Confluent Logistics United States and Canada

ACERTUS

Application Refactoring · Supply Chain Optimization

ACERTUS needed to integrate multiple monolithic business units and eliminate slow manual handoffs so vehicle transport, title, storage, and customer updates could happen in real time instead of through fragmented, error-prone processes.

5,000 schemas
50+ people
66 hub and prep locations
all 50 states

Reported impact

Business outcome

ACERTUS eliminated logistics bottlenecks, cut manual reporting from days to minutes, delivered real-time customer and partner updates, and unlocked new revenue while reducing infrastructure labor and errors.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

ACERTUS modernized its logistics architecture around Confluent Cloud and an event-driven microservices model that connects transportation management, title and registration, car haul, and related systems through one shared streaming backbone. The company uses pre-built connectors plus Schema Registry to exchange events with partners and line-of-business applications, while SQL Server and PostgreSQL CDC source connectors, the Snowflake sink connector, and ksqlDB help move, transform, and land data across operational and analytics environments. Stream Lineage gives teams visibility into how data moves through the platform, and Kafka Streams applications running in containers on AWS Fargate extend the real-time workflows. This setup replaced slow reports and manual coordination with instant event handling, which now powers external order integrations, duplicate-VIN checks, customer notifications, and internal AI-assisted decision support.

Data flow

Orders, vehicle, pricing, and title events stream through Confluent Cloud from systems such as SQL Server and PostgreSQL; ksqlDB and connectors transform and route them to partners, operational applications, notifications, and the Snowflake warehouse for analytics and AI-assisted decision support.

Solution architecture

5 components · 4 layers
  1. Ingest
    • Snowflake sink connector Moves logistics data into ACERTUS's data warehouse for analytics and modeling.
  2. Compute
    • ksqlDB Processes streaming data in flight for operational and analytical workflows.
  3. Governance
    • Schema Registry Governs evolving order and partner contracts without requiring code changes in connected systems.
    • Stream Lineage Visualizes how ACERTUS events are produced, consumed, and transformed across the platform.
  4. Uncategorized
    • Confluent Cloud Acts as the central streaming backbone across ACERTUS's transportation, title, and logistics businesses.

Architecture clues

  • Kafka Streams applications run as Docker containers on AWS Fargate through ACERTUS's AWS Marketplace integration.
  • Real-time partner integrations include CarGurus orders and evolving order contracts managed through Schema Registry.
  • The platform combines SQL Server and PostgreSQL CDC source connectors with the Snowflake sink connector.

Evidence from the source

"Confluent enabled ACERTUS to shift from a monolithic to event-driven microservices architecture."
"Confluent helps ACERTUS build streaming data pipelines using SQL Server and PostgreSQL CDC source connectors, and the Snowflake sink connector, along with ksqlDB for processing data in flight."
"Confluent serves as a central nervous system connecting core ACERTUS systems, including our transportation management, title and registration, and car haul systems."
"The solution we built with Confluent enabled us to lower costs, increase automation, eliminate errors, and open new business opportunities that are already generating more than $10 million in revenue in the first year."
Transparency Evidence & methodology 85/100 reference value · 79% confidence · 1 source

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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference 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. Confluent

    ACERTUS customer story

    First-party
    https://www.confluent.io/customers/acertus/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Oct 1, 2023 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Oct 11, 2026 · Confluent · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.