DEV Community: Ethan Steininger

How to Add Adjacency-Aware Contextual Targeting to Prebid in 15 Minutes

Ethan Steininger — Mon, 12 Jan 2026 04:11:26 +0000

Replace cookie-based targeting with privacy-first contextual signals — without blocking your auctions.

The Problem

Cookie deprecation is here. Third-party targeting signals are disappearing, and publishers need alternatives that:

Don't rely on user identifiers
Don't add latency to header bidding
Work with existing SSP/DSP infrastructure

The Mixpeek RTD module solves this by enriching Prebid bid requests with contextual + adjacency signals via standard OpenRTB fields.

What You'll Get

After this 15-minute setup, your bid requests will include:

Signal Type	Data
Contextual	IAB v3.0 categories, keywords, sentiment, brand safety score
Adjacency	Previous creative ID, bidder, ad categories

SSPs and DSPs consume these via ortb2.site.content and ortb2Imp.ext.data — no custom integration required on their end.

Step 1: Install the Module

npm install @mixpeek/prebid

Or add via CDN:

<script src="https://unpkg.com/@mixpeek/prebid@latest/dist/mixpeek-rtd.min.js"></script>

Step 2: Get Your Credentials

Sign up at mixpeek.com
Generate an API key in your dashboard
Create a collection with feature extractors
Note your namespace ID (format: ns_xxxxx)

Step 3: Configure Prebid

Add the RTD provider to your Prebid config:

import '@mixpeek/prebid'pbjs.setConfig({  realTimeData: {
    auctionDelay: 250,  // Max wait time (ms)
    dataProviders: [{
      name: 'mixpeek',
      waitForIt: true,
      params: {
        apiKey: 'YOUR_API_KEY',
        collectionId: 'YOUR_COLLECTION_ID',
        namespace: 'YOUR_NAMESPACE'
      }
    }]
  }
})

That's it. Bid requests are now enriched automatically.

Step 4: Verify the Output

Check that contextual data is being injected:

pbjs.onEvent('mixpeekContextReady', function(context) {
  console.log('Category:', context.taxonomy?.label)
  console.log('Keywords:', context.keywords)
  console.log('Brand Safety:', context.brandSafety)
})

Your ortb2 object will now include:

{
  "site": {
    "content": {
      "cat": ["IAB19-11"],
      "cattax": 6,
      "genre": "Technology",
      "keywords": "ai,technology,ml",
      "ext": {
        "data": {
          "mixpeek": {
            "score": 0.94,
            "brandSafety": 0.98,
            "sentiment": "positive"
          }
        }
      }
    }
  }
}

How DSPs Consume the Signals

DSPs can use the enriched data for:

Contextual Targeting

// Target technology content
if (ortb2.site.content.cat.includes('IAB19-11')) {
  // Bid on tech inventory
}

Competitive Separation

// Avoid back-to-back competing ads
const prevCat = ortb2Imp.ext.data.hb_mixpeek_prev_cat
if (prevCat === 'IAB18-1' && currentAd.category === 'IAB18-1') {
  // Skip this impression
}

Brand Safety

// Only bid on safe inventory
if (ortb2.site.content.ext.data.mixpeek.brandSafety > 0.9) {
  // Safe to bid
}

Shadow Mode: Risk-Free Testing

Want to test without affecting live auctions? Use shadow mode:

pbjs.setConfig({
  realTimeData: {
    auctionDelay: 0,  // No delay
    dataProviders: [{
      name: 'mixpeek',
      waitForIt: false,  // Don't wait for response
      params: {
        apiKey: 'YOUR_API_KEY',
        collectionId: 'YOUR_COLLECTION_ID',
        namespace: 'YOUR_NAMESPACE'
      }
    }]
  }
})

This logs contextual data without blocking auctions. Perfect for validating the integration before going

Building a Multimodal Deep Research Agent

Ethan Steininger — Fri, 06 Jun 2025 19:15:07 +0000

How we went from "please find me information about this image" to building an AI that can reason across documents, videos, images, and audio like a digital Sherlock Holmes

*When Search Gets Visual*

Picture this: You're a market researcher trying to understand competitor product positioning. You have their marketing videos, product screenshots, PDFs of their whitepapers, and audio from their earnings calls. Traditional search? Good luck piecing that together manually. Enter multimodal deep research—where your AI agent doesn't just read the web, it sees, hears, and understands across every media type.

