White-label AI search
A senior strategist owns the AI-search plan for your accounts: the questions to track, the sources to win and what to fix first. Specialists deliver it under your agency’s brand.
You keep the client. We never face them.
Published research
A published study on how guests who arrive from AI assistants behave. Research to weigh, not a result of our work.
Published portfolio research
85.2%
AI-referral engagement rate
Managed hotel portfolio · AI search
Q1 2026 · observational channel comparison
AI referrals: less than 0.1% of sessions.
AI referrals represented less than 0.1% of approximately 2.5 million sessions. The small AI channel had higher engagement than organic search; this does not demonstrate causation or guarantee similar results.
LOKAL Proof of Work 2026, p. 9; research published 22 April 2026.
LOKAL research on a managed hotel portfolio, Q1 2026. Observational: not a result of our work and not a forecast.
The work in scope
White-label AI-search work helps an agency measure and improve how a client is named, cited and recommended across a fixed set of answer-engine questions.
LOKAL combines query design, source mapping, entity and page work, implementation QA and repeatable reporting under the agency’s brand.
A stable set of buyer questions, markets and engines used across repeated runs so movement is comparable rather than anecdotal.
The domains and exact pages appearing around the client, classified by authority, relevance and whether an intervention is realistic.
Organisation, service, person, place, page ownership, schema and internal-link inconsistencies corrected where they obstruct a coherent entity.
Question headings, complete first-sentence answers, evidence, comparisons and commercial handoffs written into the page that owns the intent.
A prioritised mix of owned-page changes, proof, mentions and source relationships based on source weight and attainability.
Repeated results separate whether the client is mentioned, receives a source citation or is actually recommended. Runs and cited URLs stay in the record.
From brief to review
Your team approves the questions, buyer, market and engines before anyone reports a baseline.
We record the answers, cited URLs, competitor framing and source patterns across repeat runs.
We prioritise the page, entity, evidence and third-party source changes that can realistically be influenced. Your team approves each one before it ships.
We run the same panel, record named, cited and recommended share, and explain what changed. Your team presents it to the client as your agency’s report.
Before we start
The labels overlap heavily in current buying language. LOKAL treats them as one AI-search service and separates a page only when the search intent and commercial work are genuinely different.
Software can run prompts and monitor visibility. It does not choose the commercially useful panel, resolve source opportunities, rewrite the right page, coordinate implementation or own the QA and account decision.
The fixed panel separates named, cited and recommended outcomes across repeated runs. The report records the engine, answer, cited URL, competitor framing and any material run variance.
Agency retainers start from USD 2,000 a month and are configured around the questions, sources and reporting the account needs. A starting price is not a claim that every panel or every market fits the same scope.
No. Technical search health, indexation, page ownership and link structure still matter. The AI-search service adds answer-engine measurement, source analysis and citation-oriented interventions around that foundation.
Yes. Reports, recommendations and deliverables carry your agency’s brand. We never face your clients: your agency owns the relationship and communication, and LOKAL prepares the work for your team to review and present.
Your next account
Tell us the client goal and where your team needs support. Start with a 30-minute consultation.
See pricing and scope ↗The brief
One account, a pitch or a gap in the team is enough to start.