Search API

Search API for Viral Social Data

API-accessible social data enriched with keywords, brand, products, objects so you can query what's actually driving culture.

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Built for insight, innovation, and strategy teams

Search output snapshot

1 returned post preview

Period

last_24h

Posts

912

Total engagement

2,849,100

Top returned post

velocity 9.3likes 128,400comments 4,120https://instagram.com/reel/...

Transcript snippet

Generating play-by-play

queued...
Preparing frame-by-frame analysis...
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Product capabilities

Real-time social search
for brand, product, and trend intelligence

Query social content with structured enrichment across brands, ingredients, flavors, objects, and product signals without scraping together noisy consumer research inputs.

Track product mentions as they break out

Search by flavors, brands, and ingredients

Get AI-enriched summaries and visible signals

Use structured outputs across your workflows

Neon outline food scene reference
burgerfriessoda
#foodtok
McDonald's
Five Guys
brand: McDonald'sbrand: Five Guysobject: burgerobject: fries

Use cases

Social search that maps to real business questions

Built for teams that need more than dashboards and raw post URLs.

Innovation teams

Spot ingredients, products, formats, and flavor ideas before they peak in mainstream reports.

Consumer insights

Track recurring cultural signals with richer context than keyword-only listening platforms.

Brand strategy

Understand what people are actually showing, saying, and reacting to around your category.

Query example

Running club culture

Traditional provider

raw + transcript
{
  "query": "running club culture",
  "result": {
    "post_id": "1819f0d8-8af1-43dc-9ea8-6be3ac2c17c6",
    "platform": "instagram",
    "posted_at": "2026-02-08T06:12:44Z",
    "caption": "5AM run club in the city. Everyone showed up before sunrise.",
    "transcript_raw": "we met at 5am and ran five miles before work",
    "likes": 12108,
    "comments": 287
  }
}

Sociable Viral API

structured + video analysis
{
  "query": "running club culture",
  "result": {
    "postId": "1819f0d8-2b17-4a2e-9ab0-6416b6d4406a",
    "postedAt": "2026-02-08T06:12:44Z",
    "platform": "instagram",
    "postUrl": "https://www.instagram.com/reel/...",
    "media": {
      "videoUrl": "https://media.sociable.ai/posts/...mp4",
      "thumbnailUrl": "https://media.sociable.ai/posts/...jpg"
    },
    "performanceNow": {
      "likes": 12108,
      "comments": 287,
      "growthVelocity": 2.32
    },
    "performanceTrend": [
      { "time": "2026-02-08T06:30:00Z", "likes": 1102, "comments": 31 },
      { "time": "2026-02-08T07:00:00Z", "likes": 3420, "comments": 79 },
      { "time": "2026-02-08T08:00:00Z", "likes": 7214, "comments": 166 },
      { "time": "2026-02-08T10:00:00Z", "likes": 12108, "comments": 287 }
    ],
    "enrichment": {
      "contentSummary": "City run-club meetup content focused on routine, accountability, and post-run social ritual.",
      "whatHappensInVideo": "00:00 A wide shot shows runners arriving before sunrise at a city corner with reflective vests and blinking clip lights. 00:05 The camera moves past a folding table with water cups and bananas while people check watches and tighten laces. 00:11 A run-club lead points to a route map on a phone and explains that the group will split by pace. 00:16 On-screen labels mark easy, steady, and tempo groups as different packs begin jogging out. 00:23 The creator films side-by-side clips of two runners discussing breathing cadence and negative splits. 00:31 A mid-route segment shows a bridge crossing with steady footfall audio and light traffic in the background. 00:39 The camera focuses on watch screens showing live pace, heart rate zones, and elapsed distance. 00:47 A hydration stop appears with quick high-fives and one runner adjusting a knee strap. 00:56 The lead runner calls the final kilometer and the pack increases tempo together. 01:04 Finish area clips show cooldown walking, stretching, and post-run coffee orders. 01:14 The creator records a close-up recap explaining turnout growth, consistency, and accountability in the club format. 01:26 The final frame shows a group photo and next-week meetup details.",
      "keySignals": ["group runs", "pace groups", "recovery routine", "morning discipline", "community fitness"],
      "brandsMentioned": ["Nike", "Hoka", "On Running"],
      "topics": ["Fitness & Exercise", "Community Events", "Lifestyle Content"],
      "language": "English",
      "hasVoiceover": true
    }
  }
}

Why it wins

A search API designed for interpretation, not cleanup

Most social listening outputs still leave teams doing manual decoding. This layer is built to reduce that translation work.

Structured enrichment

Query by products, ingredients, brands, and other extracted entities instead of reading every post manually.

Signal over noise

Focus on viral movement and visible momentum instead of generic mention counts.

Faster downstream analysis

Hand cleaner data to insight, R&D, or strategy workflows without rebuilding the same context each time.

Feed examples

Example search outputs for real consumer intelligence work

Explore the kinds of structured outputs teams use to track velocity, compare concepts, and monitor brand presence.