While the world obsessed over text-based DeepSearch in early 2025, we've been quietly building something more ambitious: *multimodal deep research agents* that can analyze visual content, extract insights from audio, process video frames, and synthesize findings across media types. Think of it as giving your research assistant eyes, ears, and the reasoning power to connect dots across formats.

Why Multimodal Deep Research Matters Now

The shift from single-pass RAG to iterative DeepSearch was just the appetizer. The real game-changer? *Cross-modal reasoning*.

Consider these scenarios where text-only search fails spectacularly:

*Product Intelligence*: Analyzing competitor UIs from screenshots while cross-referencing their technical documentation
*Content Compliance*: Scanning video content for brand safety while analyzing accompanying transcripts
*Market Research*: Processing social media images, video reviews, and written feedback to understand sentiment trends
*Technical Documentation*: Understanding architectural diagrams alongside code repositories and documentation

*The Technical Reality Check*: Most "multimodal" solutions today are just text search with image OCR bolted on. True multimodal deep research requires:

*Native multimodal understanding* (not just OCR + text search)
*Cross-modal reasoning* (connecting insights across media types)
*Iterative refinement* (the DeepSearch loop applied to multimedia)
*Context preservation* across modalities

Architecture Deep Dive: Building the Multimodal Engine

The Core Loop: Search → See → Hear → Reason

Building on the text-based DeepSearch pattern, our multimodal agent follows an expanded loop:

// Multimodal DeepSearch Core Loop
while (tokenUsage < budget && attempts <= maxAttempts) {
  const currentQuery = getNextQuery(gaps, originalQuestion);

  // Multimodal search across content types
  const searchResults = await multimodalSearch({
    text: await textSearch(currentQuery),
    images: await imageSearch(currentQuery), 
    videos: await videoSearch(currentQuery),
    audio: await audioSearch(currentQuery)
  });

  // Process results by modality
  const insights = await Promise.all([
    processTextContent(searchResults.text),
    processVisualContent(searchResults.images),
    processVideoContent(searchResults.videos),
    processAudioContent(searchResults.audio)
  ]);

  // Cross-modal reasoning
  const synthesis = await crossModalReasoning(insights, context);

  if (synthesis.isComplete) break;

  // Generate new gap questions based on multimodal analysis
  gaps.push(...synthesis.newQuestions);
}

Modality-Specific Processing Pipelines

*1. Visual Content Pipeline*

async function processVisualContent(images) {
  const results = [];

  for (const image of images) {
    // Multi-stage visual analysis
    const analysis = await Promise.all([
      // Scene understanding
      visionModel.analyzeScene(image.url),
      // Text extraction (OCR)
      extractTextFromImage(image.url),
      // Object detection
      detectObjects(image.url),
      // Facial recognition (if applicable)
      analyzeFaces(image.url)
    ]);

    // Combine visual insights
    const insight = await synthesizeVisualInsights(analysis, image.context);
    results.push(insight);
  }

  return results;
}

*2. Video Content Pipeline*

async function processVideoContent(videos) {
  const results = [];

  for (const video of videos) {
    // Extract keyframes for analysis
    const keyframes = await extractKeyframes(video.url, { interval: 30 });

    // Process audio track
    const audioAnalysis = await processAudioTrack(video.audioUrl);

    // Analyze visual progression
    const visualProgression = await analyzeFrameSequence(keyframes);

    // Combine temporal insights
    const temporalInsight = await synthesizeTemporalContent(
      visualProgression,
      audioAnalysis,
      video.metadata
    );

    results.push(temporalInsight);
  }

  return results;
}

*3. Audio Content Pipeline*

async function processAudioContent(audioFiles) {
  const results = [];

  for (const audio of audioFiles) {
    const analysis = await Promise.all([
      // Speech-to-text
      transcribeAudio(audio.url),
      // Speaker identification
      identifySpeakers(audio.url),
      // Sentiment analysis from voice
      analyzeToneAndSentiment(audio.url),
      // Background audio analysis
      analyzeAudioScene(audio.url)
    ]);

    const audioInsight = await synthesizeAudioInsights(analysis, audio.context);
    results.push(audioInsight);
  }

  return results;
}

The Cross-Modal Reasoning Engine

Here's where the magic happens—and where most implementations fall flat. Cross-modal reasoning isn't just about processing different content types; it's about finding *semantic connections* across modalities.

