AI Search Consulting

Be the answer, not the afterthought.

We help your brand show up and get cited in AI answers across ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot. Our approach blends research-backed GEO methodology with hands-on implementation so your site becomes an LLM-readable, entity-rich source of truth.

Email us at report@aisearchlab.ai to start a project or ask a question.

Why Us

Why teams choose AI Search Lab

Science-based methodology: We operationalize research on entity salience, schema, retrieval confidence, and LLM citation behavior.
9-point GEO/AEO rubric: A clear scorecard that benchmarks your site's AI-readiness and guides a prioritized roadmap.
Implementation, not just audits: We ship schema, entities, content briefs, ingestion feeds, and evidence packs.
Measured outcomes: We track AI answer coverage, citation rate, and time-to-first-mention instead of vanity metrics.

We align classic SEO hygiene with modern AI search realities: entities and relationships, answerability, verifiability, and ingestion.

Service Delivery

What you get from a consulting engagement

AI search audit: Crawl plus rubric scoring across structure, content, entities, schema, sources, and ingestion signals.
Implementation playbook: Step-by-step fixes, sample copy blocks, and ready-to-ship JSON-LD schema and sitemap updates.
Entity and evidence map: Canonical entities, claims, citations, and third-party corroboration targets for your proof graph.
AI ingestion readiness: Feeds and patterns that make your content easier to parse, ground, and quote.
Monitoring and governance: KPIs, dashboards, and content guidelines to keep you AI-visible as models evolve.

Our Process

Our research-driven method

Intent discovery: Map high-value questions and actions your buyers ask AI systems.
Rubric scoring: Apply our 9-point GEO/AEO rubric to quantify gaps and opportunity size.
Entity and schema design: Model product and service knowledge with JSON-LD, IDs, and relationships.
Answerability and evidence: Craft concise, verifiable answers with citations and trustworthy sources.
Ingestion and distribution: Prepare sitemaps, feeds, and references so LLMs can find, parse, and ground.
Measure and iterate: Track coverage, citations, and time-to-visibility, then run controlled experiments.

Under the hood: entity salience, retrieval-grounding signals, citation likelihood, schema validation, and crawl-friendliness.

Expected Outcomes

Results you can expect

More AI answer coverage: Appear for the intents that matter, not just long-tail queries.
Higher citation rate: Increase the chances your brand is named, linked, and recommended.
Shorter time-to-first-mention: Reduce lag from publish to crawl to AI visibility.
Implementation velocity: Give your team clear tickets to ship quickly.

FAQ

Consulting questions we hear most often

Who is this consulting offer for?

Teams that already know AI search matters and need execution help: SaaS, healthcare, ecommerce, marketplaces, local services, and other businesses where accuracy, trust, and citations affect pipeline.

Is this strategy separate from SEO, or layered on top?

Layered on top. We keep core SEO hygiene intact, then add the entity, schema, answerability, and evidence work that improves citation likelihood in AI systems.

Do you just hand us a report, or do you implement?

Implementation is the point. We can deliver the roadmap to your team, work alongside your content and engineering leads, or handle execution directly depending on scope.

What usually changes on the site?

Most projects touch page structure, headings, claims, evidence placement, JSON-LD, entity relationships, internal linking, sitemaps, and feed hygiene. The work is usually more structural than cosmetic.

How long does a consulting engagement take?

Audits and initial playbooks can move quickly. Full implementation depends on site complexity, publishing velocity, and how much coordination is needed across engineering, content, and analytics.

Can you work with our internal team or agency?

Yes. In many cases the fastest model is for us to define the rubric, implementation sequence, and QA standards while your existing team ships the work.

Do you guarantee mentions or citations?

No. No one can guarantee that across black-box AI systems. What we can do is improve the quality of the inputs and the measurement framework so your odds materially improve.

Measurement Framework

How we measure success

AI Intent Coverage (AIC): Share of tracked intents where your brand appears in the AI answer or sources panel.
AIC = (# intents with appearance in top answers or sources) / (total tracked intents) x 100%
Citation Rate (CR): Percent of observed AI answers that explicitly cite or mention your brand or domain.
CR = (# answers citing or mentioning brand) / (# answers observed) x 100%
Time-to-First-Mention (TTFM): Median days from content publish or update to first brand mention in any tracked AI answer.
TTFM = median(first_mention_date - publish_date)
Entity Health Score (EHS): Completeness and validity of key entities and relationships such as schema, IDs, and sameAs links.
EHS = (valid entities with complete schema) / (target entities) x 100%
Evidence Strength Index (ESI): Weighted share of claims backed by high-trust, crawlable citations from first-party and third-party sources.
ESI = sum(claim_weight x citation_trust) / sum(claim_weight)
Grounding Accuracy (GA): Percent of sampled AI answers that are factually correct given your ground truth.
GA = (# correct answers) / (# sampled answers) x 100%
Ingestion Latency (IL): Time from sitemap or feed ping to first crawl detection as a proxy for freshness.
IL = median(first_crawl_detected - ping_time)
AI Share of Voice (AI-SoV): Weighted visibility across intents and engines, factoring rank or placement.
AI-SoV = sum(intent_weight x placement_weight) / sum(intent_weight)
Conversion Lift from AI (CL-AI): UTM-tracked lifts from AI-referred or AI-influenced sessions.
CL-AI = (ConvRate_AI - ConvRate_baseline) / ConvRate_baseline x 100%

We report per engine and geography where relevant. Benchmarks establish your baseline before implementation.

Reporting FAQ

KPI and reporting questions

How do you build the KPI panel?

We maintain a tracked set of buyer-relevant intents. Our agent runs scheduled checks across AI engines, captures raw answers and sources, and computes KPIs by engine and intent cluster.

How often do you measure?

Weekly by default and daily for launch sprints. Monthly rollups show trend lines and confidence intervals.

What is a reasonable starting target?

Targets depend on competition and content velocity, but teams often aim for meaningful growth in coverage and citation rate, faster time-to-first-mention, and strong entity-health baselines after implementation.

How do you attribute conversions to AI answers?

We use UTM conventions, landing-page tagging, and analytics segments for AI-referred or AI-influenced sessions, then compare against a matched baseline where possible.

Will KPI trends break every time models change?

Model shifts can move the numbers, so we normalize with intent weights, multi-engine sampling, rolling averages, and re-scoring after meaningful release changes.

What data do you store?

Only what is needed for KPI computation: prompts, captured answer text, citation URLs, timestamps, and your site's public metadata. No customer PII unless you explicitly provide it.

Can we bring our own intents and benchmarks?

Yes. We can merge your prompt list, historical measurements, and funnel priorities into the tracker.

Our Clients

Who we help

Growth and product marketing teams at SaaS, healthcare, ecommerce, marketplaces, and local services, especially where accuracy, trust, and citations drive conversion.

Get Started

Talk to us

Have a tricky visibility problem or need a second set of eyes on your GEO or AEO plan?

report@aisearchlab.ai

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Free Analysis

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