Discover who AI recommends
Buyers are asking AI assistants which products to consider before they visit vendor websites. GLLEAM reveals why competitors enter those recommendations, turns visibility gaps into controlled marketing experiments, and measures whether the changes create qualified pipeline.
Built for growth, SEO, content, product-marketing, and digital-PR teams.
Everything you need
to turn visibility into evidence.
Observe the buyer journey
Track where your brand is recommended, mentioned, cited, omitted, or misrepresented across commercially important buyer questions.
Diagnose the evidence gap
Identify the competitor claims, owned pages, third-party sources, comparisons, and documentation influencing AI-generated answers.
Run controlled experiments
Turn each finding into a measurable hypothesis with a treatment, baseline, target prompt group, control group, and observation period.
Attribute commercial impact
Connect changes in AI discovery to qualified traffic, demo requests, assisted conversions, opportunities, and pipeline where data is available.
Four connected layers.
One evidence loop.
GLLEAM moves beyond monitoring by connecting buyer intent, repeated observations, evidence analysis, and controlled experimentation in one auditable workflow.
Questions that reflect real buying decisions
Build a customer-specific measurement universe from Search Console data, paid-search queries, sales conversations, support questions, product documentation, category research, and customer-approved prompts.
Patterns instead of isolated screenshots
Measure recommendation inclusion, mentions, citations, competitors, source adoption, narrative consistency, and answer stability through repeated runs and prompt paraphrases.
Trace recommendations back to claims and sources
Map the relationships among buyer questions, brands, competitors, product claims, owned pages, third-party evidence, citations, and AI-generated answers.
Test what changes the result
Manage hypotheses, treatments, controls, baselines, measurement periods, outcomes, interpretation, and experiment history without forcing every result into a success story.
From buyer question
to measurable outcome.
Define the buyer journey
Identify the high-value recommendation, comparison, alternative, integration, and category questions that influence customer shortlists.
Observe and diagnose
Run repeated observations, compare competitors, extract cited sources, and identify the claims or evidence gaps shaping the answer.
Intervene and experiment
Implement a focused treatment such as improving product evidence, updating documentation, correcting a third-party profile, or strengthening a comparison page.
Measure business impact
Compare the treatment against its baseline and control group, then connect the result to qualified engagement and pipeline where possible.
Connect AI recommendations
to the evidence behind them.
A visibility score can show that a competitor appears more often. It cannot explain what the marketing team should do next. GLLEAM traces recurring recommendations back to specific claims, pages, citations, and third-party sources, then converts the gap into a testable intervention.
Build an auditable source and claim map
Connect every finding to its buyer question, evidence, competitor claim, treatment, and measurement plan.
Illustrative example — actual findings depend on the customer, category, sources, and selected AI surfaces.
AI discovery measurement
without false certainty.
AI answers vary across prompts, models, geography, language, and time. GLLEAM makes that uncertainty visible instead of hiding it behind a single deterministic score.
Repeated observations
One answer is anecdotal. Repeated runs and prompt paraphrases reveal whether a recommendation pattern is stable or incidental.
Surface-level transparency
An API result may not reproduce a consumer-facing application. Every observation records how and where it was collected.
Treatment and control groups
Before-and-after movement is not automatically causal. GLLEAM uses baselines and untreated prompt groups where practical.
Auditable records
Every conclusion remains connected to its prompt, answer, sources, collection context, experiment treatment, result, and interpretation.
Built for marketers.
Rigorous enough for analysts.
One buyer question.
A complete evidence trail.
GLLEAM does not stop at reporting whether a brand appeared. It identifies what repeatedly supports the winning recommendation, what is missing, and which intervention can be tested.
A structured pilot for teams
ready to test one meaningful intervention.
GLLEAM is working with a focused group of B2B SaaS marketing teams to validate its observation, diagnosis, experimentation, and attribution workflow.
Understand the current AI discovery landscape
- Customer-specific buyer-intent prompt panel
- Brand and competitor baseline
- Recommendation and citation analysis
- Narrative-accuracy review
- Source and claim mapping
Turn one important gap into a measurable test
- Prioritized evidence gap
- Proposed treatment
- Target and control prompt groups
- Baseline and measurement period
- Post-intervention observations
- Results interpretation
Help shape the product around real decisions
- Direct access to the founding team
- Collaborative experiment review
- Influence over product priorities
- Commercial attribution where data permits
- Optional anonymized research participation
The pilot suits B2B SaaS companies with an established SEO, content, or product-marketing program and the ability to implement one focused intervention. GLLEAM measures and tests observed AI-discovery outcomes — it does not guarantee placement by any third-party AI system.
Stop guessing what will make
AI recommend your brand.
Find the recommendation gaps that matter, understand the evidence behind them, and test the interventions most likely to create commercial impact.