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Snowflake Commercial and Professional Services Downers Grove, Illinois

Ensono

IT Operations Automation · Predictive Maintenance

predict system failures and diagnose incidents from millions of events and alerts before outages affect client operations.

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30% reduction in major incidents
38% improvement in SLA performance
54-70% Lower mean time to resolution (MTTR)
< 2 Minutes to generate a comprehensive AI analysis for incident diagnosis

Reported impact

Business outcome

Ensono reduced major incidents by 30%, improved SLA performance by 38%, cut mean time to resolution by 54-70%, and can generate an AI diagnosis in under two minutes.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Ensono uses Snowflake and Streamlit in Snowflake on Snowflake to predict system failures and diagnose incidents from millions of events and alerts before outages affect client operations. Operational events and alerts from Ensono-managed systems are centralized in Snowflake so predictive models can detect risk patterns earlier. The Envision Predictive Engine scores those signals to identify high-risk conditions, while the DiagnoseNow application presents AI-assisted root-cause analysis and incident context to responders. Frontline operations teams use the Streamlit interface to review diagnoses quickly, cut resolution time, and prevent outages before they impact client environments. Keeping ingestion, storage, transformation, and governed access on one platform reduces handoffs, improves concurrency, and makes it easier to reuse trusted data across teams and applications. Ensono reduced major incidents by 30%, improved SLA performance by 38%, cut mean time to resolution by 54-70%,...

Data flow

infrastructure events and alert streams flow into Snowflake and Streamlit in Snowflake; curated data and model outputs are then served to operations responders and incident-management teams.

Solution architecture

2 components · 2 layers
  1. Storage
    • Snowflake Provides the centralized data platform that stores governed datasets and serves analytics or AI workloads.
  2. Serving
    • Streamlit in Snowflake Delivers interactive applications and agent interfaces on top of Snowflake data.

Architecture clues

  • DiagnoseNow is built with Streamlit in Snowflake for frontline incident response.
  • EPE analyzes millions of events and alerts to flag high-risk situations.
  • Uses shared warehousing scale to absorb demand surges with little manual intervention.

Evidence from the source

54-70% Lower mean time to resolution (MTTR)
DiagnoseNow, an application built with Streamlit in Snowflake
EPE, Ensono’s award-winning ML-powered solution, automatically flags high-risk situations for faster resolution
reducing major incidents by 30% and improving SLA performance by 38%
Transparency Evidence & methodology 100/100 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 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    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. Snowflake

    Ensono customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/ensono/
    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 May 18, 2026 · 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 Aug 3, 2026 · Snowflake · 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.

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