Product knowledge becomes agent-readable.
Listing, FAQ, reviews, offers, and real-time commerce context become reusable assets across search, shopping assistants, sales, support, and ads.
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The Agentic Revenue Execution OS
Build YourAI Agent Growth Army
LEAP is the Agentic Revenue Execution OS for AI-native enterprises. We help companies transform revenue generation from human-driven workflows into compute-scalable agent operations — connecting AI search, content, ads, commerce and revenue attribution into one execution control plane.
Human Steer. Agents Execute. Revenue Scales.
Every market. Every channel. One Agentic Revenue Execution OS.
7×24
agent execution
Prompt
to GMV attribution
4
product pillars
7
Named client case studies
102
Specialized agents · 4 layers
≈18%
AI-search revenue share — flagship case*
≈25–40%
Perplexity citation share — flagship case*
* Modeled estimates from engagement modeling + public AI-surface monitoring — see the case studies for methodology.
The Shift
Enterprise Growth Is Moving from the Human Ceiling to the Compute Ceiling.
For a hundred years, enterprise growth has been linear. Companies scaled by hiring more salespeople, marketers, content teams, ad optimizers and operators. But this approach is bounded — by headcount, coordination cost, experience-replication efficiency, and management span.
In the AI Agent era, enterprises can run a second growth organization. Not a fixed-headcount, fixed-shift workforce, but a fleet of reproducible, schedulable, verifiable and attributable AI Agents. They run market research, VOC mining, content generation, AI citation building, ad testing, sales conversion, customer service and revenue attribution — continuously.
LEAP's mission is to help enterprises migrate human-bound execution into Agentic Execution.
Traditional Growth Org vs. Agentic Second Growth Org
Traditional Growth Org
Agentic Second Growth Org
Relies on human experience
Relies on data, models and Agent collaboration
Organized by job titles
Organized by task flow and business objectives
Bounded execution hours
Runs 7×24, continuously
Experience hard to replicate
Workflows, prompts and knowledge accumulate as assets
Bounded by management span
Scales with compute and Agent count
Lagging post-mortems
Prompt → Session → GMV full-link attribution
The Vision
Raise the Global Growth Ceiling from Human Cap to Compute Cap.
LEAP believes AI Agents will rewire how enterprises generate revenue. In the past, companies managed people through departments, roles, processes and software. In the future, companies will manage business execution through data, models, Agents and tokens. Every enterprise will need a self-learning, self-executing, self-testing, self-attributing AI Agent growth army.
LEAP wants to be the operating system behind that growth army — so enterprise growth is no longer capped by human execution capacity, but scales with data, models, Agents and compute.
Every company will need an AI Agent growth army.
LEAP builds the operating system for it.
Our Mission
Build Every Enterprise's Second Growth Organization, Powered by AI Agents
LEAP's mission is to upgrade enterprises from human-driven linear growth orgs into Agent-driven exponential growth orgs. Through the Agentic Revenue Execution OS, we connect market, content, search, ads, commerce, customer service, data and revenue attribution into one executable, observable, optimizable and settle-able growth system.
LEAP doesn't just “do marketing” — we redefine how revenue is generated in the AI era.
01
Agentic Execution
Upgrade insight, creation, distribution, conversion and review from human-bound work into autonomous Agent task systems.
02
Revenue Intelligence
Connect Prompt, Citation, Session, Content, Ads and GMV signals into a measurable, attributable, optimizable revenue intelligence layer.
03
Compute-Scalable Growth
Decouple growth capacity from headcount so it scales with compute, model capability and Agent count.
Build the operating system behind every enterprise's AI Agent growth army.
About LEAP
We Are Defining How AI-Era Enterprises Generate Revenue
LEAP was founded on a clear conviction: AI isn't just a new marketing tool — it's a fundamental shift in how enterprises organize themselves. In the traditional business world, companies scaled growth through hiring, training, processes and software. In the AI Agent era, companies will scale through data, models, Agents and tokens — building a new kind of execution organization.
LEAP starts from real high-value growth scenarios — AI Commerce, GEO, advertising growth and Token GMV — and accumulates them into a Revenue Execution OS purpose-built for the AI era.
We help enterprises do three things.
Long-term goal
Become the infrastructure behind every enterprise's AI Agent growth army.
Be Seen by AI
Make your brand visible and recommendable in AI search and AI shopping assistants.
Be Understood by Agents
Make your product knowledge correctly understood and invokable by AI Agents.
Build a Second Growth Org
Stand up a sustainable, self-running AI Agent growth army inside your company.
