How the Delivr.ai Intent Pipeline Works

From trillions of raw signals to 35 billion daily actionable insights — this is how the Delivr.ai intent data pipeline acquires unique data sources, resolves identities at scale, and parses context through advanced LLM intelligence to deliver the most precise person-based intent in the market.

Stage 1: Multi-Source Data Acquisition

We don’t rely on a single source. Our pipeline ingests trillions of signals daily from three types of channels — some proprietary, some generally available — each revealing different dimensions of buyer intent.

Bid Stream Data processes billions of daily impressions. 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.

Browsing Behavior Data tracks hundreds of billions of page views. Privacy-compliant web activity tracking across B2B research sites, review platforms, vendor comparisons, and content consumption patterns lets us see what decision-makers research before they ever contact sales. Key signals include content consumption patterns, vendor comparison activity, review site engagement, time-on-page and depth metrics, and research sequence mapping.

Form and Engagement Data captures millions of intent signals per week. Direct expressions of interest — demo requests, content downloads, webinar registrations, trial signups, and contact form submissions — represent 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: trillions of raw data points enter the pipeline. Bid stream events, page views, form fills, and engagement signals are captured in real time from our three core data sources. Each signal arrives with partial information — an IP address, cookie ID, MAID, company domain, or hashed email. We process 1T+ daily signals with sub-100ms ingestion latency.

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. We achieve a 97% B2B identity match rate at individual person-level granularity.

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 with real-time enrichment updates.

Step 4 is the Unified Data Pipeline: a single source of truth for buyer intelligence. All signals — bid stream, browsing, and form engagement — 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 35B daily unified signals with 100% cross-source correlation.

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). We classify intent stage with 94% accuracy. 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. Traditional keyword matching produces 40–60% false positive rates; our contextual scoring reduces false positives to under 5%, ensuring budget flows only to genuine prospects.

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

Buying Committee Role Mapping uses research patterns to reveal organizational roles. A VP searches “ROI analysis” while IT searches “security certifications.” We map each person’s concern to their likely role in the buying committee, enabling personalized messaging: ROI proof for executives, technical specs for IT, implementation support for operations.

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 stage, company, and buying committee position. Upload audience segments directly to Google DV360, The Trade Desk, or Amazon DSP for immediate campaign activation. 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 increasing filtering rigor — consuming trillions to output only the 35 billion signals that matter.

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 with 94% accuracy.

Timing Intelligence: Intent decays. Traditional platforms detect buying signals 48–72 hours too late. Our real-time pipeline activates campaigns within 4 hours of intent detection — while minds are still open.

Committee Visibility: B2B deals require consensus. We don’t just identify one person — we map the entire buying committee, their individual research patterns, and their organizational influence.

No other platform operates at this scale, precision, or speed. The combination of unique data sources, identity resolution infrastructure, and LLM-powered context parsing creates a competitive moat that traditional intent providers cannot replicate.