How the Delivr.ai Intent Pipeline Works

From licensed ad impressions and first-party pixel events to intent you can attach to a named person. This is how the Delivr.ai intent data pipeline acquires its data, resolves identities at scale, and parses context through LLM intelligence.

Stage 1: Multi-Source Data Acquisition

We don’t rely on a single source. Our pipeline is built from licensed supply-side data plus first-party pixel events, read through three types of signal, each revealing a different dimension of buyer intent.

Ad-Impression Data comes from licensed supply-side partners. Real-time programmatic advertising bid requests reveal which companies and individuals are being targeted across the open web. This signals active marketing campaigns, competitive positioning, and in-market buying behavior. Key signals include programmatic auction context, ad placement context, campaign timing patterns, competitive spend indicators, and geographic and firmographic metadata.

Page-Context Signals come from the same impressions. Each impression carries the page it was served on, so the content around it (B2B research sites, review platforms, vendor comparisons) shows what decision-makers read before they ever contact sales. Key signals include page topic, content category, vendor comparison context, and the sequence of pages a person reaches over time.

First-Party Pixel Events capture direct engagement on customer sites. Demo requests, content downloads, webinar registrations, trial signups, and contact form submissions are the strongest signal of active evaluation and purchase readiness.

Stage 2: Identity Resolution and Unified Pipeline

Raw signals are useless without identity. We resolve disparate data points into unified individual profiles — connecting anonymous web activity, programmatic signals, and direct engagement into a single coherent view.

Step 1 is Signal Ingestion: ad impressions, page context and first-party pixel events enter the pipeline. Each signal arrives with partial information, such as an IP address, cookie ID, MAID, company domain, or hashed email. Pixel visitors resolve in real time; intent and the graph refresh daily.

Step 2 is Identity Resolution: connecting the dots across fragmented signals. Our proprietary identity graph links anonymous signals to known individuals. We resolve cookies to hashed emails (HEM), MAIDs to HEM, IP addresses to households, and IP addresses to companies, then cross-reference engagement patterns to unify disparate touchpoints into single-person profiles.

Step 3 is Data Enrichment: building complete buyer profiles. Once identity is resolved, we enrich with firmographic, technographic, and role-based data. Job titles, seniority, budget authority, technology stack, company revenue, employee count, and organizational structure are layered onto each identified individual — 50+ data attributes per person refreshed daily.

Step 4 is the Unified Data Pipeline: a single source of truth for buyer intelligence. All signals (ad impressions, page context and pixel events) are merged into a unified pipeline. Each person now has a complete behavioral history: what they researched, when they researched it, which competitors they evaluated, what content they consumed, and what actions they took. The result is intent scored per person, every day, over a 14-day window.

Stage 3: LLM-Powered Context Parsing

Data without context is noise. Our advanced language models interpret semantic meaning, behavioral intent, and buying stage — transforming raw signals into strategic intelligence.

Traditional intent platforms stop at keyword matching. Delivr.ai’s LLM layer understands why someone is researching, what stage they’re in, and which context indicates genuine buying intent vs. casual browsing. We parse content, extract semantic meaning, and classify behavioral patterns to separate high-value signals from noise.

Semantic Intent Classification distinguishes “how to choose a CRM” (evaluation stage) from “CRM implementation mistakes” (problem-aware, not solution-ready). Not all searches indicate buying readiness — context determines whether someone is exploring options or actively evaluating vendors.

Contextual Relevance Scoring filters false positives. Our LLM scores relevance based on query semantics, page context, content depth, and behavioral sequence.

Competitive Intelligence Extraction identifies competitive sets when someone researches alternatives, extracts comparison criteria, and flags active evaluation — triggering competitive positioning campaigns in real time.

Urgency and Timing Detection flags windows when deals are most winnable. Frequency, recency, and depth of research signal urgency. Daily searches with increasing specificity indicate active evaluation. The window between initial research and vendor selection is often 7–14 days — our detection identifies prospects in this critical window.

Topic and Pain Point Extraction pulls specific pain points from content consumption: “integration challenges,” “cost reduction,” “compliance requirements.” These become targeting and messaging inputs that dramatically improve engagement and conversion rates.

Stage 4: Actionable Output Delivery

The final stage transforms processed intelligence into executable audience segments, individual profiles, and campaign recommendations — ready for immediate activation across your DSPs, CRM, and sales tools.

Person-Based Audiences deliver precision-targeted segments of identified decision-makers, mapped by role, intent strength, and company. Activate them through Delivr DSP or The Trade Desk, or deliver them to S3, email, Slack, HubSpot or Salesforce. Target the exact decision-makers evaluating your category with personalized creative.

Individual Intent Profiles deliver complete dossiers on high-value prospects — research history, competitor evaluation, pain points, buying stage, authority level, and recommended messaging angles. Sales teams receive warm leads with complete context, eliminating cold outreach and accelerating deal velocity.

Real-Time Alerts and Triggers deliver instant notifications for high-value buying signals. When high-value prospects enter evaluation mode, research competitors, or exhibit buying signals, revenue teams receive Slack or email alerts. Sales can engage immediately while intent is fresh, before competitors enter the conversation.

Competitive Advantages

Scale Without Noise: Most intent providers scale by lowering signal quality. We scale by filtering harder: a signal stays only if it resolves to a person and a topic.

Identity Precision: Account-level targeting reaches companies. Person-based targeting reaches decision-makers. The difference is measurable: 5x higher engagement, 72% reduction in wasted spend.

Context Clarity: Keywords can’t distinguish browsing from buying. Our LLM layer interprets semantic meaning, separating casual research from active evaluation.

Timing Intelligence: Intent decays, so freshness matters. Pixel visitors resolve in real time; intent and the graph refresh daily.