One-line definition
LEAP is the Agentic Revenue Execution OS for AI-native enterprises — building AI Agent-powered second growth organizations that transform revenue generation from human-driven workflows into compute-scalable autonomous execution systems.
Products
LEAP Agentic Revenue OS
Four connected product layers turn AI visibility, commerce readiness, growth execution, and outcome measurement into a controllable system.
Click any product to see its detail · animated visual inside
Use Cases
Start from one verifiable revenue scene.
LEAP can begin with AI search, commerce readiness, content and ads, enterprise knowledge, or the full second growth organization.
Click any use case for its detail and animated visual
Two-agent handoff — KPI signal exchange
Handoff
Industries
Built for AI-driven growth across categories
LEAP adapts to each category's buyer behavior — translating category-specific questions, trust signals, and comparison patterns into a GEO knowledge layer that AI search can cite.
Click any industry to see its detail · animated visual inside
Methodology
From service delivery to Agentic Revenue OS.
Click any stage for its detail and animated visual
Platform Capabilities
Not a tool. A control plane for the AI growth army.
LEAP records execution, evidence, and revenue contribution instead of stopping at tasks and content deliverables.
Click any capability for its detail and animated visual
Metrics
Validate the value of agent execution with data.
Prompt, citation, session, content, ads, token cost, and GMV are connected into an operating review loop.
AI Visibility
Prompt CoveragePrompt Win RateBrand Mention RateProduct Recommendation Rate
Citation
Citation CountCitation QualitySource DiversityEvidence Screenshot Count
Traffic
AI Referral SessionGoogle AIO Click-throughChatGPT / Perplexity ReferralLanding Page Engagement
Content
Content Production VolumeContent Reuse RateContent Citation RateContent-to-GMV Contribution
Revenue
AI Attributed GMVAds LiftToken Cost per GMVAgent ROI
Engagement Models
From diagnosis to co-building a second growth organization.
Start with a focused AI search or commerce readiness package, then scale into agent operations, control-plane reporting, and result-based models.
Click any package for its detail and animated visual
Evidence replaces vague reporting.
AI answers, citation sources, screenshots, sessions, content contribution, and GMV signals form an auditable proof chain.
Case Studies·Revenue execution scenes
Seven execution snapshots,
measurable agentic growth lift.
Representative engagements across AI search growth, agentic commerce readiness, content-and-ads automation, and enterprise knowledge OS. Quantified results are derived from engagement modeling combined with public-source AI-surface monitoring and are labeled ≈ to indicate estimation.
Case 01·Consumer audio · Anker Group
Soundcore
Anker Group's audio sub-brand — moving from "budget alternative" to "premium-comparable at 10% lower price," including the Sleep A20 / A30 sleep-audio line.
Case 02·Smart cleaning · robot vacuum
Eureka
Globally recognized robot-vacuum brand competing for the value-tier crown — where AI engines parrot "Roomba" or "Roborock" by reflex even when the Eureka spec sheet is comparable at 30-40% lower price.
Case 03·Mom-tech / DTC maternity
Momcozy
Wearable-pump category leader holding ≈19% global market share — but losing AI-answer real estate to incumbent legacy brands (Medela / Spectra / Elvie).
Case 04·Variety / lifestyle retail
Miniso
Global variety retailer with 7,000+ stores across 100+ countries — fighting for AI-discovery in the "where to buy cute / licensed merch" answer fragments dominated by Daiso, Flying Tiger, and Five Below.
Case 05·Smart home / kitchen appliance conglomerate
Midea
World's largest home-appliance group by revenue (Fortune Global 500) — but losing AI-answer share for kitchen and home prompts because the parent-child brand graph (Comfee / Eureka / Toshiba Lifestyle) was fragmented.
Case 06·Smart pet care · self-cleaning litter box
Neakasa
Self-cleaning cat-litter-box specialist competing with Litter-Robot's premium-priced incumbency — where AI engines parrot "Litter-Robot 4 is best" by reflex even when the Neakasa M1 spec sheet is comparable at less than half the price.
Case 07·Cross-border automotive parts e-commerce
Tanlink
Cross-border automotive-parts retailer running the full AI Commerce OS — a 4-layer engagement showcase where LeapUnion's Agentic Commerce Growth platform generates per-SKU AI-readable schema across a 51,000-product catalog.
Blog·What we're shipping
Latest from LeapUnion
Announcements, behind-the-scenes ship logs, customer stories, and quick takes on the AI Commerce shift. Lighter cadence than Insights; closer to "what we're thinking right now."
