Databricks Software / SaaS Global

Freshworks

Customer Service Automation · Data Lake / Data Warehouse Modernization · Real-time Analytics

Saddled by a legacy Hadoop infrastructure built on Cloudera and an assortment of data tools, the company quickly realized it needed to modernize its infrastructure, which would help it use machine learning to introduce new ways to boost help desk productivity and customer satisfaction.

4x Faster predictions for triaging help desk tickets
500+ TB And 40+ data sources and tools migrated, across 750+ users
75% Reduction in data platform maintenance costs
With 500+ TB of data and 40+ data sources and tools across multiple clouds to move, Freshworks knew the migration wasn’t going to be easy, but the whole process was achieved within seven months.

Reported impact

Business outcome

Freshworks achieved measurable business impact on Databricks, including 500+ TB And 40+ data sources and tools migrated, across 750+ users; 75% Reduction in data platform maintenance costs; 4x Faster predictions for triaging help desk tickets.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Whether you’re in customer service, sales or marketing, your end goal is the same: delight your customers with outstanding products and service. Freshworks makes this possible with a wide range of CRM and customer experience solutions. As its customer base continued to grow, so did the pressures to improve the performance of its customer support organization. Saddled by a legacy Hadoop infrastructure built on Cloudera and an assortment of data tools, the company quickly realized it needed to modernize its infrastructure, which would help it use machine learning to introduce new ways to boost help desk productivity and customer satisfaction. After migrating to the Databricks Data + AI Platform, it was able to predictively triage help desk tickets in an automated manner, greatly increasing ticket resolution speeds, which directly impacts customer satisfaction.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed into curated datasets stored in Delta Lake before being served through Databricks SQL

Solution architecture

2 components · 2 layers
  1. Storage
    • Delta Lake Stores Freshworks's curated datasets for analytics, machine learning, and operational use cases.
  2. Serving
    • Databricks SQL Serves dashboards, interactive analysis, and governed SQL workloads for business users at Freshworks.

Architecture clues

  • Cloud: AWS
  • Key product: Databricks SQL
  • Key product: Delta Lake

Evidence from the source

4x Faster predictions for triaging help desk tickets
500+ TB And 40+ data sources and tools migrated, across 750+ users
75% Reduction in data platform maintenance costs
Whether you’re in customer service, sales or marketing, your end goal is the same: delight your customers with outstanding products and service.
With 500+ TB of data and 40+ data sources and tools across multiple clouds to move, Freshworks knew the migration wasn’t going to be easy, but the whole process was achieved within seven months.
Transparency Evidence & methodology 90/100 reference value · 92% confidence · 2 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 value90/100
  • 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
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 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

    Freshworks customer story

    First-party
    https://www.databricks.com/customers/freshworks
    Source type
    Vendor-published customer story
    Published
    Feb 22, 2023
    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. Stock Analysis

    Freshworks (FRSH) Number of Employees

    Independent secondary
    https://stockanalysis.com/stocks/frsh/employees/

    Freshworks had 4,500 employees as of December 31, 2025.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • Published on source page Feb 22, 2023 · Databricks · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.