Targeted Impressions / Search + AI
Find out where buyers discover you, how AI answers describe your brand, and which improvements deserve your next investment. I turn search, content and measurement findings into quick wins, experiments and a longer-term plan—with implementation support where agreed.
Three questions. A practical next move.
Map where buyers can find you. Review Google search performance, identifiable AI referrals and sampled AI answers. Check whether important pages can be crawled, indexed and reached through internal links.
What you getA visibility baseline with gaps by channel and page family.
Inspect how answers describe and compare you. Test relevant buyer questions. Compare sampled descriptions, recommendations and citations with your approved brand and product facts, and note where competitors appear.
What you getA review of sampled answers showing inaccuracies, omissions and supporting pages to investigate.
Turn the findings into decisions. Connect demand, competitor gaps and technical constraints to your commercial objective. Rank quick wins, experiments and larger work against likely value, evidence and effort.
What you getA prioritized plan: act now, test next, plan for later—or leave alone, with a reason.
Experience
14+ yearsin enterprise SEO, growth, technical search and measurement
In-house leadership
Skyscanner AmericasGrowth Manager, SEO Americas · May 2016–September 2019
Martech startup for AI search
B2B + B2CSenior Customer Success Manager at Quattr · December 2020–August 2026
The practical outcome
Recommendations your team can act on. We agree the depth and deliverables before kickoff.
Where you stand today across the agreed pages, markets and data sources. What is measured, what is estimated and what remains unknown.
Quick wins and larger workstreams, with the issue, affected pages, recommendation, evidence, effort, dependencies and a way to check completion.
Buyer questions and demand your current pages do not serve well. What to improve, consolidate or create—and where competitors offer a stronger answer.
A hypothesis, target pages, proposed change, success measure and review date. A suitable comparison where the data and implementation allow it.
The metrics, source definitions, reporting cadence and decisions to make from the results. Reported AI referrals, sampled answers and crawler activity stay separate.
A sequence of improvements tied to the business objective, team capacity and dependencies.
Illustrative example: important service pages are hard to reach through internal links. The recommendation identifies the affected URLs and templates, proposes specific links, assigns an owner and defines a post-release crawl check. Ongoing reporting tracks search and conversion performance.
Discoverability, data integration and delivery belong in the same plan. I connect the analysis to the technical work: how information enters the site, how pages and tools use it, and how the team checks what changed. Explore the thinking and examples →
From discovery to a usable plan
My approach draws on enterprise search, in-house growth and working with client teams. As AI changes how people research and compare businesses, I connect search demand, technical access, content and the available marketing data to one question: what should your team do next?
Start with the business objective, not a prompt list. Technical and content discovery can run together. Material access or delivery blockers take priority; new tools and metrics earn their place only when they help answer the agreed question.
Define the audience, commercial question, scope and implementation owner. Establish the available baseline.
Review crawling, indexing, rendering, canonicals, internal linking and speed. Identify affected templates and prioritize material blockers.
Map useful answers, keyword demand and competitor gaps to existing or proposed pages. Use customer questions where available.
Build a representative question set from that research. Record sampled answers and citations, with the model, date and test conditions.
Review recommendations together. Agree quick wins, experiments, reporting and the larger plan; implement or hand off within the selected scope.
Keyword demand, customer language and competitor research inform the buyer questions we test in AI assistants. Together, they help focus the work on your next opportunity.
See the Skyscanner seasonal-search case → Explore my historical search and implementation experience.
01 / Fixed scope
Three depths. Each includes the one before it.
1–2 weeks
Teams that need a focused read on one business question before committing budget
Agree the objective and priority pages. Review technical access, content and available search and analytics data, then sample relevant buyer questions in search and AI assistants.
3–4 weeks
Teams planning a 60 to 90 day push, for example ahead of a seasonal peak, a launch or a budget cycle
Assess technical and content gaps across the agreed page families. Compare named competitors, research demand, and test a larger buyer-question set across agreed AI assistants over repeated runs. Identify patterns in cited pages and opportunities to test.