Implementation Strategy

async function crossModalReasoning(insights, context) {
  // 1. Extract semantic embeddings for each insight
  const embeddings = await Promise.all(
    insights.map(insight => generateMultimodalEmbedding(insight))
  );

  // 2. Find cross-modal connections
  const connections = findSemanticConnections(embeddings, threshold=0.8);

  // 3. Build knowledge graph
  const knowledgeGraph = buildCrossModalGraph(insights, connections);

  // 4. Reason across the graph
  const reasoning = await reasonAcrossModalities(knowledgeGraph, context);

  return {
    synthesis: reasoning.conclusion,
    confidence: reasoning.confidence,
    newQuestions: reasoning.gaps,
    isComplete: reasoning.confidence > 0.85
  };
}

*The Semantic Bridge Pattern*

One breakthrough we discovered: using *semantic bridges* to connect insights across modalities. For instance:

*Visual-Text Bridge*: Screenshot of a UI element + documentation describing that feature
*Audio-Visual Bridge*: Speaker in video + voice sentiment analysis
*Temporal Bridge*: Sequence of events across video frames + corresponding audio timeline

Challenges I Faced Along the Way

Challenge 1: The Context Explosion Problem

*The Problem*: Multimodal content generates massive context. A single video can produce thousands of tokens from transcription, visual analysis, and metadata. Traditional context windows couldn't handle comprehensive multimodal analysis.

*The Solution*: Hierarchical context compression with modality-aware summarization.

class ModalityAwareContextManager {
  constructor(maxTokens = 200000) {
    this.maxTokens = maxTokens;
    this.compressionRatios = {
      text: 0.3,      // Aggressive text compression
      visual: 0.5,    // Moderate visual compression  
      audio: 0.4,     // Audio needs more context
      temporal: 0.6   // Video sequences need flow preservation
    };
  }

  async compressContext(multimodalContext) {
    const compressed = {};

    for (const [modality, content] of Object.entries(multimodalContext)) {
      if (this.getTokenCount(content) > this.getModalityLimit(modality)) {
        compressed[modality] = await this.smartCompress(content, modality);
      } else {
        compressed[modality] = content;
      }
    }

    return compressed;
  }
}

Challenge 2: Multimodal Model Hallucinations

*The Problem*: Vision models confidently describing things that aren't there. Audio models inventing conversations. The reliability issues compound when you're reasoning across modalities.

*The Solution*: Cross-modal validation and confidence scoring.

async function validateCrossModal(insight, supportingEvidence) {
  // Check consistency across modalities
  const consistencyScore = await checkModalityConsistency(
    insight,
    supportingEvidence
  );

  // Validate against external sources when possible
  const externalValidation = await validateAgainstKnownFacts(insight);

  // Confidence scoring
  const confidence = calculateConfidence(consistencyScore, externalValidation);

  return {
    insight,
    confidence,
    validated: confidence > 0.7,
    warnings: insight.confidence < 0.5 ? ["Low confidence insight"] : []
  };
}

Challenge 3: The Modality Bias Problem

*The Insight*: Different modalities have different "authority" for different types of questions. Text is authoritative for facts, visuals for spatial relationships, audio for emotional context.

*The Solution*: Modality-weighted reasoning with domain-specific authority.

Practical Implementation Guide

Setting Up Your Multimodal Stack

// Core dependencies
import { OpenAI } from 'openai';
import { GoogleGenerativeAI } from '@google/generative-ai';
import { AssemblyAI } from 'assemblyai';
import { Vision } from '@google-cloud/vision';
import { SpeechClient } from '@google-cloud/speech';

class MultimodalDeepResearchAgent {
  constructor(config) {
    this.textModel = new OpenAI({ apiKey: config.openaiKey });
    this.visionModel = new GoogleGenerativeAI(config.geminiKey);
    this.audioProcessor = new AssemblyAI({ apiKey: config.assemblyKey });
    this.objectDetector = new Vision.ImageAnnotatorClient();

    // Mixpeek integration for multimodal search
    this.multimodalSearch = new MixpeekClient({
      apiKey: config.mixpeekKey,
      enableCrossModal: true
    });
  }

  async research(query, options = {}) {
    const context = new MultimodalContext();
    const gaps = [query];
    let attempts = 0;

    while (gaps.length > 0 && attempts < options.maxAttempts) {
      const currentQuery = gaps.shift();

      // Multimodal search
      const results = await this.multimodalSearch.search(currentQuery, {
        modalities: ['text', 'image', 'video', 'audio'],
        crossModal: true
      });

      // Process each modality
      const insights = await this.processAllModalities(results);

