Databricks Software / SaaS United States

Aktify

Customer Service Automation · Enterprise Knowledge Search

Aktify aims to help its clients convert their customers through conversational AI. Clients can use a conversational AI agent to conduct thousands of SMS conversations with sales prospects at the same time.

6-figure Annual savings in IT head count costs
80% Reduction in data engineering hours, reducing costs
95% Reduction in time to onboard new employees
With Aktify’s solutions, the average customer generates $227,000 in revenue per year and achieves a 19x ROI.

Reported impact

Business outcome

Reported outcomes include 80% Reduction in data engineering hours, reducing costs; 95% Reduction in time to onboard new employees; 6-figure Annual savings in IT head count costs.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth

Solution

Aktify built its Databricks implementation around Databricks SQL, Delta Lake, More from, Aktify. Aktify aims to help its clients convert their customers through conversational AI. Clients can use a conversational AI agent to conduct thousands of SMS conversations with sales prospects at the same time. Databricks SQL serves governed analytics, dashboards, and ad hoc queries for business users and operational teams. Delta Lake stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning. More from more from is part of the named implementation described in the customer story. Aktify aktify is part of the named implementation described in the customer story. Reported outcomes include 80% Reduction in data engineering hours, reducing costs; 95% Reduction in time to onboard new employees; 6-figure Annual savings in IT head count costs.

Data flow

Source data is ingested into databricks, curated in Delta Lake 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. Storage
    • Delta Lake Stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning.
  2. Serving
    • Databricks SQL Serves governed analytics, dashboards, and ad hoc queries for business users and operational teams.
  3. Orchestration
    • dbt Defines tested SQL transformations and documentation around analytics models.
  4. Uncategorized
    • More from More from is part of the named implementation described in the customer story.
    • Aktify Aktify is part of the named implementation described in the customer story.
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment.

Architecture clues

  • Aktify
  • Annual savings in IT head count costs
  • Complex data dependencies prevent data democratization
  • Databricks SQL
  • Delta Lake
  • More from

Evidence from the source

80% Reduction in data engineering hours, reducing costs
95% Reduction in time to onboard new employees
“A typical use case for our customers is to look at the data points in our software and wonder when and why their customers and prospects are dropping off their chat funnel,” said Brandon Smith, Director of Data and Analytics at Aktify. “My
“Data transformations are no longer a scary thing for Aktify,” Smith said. “From day one, we can let new employees loose on our data without wrapping a lot of red tape around their hands.
“I knew if we used SQL Server Integration Services (SSIS), everyone would be afraid to work with data in production because it would probably break something downstream,” Smith explained. “We would probably have people complaining that we b
“With everything I have going on in Databricks and dbt, the time travel features in Delta Lake enable me to stop after making changes and see what actually changed,” Smith explained. “If anomalies surface, I can go back and spot when I intr
Transparency Evidence & methodology 95/100 reference value · 94% 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 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • 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 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

    Aktify customer story

    First-party
    https://www.databricks.com/customers/aktify
    Source type
    Vendor-published customer story
    Published
    Feb 6, 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. Aktify

    Aktify | AI-Powered Sales Outreach & Lead Follow-Up

    First-party
    https://aktify.com/

    The official page JSON-LD lists addressLocality Spanish Fork, addressRegion UT, addressCountry US, and numberOfEmployees minValue 11 and maxValue 50.

    Source type
    Official company website
    Supports
    • Region
    • Company size

Date evidence

  • Published on source page Feb 6, 2023 · Databricks · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.