Databricks Financial Services Germany

Allianz Direct

Customer Service Automation · Enterprise Knowledge Search

Allianz Direct wanted to use generative AI to remove mundane contact-center tasks while keeping customer-service answers trustworthy, compliant and built on the same governed data platform the company already used.

10 to 15% uplift in accuracy
15% improved answer accuracy with agent assist tool

Reported impact

Business outcome

The new agent-assist workflow improved answer accuracy by 10 to 15%, built agent trust in the tool, reduced time spent searching across multiple systems and gave Allianz Direct a governed foundation for rolling out more GenAI use cases across contact centers.

  • Compliance
  • Customer Experience
  • Productivity Improvement

Solution

Allianz Direct launched its first generative-AI customer-service workflow on the Databricks Data + AI Platform so contact-center agents could spend less time hunting through policy terms and more time helping customers. The team built a RAG-based agent-assist application that loads Allianz Direct's publicly shareable terms and conditions, retrieves the right passages for each policy question and presents grounded answers to a human agent rather than directly to the caller. Agent Bricks Custom Agents provides the core GenAI capability, Databricks Notebooks make workflow changes easy for developers, and Unity Catalog keeps the broader company data estate securely governed as more teams adopt self-service AI. Because the application runs on the same data and AI foundation Allianz Direct already used, the team could prototype quickly, test with a subset of agents and expand from the initial proof of concept toward broader rollouts and additional GenAI use cases.

Data flow

Publicly shareable policy terms and conditions are retrieved into a RAG workflow on Databricks, where Agent Bricks Custom Agents generates grounded responses for human contact-center agents. Developers update the workflow in Databricks Notebooks, and governed platform access remains controlled through Unity Catalog as the use case scales.

Solution architecture

4 components · 3 layers
  1. Compute
    • Databricks Data + AI Platform Provides the shared data and AI environment where Allianz Direct builds and runs GenAI contact-center workflows.
  2. Governance
    • Unity Catalog Keeps Allianz Direct's broader data estate securely governed as self-service GenAI use grows.
  3. Uncategorized
    • Agent Bricks Custom Agents Powers the RAG-based agent-assist workflow that surfaces grounded answers to policy questions for human agents.
    • Databricks Notebook Makes workflow changes and proof-of-concept iteration simple for Allianz Direct developers.

Architecture clues

  • Responses are delivered to human agents instead of directly to customers.
  • The first use case is a RAG-based agent-assist tool over policy terms and conditions.
  • The platform choice was aided by lakehouse scalability and self-service data access governed through Unity Catalog.

Evidence from the source

Compared with the previous application, the Databricks Agent Bricks Custom Agents-powered use case demonstrated a 10 to 15% uplift in accuracy.
Thanks to Databricks, everyone in Allianz Direct has access to the data they need to do their job, securely governed via Unity Catalog.
The team built a proof of concept for a RAG-based agent-assist tool.
This application incorporated all of Allianz Direct products' terms and conditions, intelligently delivering the right information to the agent at the right time.
We don't want shorter calls, we're not trying to replace agents with automation — we're getting rid of the mundane stuff agents don't like doing so they can spend more time talking to customers.
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Databricks

    Allianz Direct customer story

    First-party
    https://www.databricks.com/customers/allianz-direct
    Source type
    Vendor-published customer story
    Published
    Aug 15, 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

Date evidence

  • Published on source page Aug 15, 2024 · Databricks · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.