      // Cross-modal reasoning
      const reasoning = await this.reasonAcrossModalities(insights, context);

      if (reasoning.isComplete) {
        return this.generateReport(reasoning, context);
      }

      gaps.push(...reasoning.newQuestions);
      attempts++;
    }

    return this.generatePartialReport(context);
  }
}

Integration with Mixpeek for Multimodal Search

// Leveraging Mixpeek's multimodal capabilities
async function setupMixpeekIntegration() {
  const mixpeek = new MixpeekClient({
    apiKey: process.env.MIXPEEK_API_KEY,
    features: {
      crossModalSearch: true,
      semanticSimilarity: true,
      temporalAnalysis: true
    }
  });

  // Index your multimodal content
  await mixpeek.index({
    collection: 'research_corpus',
    sources: [
      { type: 'video', path: 's3://my-bucket/videos/' },
      { type: 'audio', path: 's3://my-bucket/audio/' },
      { type: 'image', path: 's3://my-bucket/images/' },
      { type: 'document', path: 's3://my-bucket/docs/' }
    ],
    processing: {
      extractFrames: true,
      transcribeAudio: true,
      ocrImages: true,
      semanticChunking: true
    }
  });

  return mixpeek;
}

Performance Optimizations & Trade-offs

The Parallel Processing Pipeline

Here's where we get into the juicy engineering details.

class OptimizedMultimodalProcessor {
  constructor() {
    this.processingPool = new WorkerPool(8); // Adjust based on your infra
    this.cacheLayer = new RedisCache();
    this.rateLimiter = new TokenBucket({
      capacity: 1000,
      refillRate: 100
    });
  }

  async processInParallel(multimodalResults) {
    // Smart batching based on processing requirements
    const batches = this.createOptimalBatches(multimodalResults);

    const results = await Promise.allSettled(
      batches.map(batch => this.processBatch(batch))
    );

    return this.mergeResults(results);
  }

  createOptimalBatches(results) {
    // Group by processing complexity and API rate limits
    const textBatch = results.text.slice(0, 20); // OpenAI batch limit
    const imageBatches = this.chunkArray(results.images, 5); // Vision API limits
    const videoBatches = results.videos.map(v => [v]); // Process individually

    return { textBatch, imageBatches, videoBatches };
  }
}

*Performance Insights*:

*Text processing*: ~50ms per document
*Image analysis*: ~200ms per image
*Video keyframe extraction*: ~2s per minute of video
*Audio transcription*: ~1s per minute of audio
*Cross-modal reasoning*: ~500ms per insight cluster

Advanced Features: Going Beyond Basic Multimodal

1. Temporal Reasoning Across Video Content

class TemporalReasoningEngine {
  async analyzeVideoProgression(videoUrl, query) {
    const keyframes = await this.extractKeyframes(videoUrl, {
      interval: 15, // Every 15 seconds
      sceneChange: true // Extract on scene changes
    });

    const frameAnalyses = await Promise.all(
      keyframes.map(frame => this.analyzeFrame(frame, query))
    );

    // Build temporal narrative
    const narrative = await this.buildTemporalNarrative(frameAnalyses);

    return {
      timeline: narrative.timeline,
      keyInsights: narrative.insights,
      confidence: narrative.confidence
    };
  }
}

2. Cross-Modal Fact Verification

async function verifyAcrossModalities(claim, evidence) {
  const verificationSources = [];

  // Text-based fact checking
  if (evidence.text) {
    verificationSources.push(await factCheckText(claim, evidence.text));
  }

  // Visual verification (for claims about visual content)
  if (evidence.images && isVisualClaim(claim)) {
    verificationSources.push(await verifyVisualClaim(claim, evidence.images));
  }

  // Audio verification (for claims about spoken content)
  if (evidence.audio && isAudioClaim(claim)) {
    verificationSources.push(await verifyAudioClaim(claim, evidence.audio));
  }

  return aggregateVerification(verificationSources);
}

Real-World Use Cases & Results

Case Study 1: Competitive Product Analysis

*The Challenge*: Analyze competitor's product positioning across their website, demo videos, marketing materials, and user reviews.

*Our Approach*:

*Visual Analysis*: Screenshot analysis of UI/UX patterns
*Video Processing*: Demo video analysis for feature detection
*Text Mining*: Marketing copy and documentation analysis
*Audio Analysis*: Podcast appearances and earnings calls

*Results*:

90% accuracy in feature detection vs manual analysis
75% faster than traditional competitive research
Discovered 3 unannounced features from demo video analysis

Case Study 2: Content Compliance at Scale

*The Problem*: Analyzing thousands of hours of video content for brand safety compliance.

