50–75% autonomous resolution rates.
The product sits at the intersection of two fast-moving trends: the shift from workflow-based automation to agent-native AI, and the collapse of per-seat pricing in favour of outcome-based models. Kayako's pricing reflects that shift — $1 per ticket resolved, zero cost if nothing is resolved.
When I came in, the brand was positioned as a conventional customer support tool in a crowded market. The product had evolved. The story had not.
Two things, in order. First: build organic traffic from zero — topical maps, content strategy, SEO execution, and ranking for the transactional keywords that matter in the customer support space.
Second: reposition the entire brand — from a conventional help desk tool to an AI employee for customer support. That meant working with leadership to define the new narrative, then translating it across 70+ pages: product pages, solution pages, homepage, and every touchpoint in between.
Both had to happen simultaneously. The SEO build needed new content. The repositioning needed a complete content architecture. They fed each other.
I built the entire topical authority framework from scratch. The map centred on three clusters: AI customer support (the new positioning), help desk and ticketing (high-intent transactional), and customer service operations (top-of-funnel educational). Every piece of content had a dual purpose: rank for a keyword, and support the broader repositioning narrative.
Ranking for "live chat" — a KD 73 keyword competing against LiveChat, Intercom, and Freshdesk — required building real topical depth first. I sequenced the content rollout around that: lighter-competition cluster pages first to build domain authority, then the hard transactional terms once we had enough signal.
Kayako's buyer is a VP of Support or COO at a mid-market SaaS company. At $15K onboarding + outcome-based resolution fees, the average deal has meaningful lifetime value. Organic traffic at a 1–2% demo conversion rate and 20–25% close rate translates to an estimated ~$400K in attributed inbound pipeline from the organic build — a deliberately conservative figure given the growth trajectory.
More importantly, organic is the channel that compounds. As the content library matures and authority builds, cost-per-lead from organic approaches zero — while paid channels scale linearly with spend.
Kayako was named in a sea of conventional customer support tools. The messaging was feature-led: ticketing, live chat, shared inbox. Nothing wrong with the product. Everything wrong with how it was communicated.
In a crowded market, feature parity is table stakes. The buyers Kayako was going after — VPs of Support at mid-market SaaS — had already been burned by AI tools that promised automation and delivered "suggested replies." The messaging needed to cut through that scepticism, not add to it.
Working with leadership, I helped define the new narrative anchor: Kay is not a chatbot. Not a copilot. Not a feature inside a helpdesk. Kay is an AI employee.
That one frame changes everything downstream. An AI employee has a job to do. It earns trust progressively. It gets smarter over time. It costs nothing if it resolves nothing. Every headline, every product page subhead, every feature description had to flow from that frame — not describe a tool, but describe a hire.
without Scaling Headcount."
Every VP of Support reading this has the same problem: ticket volume grows, headcount doesn't. This headline names the tension in the first three words and promises the resolution in the last three. No feature mentioned. No AI jargon. Just the buyer's actual situation.
Seventy-plus pages, structured around three content tiers. Product pages — each rebuilt around a specific buyer outcome, not a feature list. The maturity curve (Triage → Assist → Autonomous Resolution) became the architecture of the product narrative. Solution pages — mapped to buyer personas: VP of Support, Team Lead, COO. Each page speaks to one person's specific pain, not a generalist audience. Homepage — rebuilt around the before/after contrast that makes the AI value proposition tangible: open backlog 1,284 → 52% AI-resolved; CSAT 76% → 90%.
For every page, I defined not just the copy but the visual brief — what imagery would reinforce the positioning, what UI screenshots to feature, how the before/after contrast should be visualised. The network graph background, the comparison module, the G2 rating placement — all of those were art-directed as part of the copy brief, not added after.
The PRD behind Kay — which defines the agent-native architecture, the $1/resolution pricing model, and the trust-ladder maturity curve — was built in parallel with the website refresh. The brand narrative and the product narrative were written as one document, not two.
The central repositioning frame. Kay earns trust progressively, gets smarter over time, and costs nothing unless it resolves tickets. Not a bolt-on. A hire.
Plugs into Zendesk, Freshdesk, Intercom via one API seat. Keep your platform. The risk reversal narrative made the sales conversation materially easier.
$1 per ticket resolved. Zero cost if zero resolved. The pricing model is itself a positioning statement — and it became a key headline across the site.