Databricks Software / SaaS United States

MediaRadar/Vivvix

Customer Service Automation · Enterprise Knowledge Search

MediaRadar | Vivvix provides comprehensive advertising intelligence that empowers brands to understand and optimize their ad spending across all media channels. Initially, MediaRadar | Vivvix faced challenges with manual data processing and fragmented workflows that made it harder to support a rapid growth in ad volumes.

150% Increase in ads categorized per hour
2 days to 4 hours
2 days to 4 hours Time saved experimenting with Agent Bricks AI Playground
Serving clients better with a 150% increase in hourly throughput

Reported impact

Business outcome

Reported outcomes include 2 days to 4 hours Time saved experimenting with Agent Bricks AI Playground; 150% Increase in ads categorized per hour; 2 days to 4 hours.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

MediaRadar/Vivvix built its Databricks implementation around Agent Bricks, Databricks Data + AI Platform, Unity Catalog, Databricks Model Serving. MediaRadar | Vivvix provides comprehensive advertising intelligence that empowers brands to understand and optimize their ad spending across all media channels. Initially, MediaRadar | Vivvix faced challenges with manual data processing and fragmented workflows that made it harder to support a rapid growth in ad volumes. Agent Bricks provides the agentic and generative AI runtime used to build domain-specific applications. Databricks Data + AI Platform provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment. Unity Catalog applies centralized governance, lineage, auditing, and access control across shared data assets. Databricks Model Serving hosts production model endpoints or LLM interfaces used by applications and business users.

Data flow

Source data is ingested into databricks, curated in shared lakehouse tables, processed with Databricks compute and orchestration services, and served through analytics, semantic search, or model endpoints for downstream users and applications.

Solution architecture

6 components · 4 layers
  1. Compute
    • Agent Bricks Provides the agentic and generative AI runtime used to build domain-specific applications.
    • Structured Streaming Processes live streams continuously for low-latency analytics.
    • Spark Structured Streaming Processes live streams continuously for low-latency analytics.
  2. Serving
    • Databricks Model Serving Hosts production model endpoints or LLM interfaces used by applications and business users.
  3. Governance
    • Unity Catalog Applies centralized governance, lineage, auditing, and access control across shared data assets.
  4. Uncategorized
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment.

Architecture clues

  • Agent Bricks
  • Databricks Data + AI Platform
  • Databricks Model Serving
  • Increase in ads categorized per hour
  • Unity Catalog
  • structured streaming

Evidence from the source

150% Increase in ads categorized per hour
2 days to 4 hours Time saved experimenting with Agent Bricks AI Playground
MediaRadar | Vivvix, an advertising intelligence company, helps brand marketers and advertising agencies understand their competitive landscape and the impact of their media spends. Their primary mission is to discern “what, where and when”
Prior to Databricks, MediaRadar | Vivvix faced bottlenecks with their previous setup using Amazon Simple Queue Service (SQS), where manual polling for data and a limit of 10 messages at a time severely hindered the company’s ability to meet
The company’s ad classification process relied on manual labor. Hundreds of operators were tasked with watching ads and recording detailed information on their elements, which was incredibly time-consuming and could not keep pace with the r
Thierry Steenberghs, Principal Software Engineer at MediaRadar | Vivvix, highlighted the difficulties of managing the data and ensuring the smooth operation of their models. “One of the nightmares that we were having was the fragmented stru
Transparency Evidence & methodology 95/100 reference value · 95% confidence · 4 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    4 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Databricks

    MediaRadar/Vivvix customer story

    First-party
    https://www.databricks.com/customers/mediaradar-vivvix
    Source type
    Vendor-published customer story
    Published
    Jun 20, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Prospeo

    MediaRadar Overview, Address & Contact

    Independent secondary
    https://prospeo.io/c/mediaradar

    The embedded Organization data lists streetAddress "114 W 41st St, 8th Floor, New York, NY 10036, US", addressCountry "US", and numberOfEmployees value 501.

    Source type
    Structured profile
    Supports
    • Region
    • Company size
  3. MediaRadar

    Who We Are

    First-party
    https://www.mediaradar.com/who-we-are

    The page states, "We have over 500 employees in multiple offices in the U.S., Canada, and across the globe."

    Source type
    Official company website
    Supports
    • Company size
  4. MediaRadar

    Cookies & Privacy Notice

    First-party
    https://www.mediaradar.com/privacy

    The privacy notice directs requests to "Privacy Official, MediaRadar, 119 West 40th Street, 8th Floor, New York, NY 10018," placing the company in the United States.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Jun 20, 2024 · Databricks · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.