Google Cloud Software / SaaS France

Aldecis

Real-time Analytics · Risk Management

Aldecis needed to automate bottom-up financial analysis because CFO and FP&A teams were spending days hunting through dashboards to find the operational causes of margin and profit drift.

85% faster financial data analysis with
BigQuery ML Reduces time-to-insight from 3–5 days to under two hours
Cuts computing infrastructure costs by 90% to 95%
Eliminates the ~40% of time spent on data exploration

Reported impact

Business outcome

Aldecis reported Results on Google Cloud 85% faster financial data analysis with, BigQuery ML Reduces time-to-insight from 3–5 days to under two hours, and Cuts computing infrastructure costs by 90% to 95%, improving P&L drift detection and financial anomaly analysis with measurable gains in cost reduction, productivity improvement, risk reduction, and speed to market.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

Aldecis uses BigQuery, BigQuery ML, Gemini, and Cloud Run to support P&L drift detection and financial anomaly analysis. Aldecis needed to automate bottom-up financial analysis because CFO and FP&A teams were spending days hunting through dashboards to find the operational causes of margin and profit drift. In practice, the implementation stores or organizes core datasets in BigQuery, governs access and lineage with Google Cloud Identity and Access Management, coordinates repeatable workflows with Cloud Run, and runs transformation or AI logic on BigQuery ML. Streaming operational telemetry and business events are consolidated into BigQuery; pipeline execution is coordinated with Cloud Run; BigQuery ML applies analytics or model logic. The case study reports Results on Google Cloud 85% faster financial data analysis with, BigQuery ML Reduces time-to-insight from 3–5 days to under two hours, and Cuts computing infrastructure costs by 90% to 95%, showing measurable improvement after...

Data flow

Streaming operational telemetry and business events are consolidated into BigQuery; pipeline execution is coordinated with Cloud Run; BigQuery ML applies analytics or model logic.

Solution architecture

6 components · 4 layers
  1. Compute
    • BigQuery ML Supports model development, tuning, evaluation, or managed generative ai inference
    • Gemini Provides the language or embedding model used for reasoning, retrieval, or response generation
  2. Storage
    • BigQuery Stores or organizes the core datasets that feed downstream analytics and ai workflows
  3. Orchestration
    • Cloud Run Runs scalable cloud-native application services that package the workflow
    • Workflows Orchestrates batch and streaming pipelines that keep the solution current
  4. Governance
    • Google Cloud Identity and Access Management Applies identity, policy, and domain governance controls around the solution

Architecture clues

  • BigQuery ML Reduces time-to-insight from 3–5 days to under two hours
  • The platform is designed for near-real-time or streaming analysis rather than batch-only reporting.
  • Uses serverless or elastic compute so capacity can scale up and down with workload demand.

Evidence from the source

85% faster financial data analysis with
BigQuery ML Reduces time-to-insight from 3–5 days to under two hours
Cuts computing infrastructure costs by 90% to 95%
Eliminates the ~40% of time spent on data exploration
Results on Google Cloud 85% faster financial data analysis with
Transparency Evidence & methodology 95/100 reference value · 84% confidence · 3 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
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
  • 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
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency9/10
    3 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. Google Cloud

    Aldecis customer story

    First-party
    https://cloud.google.com/customers/aldecis
    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
  2. Aldecis

    Aldecis - Align Decisions, Insights into Actions

    First-party
    https://aldecis.com

    The official site lists 22 rue Bréguet, 75011 Paris.

    Source type
    Official company website
    Supports
    • Region
  3. RocketReach

    aldecis Management Team | Org Chart

    Independent secondary
    https://rocketreach.co/aldecis-management_b5c87812f42e352b

    The company profile states that aldecis employs 22 employees.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • First archived Sep 25, 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 Oct 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.