Google Cloud Commercial and Professional Services France

Cogedis

Enterprise Knowledge Search · IT Operations Automation · System Migration

Cogedis needed to replace unreliable on-premise infrastructure and outage-prone API management, lower infrastructure spend, and create enough operational flexibility to pursue AI services for its accounting and advisory customers.

66.6% reduction in infrastructure costs
Gemini Code Assist reviews 90% of the OPS code related to DevOps adoption before it reaches production

Reported impact

Business outcome

Cogedis cut infrastructure costs sharply, stabilized critical API flows, and freed technical capacity to build new data-driven and AI-assisted advisory services for its 20,000 customers.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Cogedis pursued a DevOps-led migration to Google Cloud that modernized both its runtime platform and its path to AI services. Cloud SQL gave the team on-demand database and virtual-machine flexibility so environments could be provisioned only when needed rather than permanently overprovisioned. Apigee replaced a fragile legacy API manager that had been failing several times per week, stabilizing document-scanning and other customer-facing integrations. Pub/Sub and Cloud Run support near-real-time data transformation and visualization pipelines, while BigQuery is being explored for predictive profitability recommendations to customers such as farmers deciding when to sell crops. On the engineering side, Gemini Code Assist reviews most operations code before production, and Gemini Enterprise Agent Platform underpins an AI legal-assistant initiative. Together, these services turned reliability improvements and cost control into a base for ongoing automation and advisory innovation.

Data flow

Operational and customer data moves through Pub/Sub and Cloud Run for near-real-time transformation, with APIs exposed through Apigee and persisted in Cloud SQL. BigQuery is used for predictive analytics, while Gemini-based developer and legal-assistant workflows sit on top of the modernized platform.

Solution architecture

7 components · 5 layers
  1. Ingest
    • Pub/Sub Carries events for near-real-time data transformations and downstream visualizations.
  2. Compute
    • Cloud Run Executes scalable services for transformation and visualization workloads.
  3. Storage
    • Cloud SQL Provides elastic database capacity so Cogedis can provision and retire environments as jobs require instead of overbuilding infrastructure.
    • BigQuery Supports predictive analytics work such as profitability forecasting for customers.
  4. Serving
    • Apigee Manages customer-facing APIs reliably after frequent outages with Cogedis's previous API manager.
    • Gemini Code Assist Reviews most operations code before production as part of Cogedis's DevOps adoption.
  5. Orchestration
    • Gemini Enterprise Agent Platform Supports Cogedis's customer-facing AI legal-assistant initiative.

Architecture clues

  • Apigee replaced an API manager that could go down three or four times a week
  • BigQuery is being explored for customer profitability predictions
  • Cloud SQL lets the company spin up database solutions or virtual machines as needed
  • Pub/Sub and Cloud Run support near-real-time visualization and transformation

Evidence from the source

Cogedis is currently exploring how to use BigQuery to provide customers with data-driven predictions
Gemini Code Assist is integrated into all IDEs, and it reviews 90% of the OPS code related to DevOps adoption before it reaches production
Now, with Apigee, we have a reliable, scalable solution, meaning our customers enjoy smoother data streams without interruptions.
This has allowed the company to avoid over-provisioning and has reduced infrastructure costs by 66.6%
With Cloud SQL, Cogedis can spin up virtual machines or database solutions as needed and shut them down when a job is complete
Transparency Evidence & methodology 95/100 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 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
    2 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
    5 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. Google Cloud

    Cogedis customer story

    First-party
    https://cloud.google.com/customers/cogedis
    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 10, 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 Jul 30, 2026 · Google Cloud · 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.