Everything in Rapid Baseline, plus
6–8 weeks
Larger or multi-product teams that need one strategy across business units
The Growth Audit, run per audience and product line, plus three checks: do your brand facts agree across site, docs and third-party sources; does site structure help or hurt; can your analytics attribute AI traffic and support testing.
Everything in Growth Audit, plus
We name the domains, page families, markets, competitors, question set and data sources in the scope. Recommendations include priority, rationale and the next action. Detailed implementation specs are included at the agreed audit depth.
02 / Custom scope
Project or retainer. Pick your modules.
Deliver the agreed priority changes: crawl and rendering fixes, internal links, performance improvements and stronger source content. Address inaccurate brand information at its source; third-party AI answers remain outside our control.
Your prompt baseline, re-run across LLMs on a set cadence. I compare model shifts with your own changes and report what to do next.
A ranked backlog fed by monitoring. I implement or coordinate each change, confirm it’s live, measure it, and test before you scale where the data allows.
Is AI visibility producing value? I connect identifiable AI and search referrals to GA4 conversions and your CRM where the data supports it, and report each stage separately.
Targets are set at kickoff, against the baseline from your audit. Every readout reports against them.
Reporting connects the relevant measures: sampled AI answers, crawler activity, referrals and business outcomes.
03
We agree scope, timing, fees, access and implementation ownership before kickoff.
04
Only the sources needed for the agreed question
Source → role in the analysis
Every engagement ends with a recommended stack. Your team runs it, I maintain it, or we hand it off.
To start, I only need read-only access or exports from the relevant sources you have. Missing data stays an explicit limitation. Do not email credentials or confidential customer records; we agree secure access during onboarding.
Optional / Separate project
When the platform is the bottleneck, assess a move to structured content that authorized tools can work with—while people retain review and release control.
An agent-ready publishing workflow needs more than generated pages: clear content models, role-based permissions, previews, version history, validation and rollback.
After discovery, we scope a rapid migration for a suitable site or defined page family. Where a smaller repair meets the objective, we recommend that instead.
Before we start
A written readout and prioritized recommendations for the agreed scope, reviewed together on a handoff call. Depending on the audit depth, this includes technical tickets, content briefs, competitor gaps, experiment plans and reporting specifications. We list the exact deliverables before work starts.
Implementation is available as a separately agreed project or ongoing engagement. I can do the scoped work, coordinate with your agency or engineers, or provide an implementation-ready handoff. An audit does not automatically include every repair, new page or integration it identifies.
Yes. The starting point is your existing website, data and delivery process. We agree owners and access, and choose tools only where they answer the question. You do not need to buy an entire new stack.
The audit ranges above are planning estimates, confirmed once scope and access are agreed. Fees depend on the site, markets, products, data and delivery responsibility. You receive a written scope, fee and timetable before kickoff. Delayed access or added scope can change that timetable.
We use relevant read-only access or exports that are available and identify what cannot be measured. If a missing source would prevent a useful answer, we resolve that before proceeding with the affected work. Missing analytics are not treated as zero activity.
I commit to the agreed analysis, actionable deliverables and implementation checks. Rankings, AI answers and revenue depend on factors beyond the engagement, so we agree objectives and track progress against the baseline.
Not necessarily. Migration is worth considering when the current platform materially limits discoverability, publishing or the agreed workflow. A focused repair may be the better option. CMS modernization is separately scoped, not a prerequisite for an audit.
We add it to the backlog, explain its relevance and agree whether it replaces a lower-priority item or needs additional scope. Ad hoc questions are welcome; they do not become unbounded work or an unexpected commitment.
A practical first step
Send your website, the business question, priority market and any deadline. We will discuss fit, relevant data and the right scope, then agree deliverables, fees and ownership before work begins.
Book a discovery callOpens an email to [email protected]. Other contact options · Career and work