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
Enables continuous monitoring for granular due diligence
Aldecis uses BigQuery ML and Google AI to automate performance drift detection, increasing margins for multinational CFOs.
Aldecis uses BigQuery ML and Google AI to automate performance drift detection, increasing margins for multinational CFOs.
Building a scalable cloud foundation
to meet growing operational demand
Every monthly financial closing, business growth and operations managers are required to crunch huge volumes of performance data to try to identify where profit and margin drops or unexpected shifts occurred.
While traditional business intelligence tools excel at only generating high-level dashboards, their top-down approach obscures daily operational reality.
DeepDetect empowers CFOs and financial planning analysts to turn heavy financial reporting into targeted business actions. Native to Google Cloud, our high-scale engine processes billions of transactions in real time - tracking revenue, cost, and headcount variances, so managers know exactly where to act to optimize margins and drive performance.
Arthur F.
Senior Finance Data Scientist, Aldecis
Regional challenges and opportunities mathematically compensate each other out at a consolidated level, leaving important operational changes hidden during corporate review.
Aldecis R&D, experts in performance and business intelligence applications, founded by veteran CFOs, leaders, and corporate auditors, designed its DeepDetect AI application to solve this bottleneck with a disruptive bottom-up methodology. Instead of wasting finance teams’ time having to manually search through hundreds of pages of dashboards, the application automates the detection of all performance drifts and variances throughout the entire business structure, saving up to 40% of managers’ closing time.
Originally, Aldecis operated its application inside its own secured data center. However, as more operational managers used DeepDetect, the company needed to move to the cloud to scale with growing demand. With most of its international clients already running on Google Cloud, Aldecis selected the platform for its high-performance data analytics tools and built-in security compliance, which allowed the company to interact smoothly, efficiently, and securely with its clients’ systems.
“Deploying on Google Cloud natively aligns DeepDetect with clients’ existing cloud setups without any modification, while providing the flexible computing infrastructure needed to smoothly process billions of transactions,” explains Quentin T, performance data and FP&A solutions director at Aldecis.
Protecting and enhancing margins with DeepDetect
To bring its cloud deployment to life, Aldecis collaborated closely with Google Cloud architects and Google Cloud partner Aceo Tech to evaluate and deploy its systems efficiently. Today, DeepDetect uses BigQuery and Gemini Enterprise Agent Platform to transform finance business partners’ operations by turning complex financial datasets into immediate actionable operational levers that anticipate any impact on profits and loss, guaranteeing a return on investment (ROI) in less than three months.
Previously, finance and sales teams would spend around 40% of their time manually gathering and compiling performance data. DeepDetect replaces this manual data gathering with the automated extraction of financial records using BigQuery. This allows teams of analysts to shift their focus so they can spend all their time making decisions that anticipate profit margins. When unexpected variations in cost or sales occur, DeepDetect uses Gemini to justify complex statistical variances into clear, plain-language investigation paths. This gives financial planning and analysis and sales growth analysts prioritized steps they can take to stop hidden margin erosion or monitor growth traction.
DeepDetect delivers a return on investment in under three months with smooth API deployment on Google Cloud. By building exclusively on externally audited, tax-validated financial data within BigQuery, it ensures high data quality and consistency, complemented by a proprietary data-cleansing module to generate the most accurate performance drift detection and monitoring.
Benjamin Dez-Perrard
Senior Finance Transformation Officer, Aldecis
With BigQuery ML, Aldecis builds and runs hundreds of predictive models that evaluate revenue trends, seasonal changes, and financial variations. These models analyze historical data to outline clear upcoming business risks and opportunities, helping managers adjust their budgets and action plans before an impact on their business units. