Capabilities and products
- multilingual natural-language exhibitor matching
- graded intent leads with behavioral evidence
- exhibitor inquiry management
- buyer-exhibitor meeting scheduling
- exhibitor content and media management
- booth performance analytics
- Demand Radar show demand intelligence
- four-language public exhibitor page creation
- trade-show GEO and AI-search citable pages
- Demand Pulse trending demand themes and sample questions
- Reverse Match for unmatched high-intent demand
Exhibits
Askpo is the buyer-facing conversational assistant: buyers can describe what they need in natural language, use more than one hundred languages and receive matching exhibitors and booth information without having to browse a classification directory first. Each buyer action can contribute to intent evidence, including the questions asked, exhibitors saved, comparisons made, inquiries submitted and meetings requested, so the platform can preserve a trail of self-declared interest rather than only page-view counts. The exhibitor-facing graded-lead inbox classifies Grade A as strong intent with explicit purchasing or discussion interest, Grade B as high intent shown by active attention or repeated comparison of similar exhibitors, and Grade C as medium intent associated with initial exploration or information gathering. The grade is only an entry point because every lead carries behavioral evidence explaining why the buyer received that level, giving exhibitors a basis for deciding whether and how quickly to follow up. The exhibitor workspace combines the graded lead inbox and inquiry management with meeting scheduling, company and product content management, media management, exposure information and booth-performance analytics. Demand Radar gives organizers a year-round view of show demand and includes demand themes, recruitment opportunities and hot leads that are not yet being served by the existing exhibitor supply. The organizer tools also include Demand Pulse, where aggregated hot topics can be expanded to sample buyer questions, and Reverse Match, which surfaces high-intent needs that have not yet found an adequate exhibitor match. Demand-supply comparison is used to show what buyers are asking for against what the current exhibitor list can provide, creating evidence that can support exhibitor recruitment and exhibition-zone planning rather than relying only on general attendance totals. Deployment begins with the organizer providing the exhibitor list and existing materials and designating information-technology and marketing contacts, while Omni Matcher handles platform setup, exhibitor onboarding and day-to-day operation. The deployment process includes one-to-one exhibitor onboarding and an integration package that can be embedded with the organizer’s existing digital properties, with implementation described as taking from weeks to months depending on show scale. For exhibitors, the platform can package Grade A buyer intelligence with the lead list, behavioral evidence, show report and dashboard, and it can provide listing or recommendation placements plus Askpo answer prioritization under designated keywords. Buyer-exhibitor communication remains inside the platform, where exhibitor outreach is limited so the same exhibitor can send at most one message to a buyer per show, buyers have an overall daily message limit and buyers can disable this contact channel. The service package also includes platform-operation demonstrations, support for adoption and discussions with exhibitors and organizers about how the matchmaking service can be deployed and operated in cooperation with an event. Because the platform keeps public exhibitor pages, buyer questions, graded leads and organizer demand intelligence connected, its stated workflow links external search discovery, in-platform need clarification, exhibitor follow-up and organizer-level demand analysis in one system.
Company profile
Omni Matcher operates an AI business-matchmaking platform for trade shows and provides matchmaking services across the buyer, exhibitor and organizer sides, so one incoming demand can be converted into value for all three roles. The platform is designed to make a show and its exhibitors discoverable while prospective buyers are still searching for suppliers through Google or AI services such as ChatGPT and Gemini, including buyers who were not already present in the event registration list. Omni Matcher addresses the limitation that exhibitor information often remains in printed directories or login-protected event systems that search engines cannot read and AI search cannot cite, while exhibitors are not required to perform their own search optimization, change their websites or submit extra files. When an exhibitor list becomes public, the platform can create a four-language page for each exhibitor so the information can accumulate visibility in search and become readable by AI systems, extending discoverability beyond the days when a physical booth is open. For buyers, the Askpo conversational assistant accepts natural-language questions in more than one hundred languages and directs each stated need toward matching exhibitors and booths, after which buyers can save and compare exhibitors, send inquiries and request meetings. For exhibitors, Omni Matcher turns buyer behavior into graded intent leads with behavioral evidence attached to each lead, helping teams decide which opportunities deserve earlier follow-up instead of treating every visit as an undifferentiated traffic count. Exhibitors can also manage company descriptions, product content, inquiries and meeting schedules on the platform while reviewing their own exposure and booth-performance information. For organizers, the Demand Radar demand-intelligence function retains a show-wide view of what visitors are looking for, which demand themes are active, which opportunities can inform exhibitor recruitment and which hot needs have not yet been matched by the current supply side. The company distinguishes its output from conventional SEO results by focusing not only on exposure and clicks but on the buyers who enter, the requirements those buyers state and the leads exhibitors receive, while the organizer keeps the resulting demand information as an ongoing asset. Omni Matcher also supports a generative-engine-optimization approach in which public exhibitor pages are structured so services such as ChatGPT and Gemini can identify the show and its exhibitors, and organizers can see buyer visits originating from Google and AI search. The platform is designed to coexist with an organizer’s existing event application: the existing app can continue serving registered participants, while Omni Matcher focuses on buyers who are still searching externally and then carries their stated needs into the matchmaking workflow. Omni Matcher states that exhibitor lead views do not expose buyer email addresses, phone numbers or full personal names; exhibitors instead receive a company identity or masked name together with intent signals and behavioral evidence, and follow-up interactions stay inside the platform.