Technical Overview
Conversational ads like Spark by Adgentek deliver an interactive, brand-authored ad experience inside AI conversations. Here is the step-by-step path from user query to conversion, and how it differs from ChatGPT’s static sponsored links at every stage.
When a user asks an AI assistant a question with commercial intent, “what’s the best SUV for my family?” or “I need a new mattress for side sleepers”, Spark’s intent-matching engine identifies the conversational context and category relevance in real time.
Based on the detected intent, Spark selects the most relevant brand and product catalog from its advertiser network. The match is contextual, driven by what the user is actually asking about, not behavioral cookies or demographic targeting.
The conversational ad unit appears natively within the AI conversation: not a banner above or below the response, but an interactive branded experience that enhances the conversation. It includes a branded header, guided Q&A chips, free-text input, and clear sponsorship disclosure. This is fundamentally different from display ads transplanted into AI.
The user engages through chip selections and free-text questions. Each interaction serves two purposes: it helps the user find what they want, and it captures granular purchase intent signals, vehicle type, budget, features, timeline, passenger needs, and more.
Every interaction generates first-party intent data with full attribution, flowing directly to the brand’s CRM and analytics stack. Unlike a banner click that says someone was “interested,” Spark data says exactly what they want, what they can spend, and which features matter most.
Based on the user’s self-qualification, Spark surfaces contextual CTAs: Build & Price for auto, Shop Now for retail, Book a Consultation for services, Find a Dealer for local. Attribution tracks from first interaction through completed conversion.
Every response in a Spark ad is pre-authored and approved by the brand. Content comes from a brand-controlled content library, not from AI generation. This eliminates hallucination risk, keeps brand voice consistent, and gives brands full control over claims, pricing, and competitor mentions.
Spark deploys across multiple AI conversation platforms through Adgentek’s AdsMCP infrastructure. Brands are not locked to a single AI platform, unlike ChatGPT’s ad program, which only reaches ChatGPT users. For the full landscape, see How to Advertise in AI Chatbots.
See Spark in action across industries or contact hello@adgentek.ai.
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Get Started FreeWhen a user query enters an AI surface, the Adgentek Agentic Ad Server analyzes the conversational context in real time using semantic intent classification across nine intent buckets (purchase, transactional, consideration, comparison, research, informational, navigational, support, entertainment). The ad server extracts entities, matches against live advertiser demand, and returns a native ad unit formatted for conversational integration, all within the response latency window.
Adgentek captures conversational intent signals including IAB content categories, named entities (brands, products, locations), intent classification (purchase vs. research vs. comparison), and interactive signals from user responses within Spark ad units (product type, budget range, feature preferences, timeline). Only derived signals are transmitted to demand partners, raw user prompts are never shared.
Integration via AdsMCP (Model Context Protocol) takes minutes for any AI app that supports MCP, no SDK installation required. SDK integration typically takes a few hours. Direct API integration for custom deployments typically takes one to three days.