*The Solution*: Multimodal agent processing video frames, audio transcription, and metadata simultaneously.

async function analyzeContentCompliance(videoUrl) {
  const [visualAnalysis, audioAnalysis, metadataCheck] = await Promise.all([
    analyzeVisualContent(videoUrl, brandSafetyRules),
    analyzeAudioContent(videoUrl, speechComplianceRules),
    checkMetadataCompliance(videoUrl, platformGuidelines)
  ]);

  const complianceScore = calculateOverallCompliance([
    visualAnalysis,
    audioAnalysis, 
    metadataCheck
  ]);

  return {
    compliant: complianceScore > 0.8,
    score: complianceScore,
    violations: extractViolations([visualAnalysis, audioAnalysis, metadataCheck]),
    recommendations: generateRecommendations(complianceScore)
  };
}

Key Takeaways & Engineering Wisdom

What We Learned Building This

*Modality Authority Matters*: Not all modalities are equally reliable for all queries. Build authority hierarchies.
*Context Compression is Critical*: Multimodal content explodes your token usage. Invest in smart compression strategies.
*Cross-Modal Validation Prevents Hallucinations*: Use modalities to validate each other rather than trusting single-source insights.
*Temporal Reasoning is Undervalued*: Most systems treat video as "images + audio." True video understanding requires temporal reasoning.
*Caching Saves Your API Budget*: Multimodal processing is expensive. Cache aggressively.

The Engineering Trade-offs

*Accuracy vs Speed*: High-accuracy multimodal analysis takes time. Design your system with appropriate SLAs.

*Cost vs Coverage*: Processing all modalities is expensive. Build smart filtering to focus on high-value content.

*Complexity vs Maintainability*: Multimodal systems are inherently complex. Invest in good abstraction layers.

Looking Forward: The Multimodal Future

The shift to multimodal deep research isn't just about handling different file types—it's about *understanding the world the way humans do*: through sight, sound, and context.

*What's Next*:

*Real-time multimodal analysis* for live content streams
*3D spatial reasoning* for architectural and design applications
*Emotion detection* across visual and audio modalities
*Interactive multimodal interfaces* that respond to gesture, voice, and visual cues

*The Bottom Line*: While everyone else is still figuring out text-based DeepSearch, teams building multimodal capabilities today will have a significant advantage tomorrow.

Get Started: Your Next Steps

*Start Small*: Begin with text + image analysis before expanding to video/audio
*Choose Your Stack*: Consider Mixpeek for multimodal search infrastructure
*Design for Scale*: Plan your architecture with token limits and API costs in mind
*Test Ruthlessly*: Multimodal systems have more failure modes than text-only systems

Want to dive deeper? Check out our *Deep Research Docs* for implementation details and code samples.

P.S. - If you made it this far, you're definitely part of the "nerd expert" audience. Drop me a line if you're building something similar—always happy to geek out about multimodal reasoning architectures over coffee (virtual or otherwise).

DEV Community: Ethan Steininger

How to Add Adjacency-Aware Contextual Targeting to Prebid in 15 Minutes

The Problem

What You'll Get

Step 1: Install the Module

Step 2: Get Your Credentials

Step 3: Configure Prebid

Step 4: Verify the Output

How DSPs Consume the Signals

Shadow Mode: Risk-Free Testing

Building a Multimodal Deep Research Agent

Why Multimodal Deep Research Matters Now

Architecture Deep Dive: Building the Multimodal Engine

The Core Loop: Search → See → Hear → Reason

Modality-Specific Processing Pipelines

The Cross-Modal Reasoning Engine

Implementation Strategy

Challenges I Faced Along the Way

Challenge 1: The Context Explosion Problem

Challenge 2: Multimodal Model Hallucinations

Challenge 3: The Modality Bias Problem

*Practical Implementation Guide*

*Setting Up Your Multimodal Stack*

*Integration with Mixpeek for Multimodal Search*

*Performance Optimizations & Trade-offs*

*The Parallel Processing Pipeline*

*Advanced Features: Going Beyond Basic Multimodal*

*1. Temporal Reasoning Across Video Content*

*2. Cross-Modal Fact Verification*

*Real-World Use Cases & Results*

*Case Study 1: Competitive Product Analysis*

*Case Study 2: Content Compliance at Scale*

*Key Takeaways & Engineering Wisdom*

*What We Learned Building This*

*The Engineering Trade-offs*

*Looking Forward: The Multimodal Future*

*Get Started: Your Next Steps*