MenuData feed 1

Top viral food posts

Highest-momentum food posts in the selected period, ranked by viral velocity.

Sample output

{
  "period": "last_24h",
  "totalPostsInPeriod": 912,
  "posts": [
    {
      "post_url": "https://instagram.com/reel/...",
      "posted_at": "2026-03-04T13:20:00Z",
      "velocity_score": 9.3,
      "engagement": { "likes": 128400, "comments": 4120 },
      "ai_enrichment": {
        "contentSummary": "Creator compares premium pistachio bars.",
        "whatHappensInVideo": "00:00 lineup ... 00:22 blind taste ...",
        "keySignals": ["pistachio", "taste test"],
        "brandsMentioned": ["Lindt"],
        "topics": ["Food & Beverage"],
        "language": "English",
        "hasVoiceover": true
      }
    },
    {
      "post_url": "https://instagram.com/reel/...",
      "posted_at": "2026-03-04T11:05:00Z",
      "velocity_score": 8.7,
      "engagement": { "likes": 91300, "comments": 2760 },
      "ai_enrichment": {
        "contentSummary": "Protein dessert meal-prep test.",
        "whatHappensInVideo": "00:00 prep ... 00:19 macro breakdown ...",
        "keySignals": ["high-protein dessert", "meal prep"],
        "brandsMentioned": ["Fairlife"],
        "topics": ["Nutrition"],
        "language": "English",
        "hasVoiceover": true
      }
    },
    "..."
  ],
  "morePostsInPeriod": 910
}

MenuData feed 2

Recent viral food posts by flavor

Flavor-level growth view with recent viral posts and AI enrichment.

Sample output

{
  "period": "last_7d",
  "flavors": [
    {
      "flavor": "Pistachio",
      "viralPostsInPeriod": 42,
      "growthVsPriorPeriodPct": 146,
      "avgVelocityScore": 8.4,
      "recentPosts": [
        {
          "post_url": "https://instagram.com/reel/...",
          "posted_at": "2026-03-03T19:02:00Z",
          "velocity_score": 8.9,
          "engagement": { "likes": 68420, "comments": 1810 },
          "ai_enrichment": {
            "contentSummary": "Flavor ranking and texture comparison.",
            "whatHappensInVideo": "00:00 intro ... 00:16 ranking ...",
            "keySignals": ["kataifi", "pistachio cream"],
            "brandsMentioned": ["Patchi"],
            "topics": ["Consumer Reviews"],
            "language": "English",
            "hasVoiceover": true
          }
        },
        {
          "post_url": "https://instagram.com/reel/...",
          "posted_at": "2026-03-03T15:41:00Z",
          "velocity_score": 8.1,
          "engagement": { "likes": 51210, "comments": 1210 },
          "ai_enrichment": {
            "contentSummary": "Cafe menu flavor launch reaction.",
            "whatHappensInVideo": "00:00 first bite ... 00:14 crowd reaction ...",
            "keySignals": ["menu launch", "dessert special"],
            "brandsMentioned": ["% Arabica"],
            "topics": ["Cafe Trends"],
            "language": "English",
            "hasVoiceover": true
          }
        },
      "..."
      ]
    }
  ]
}

MenuData feed 3

Recent posts by brands

Recent brand post activity for the selected period with consistent AI enrichment.

Sample output

{
  "period": "last_24h",
  "brands": [
    {
      "brand": "Trader Joe's",
      "postsInPeriod": 9,
      "viralPostsInPeriod": 2,
      "recentPosts": [
        {
          "post_url": "https://instagram.com/reel/...",
          "posted_at": "2026-03-04T09:11:00Z",
          "velocity_score": 8.1,
          "engagement": { "likes": 35200, "comments": 980 },
          "ai_enrichment": {
            "contentSummary": "Limited snack drop post performance.",
            "whatHappensInVideo": "00:00 shelf reveal ... 00:18 tasting ...",
            "keySignals": ["limited drop", "snack review"],
            "brandsMentioned": ["Trader Joe's"],
            "topics": ["Retail Food"],
            "language": "English",
            "hasVoiceover": true
          }
        },
        {
          "post_url": "https://instagram.com/reel/...",
          "posted_at": "2026-03-04T07:52:00Z",
          "velocity_score": 7.6,
          "engagement": { "likes": 24100, "comments": 640 },
          "ai_enrichment": {
            "contentSummary": "Quick recipe using branded ingredients.",
            "whatHappensInVideo": "00:00 ingredients ... 00:20 final plate ...",
            "keySignals": ["quick recipe", "ingredient spotlight"],
            "brandsMentioned": ["Trader Joe's"],
            "topics": ["Recipe Content"],
            "language": "English",
            "hasVoiceover": true
          }
        },
      "..."
      ]
    }
  ]
}

Next step

See how the search API fits your research workflow

If you need faster visibility into what consumers are actually reacting to, this is easier to evaluate with a live walkthrough than a static spec sheet.

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