Customer story
Jun 10, 2026·8 min
Case study, long form: running the full AI Commerce OS at 51,000-SKU scale with Tanlink
How a cross-border auto-parts retailer went from invisible in AI answers to ≈18% of revenue attributable to AI-search traffic — the complete 4-layer engagement, written up end to end.
The deepest engagement on our case-study roster, expanded to long form: per-SKU AI-readable schema across 51,000+ products and 1,200+ vehicle combinations, a 200-video fitment content program, and the attribution loop that ties citation lift to revenue. Quantified results are modeled estimates, labeled approximate throughout.
Methodology
Jun 10, 2026·5 min
From content vendor to enterprise AI operating system: the three leaps behind our new Evolution page
We just published a page that admits we used to be replaceable. Here is why the three-stage story — Video Commerce → AI Commerce → Agentic Enterprise Outcome — is the most honest sales document we have ever shipped.
Our new Evolution page tells the three-stage story of LEAP — from a pay-per-deliverable video vendor to an AI Commerce growth partner to the enterprise AI operating system — and why each leap changes what clients buy: deliverables, then GMV share, then full value-chain outcomes.
Industry commentary
May 26, 2026·11 min
Global Agent OS Ecosystem 2026 — seven layers, six regions, and where LEAP fits
A radar of the 2026 Agent OS war: 7 structural insights · 7 stack layers · 6 regional ecosystems · 6 avoidance patterns — distilled from the LEAP AI Research Subagent internal map, with 7 custom diagrams.
Our internal Agent OS radar, rewritten as a public blog post. Seven structural insights, seven stack layers, six regional ecosystems, six avoidance patterns — and where LEAP's AI Commerce / GEO Agent OS slots into the global map. With seven custom diagrams.
Announcement
May 3, 2026·3 min
leapunion.com 2.0 — now powered by Leap Agents Commerce OS
LEAP AI PTE. LTD. (operating as LeapUnion) has refreshed the public site to reflect the Leap Agents Commerce OS positioning. New Platform section, new Architecture diagram, 4 named clients added to the case-study roster, and an updated Solution Matrix taxonomy across the entire site.
Behind the scenes
May 3, 2026·4 min
What we learned shipping the Solution Matrix in 48 hours
Two days from internal strategy doc → live on the public site, with full JSON-LD schema alignment, 9 Total Solutions, 6 GEO add-ons, and a 6-row customer-archetype recommendation table. Here's the actual sequence — and why we split the work into two waves.
Customer story
May 2, 2026·4 min
From Brand X / Y / Z to named clients: why we de-anonymized our case studies
For the first 3 months of leapunion.com our 3 case studies were Brand X (consumer audio), Brand Y (smart home), Brand Z (mom-tech). On 2026-05-02 we replaced them with named clients — Soundcore, Eureka, Momcozy — and added 4 more (Miniso, Midea, Neakasa, Tanlink). Here's the call.
Methodology
May 2, 2026·5 min
Why we replaced 6 service tiles with a 4-layer Solution Matrix
Our old taxonomy was 6 tiles: GEO · Social & Content · AI-Commerce · Data Intelligence · Influencer & PR · Brand Knowledge Base. The new one is 4 strategic layers × 9 Total Solutions × 6 GEO add-ons. Here's why a flat list became a hierarchy — and what we tell clients picking a starting point.
Want a topic covered? Email contact@leapunion.com with [BLOG-PITCH] in the subject line.
Frequently Asked
Questions
Everything you need to know about Generative Engine Optimization, AI search, and how LeapUnion delivers brand visibility in the AI era.
Generative Engine Optimization (GEO) is the AI-commerce extension of SEO, content, product data, and attribution. It structures brand content so that AI surfaces — Google AI Overviews / AI Mode, Perplexity, SearchGPT, Gemini, Anthropic Claude, and Amazon Rufus — can ground their answers in your pages, feeds, reviews, and source ecosystem. On Google, that runs on the same Search index + ranking and quality systems used for blue links; on ChatGPT / Perplexity / Amazon it additionally requires feed, review, UGC, product-graph, and source-graph coverage.
GEO extends traditional SEO into AI-generated answers — it keeps crawlability, content quality, structured data, and authority, while adding citation share, answer prominence, source ecosystems, and commerce attribution. Per Google's May 2026 AI optimization guidance, AI Overviews and AI Mode are built on Google's core Search ranking and quality systems and highlight content from the Search index via retrieval + grounding and query fan-out; foundational SEO therefore stays load-bearing. Across non-Google surfaces (ChatGPT, Perplexity, Claude, Rufus), GEO adds the feed / review / community / knowledge-graph signals those engines uniquely rely on. SEO and GEO are layered, not opposed.
LeapUnion optimizes for the four major generative AI engines: Perplexity, OpenAI SearchGPT, Google AI Overviews, and Anthropic Claude. We also optimize for vertical AI search like Amazon Rufus (e-commerce) and Microsoft Copilot. Our methodology covers both training-data presence and live-retrieval citation patterns.
Initial GEO signals — structured data ingestion, FAQ surfacing — typically appear within 2 to 4 weeks. Sustained citation share growth across multiple AI platforms usually takes 60 to 90 days, depending on category competitiveness and existing brand authority. AI engines re-index citation graphs on different cadences, so we measure progress weekly across all 5+ surfaces.
Amazon Rufus is Amazon's generative AI shopping assistant, launched 2024. It answers product questions, recommends items, and synthesizes reviews. For brands, Rufus represents a new "AI shelf" — products surfaced in Rufus answers see materially higher click-through and conversion rates than products only in standard search. Optimizing for Rufus involves structured product data, review depth, and answer-pattern alignment.
A Brand Knowledge Base is a structured, machine-readable repository of authoritative facts about a brand: products, history, leadership, certifications, FAQs, and citations. AI search engines preferentially cite brands with consistent, well-sourced knowledge bases because they reduce hallucination risk. Wikipedia, Wikidata, and brand-owned `/about`, `/faq`, `/llms.txt` endpoints are common knowledge base surfaces.
We measure GEO across three layers: (1) Citation Share — how often a brand appears in AI answers for target prompts, vs. competitors; (2) Answer Prominence — position and prominence inside the answer (lead mention vs. trailing footnote); (3) Assisted Attribution — downstream traffic, leads, and revenue traced back to AI answer surfaces. We deliver weekly dashboards across all three.
Content Commerce is the end-to-end commercialization of branded content, from creation through transaction attribution. It combines viral short-form content (TikTok, YouTube Shorts), owned channels (YouTube, Instagram), and AI-search-aware copy so that every piece of content can be cited, ranked, and converted. LeapUnion bridges creative production and revenue attribution in a single pipeline.
Yes. GEO is fundamentally an organic-content strategy — AI engines do not have paid placement equivalents (yet). The core levers are structured data, citable original research, brand knowledge bases, and authoritative third-party mentions. Paid media can accelerate distribution of content assets that AI engines later ingest, but is not required.
AI engines preferentially cite content that is: (a) structured (FAQ, How-To, Article schemas); (b) factual with verifiable claims; (c) attributed (clear authorship and dates); (d) self-contained (full answers in one paragraph); (e) recently updated (lastmod within 6 months); and (f) consistent with knowledge graphs (Wikipedia, Wikidata, GitHub for tools). Listicles and comparison tables also perform well.
Perplexity is retrieval-first — every answer is grounded in 5-10 cited sources at query time, making it the most "SEO-like" of the AI engines. ChatGPT (without browsing) relies primarily on pre-training data, so brand presence depends on training-corpus inclusion (Wikipedia, Reddit, news). When ChatGPT enables browsing, it behaves closer to Perplexity. Both reward structured content, but Perplexity rewards freshness more.
LeapUnion serves consumer brands across smart home (Eufy, Anker, Soundcore), beauty and lifestyle (Miniso, Titika), pet care (Catlink, Petkit), small appliances (Cosori, Levoit), and wellness (Sweetnight, Renpho). Our framework is brand-agnostic but is currently optimized for D2C consumer e-commerce.
Engagements typically begin with a 4-week GEO Diagnostic — a baseline citation audit across all major AI platforms, knowledge graph health check, and content gap analysis. From there, a 90-day Sprint executes the priority optimizations. Long-term retainers focus on continuous monitoring, content production, and platform expansion.
Reach out via the contact form on this page or email contact@leapunion.com with your brand name and current AI search visibility goals. We respond within 1 business day with a free initial assessment and proposal scoping call.
Yes. LeapUnion has built an internal Agentic OS platform — including the Brand GEO Framework (BGF) — that orchestrates citation evidence collection (across Perplexity, ChatGPT, Google AIO, Gemini, Amazon Rufus), AI traffic attribution pipelines, and content production at scale. Tooling is proprietary; clients receive structured outputs (dashboards, reports, raw data exports).
Have a different question? contact@leapunion.com
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growth org.
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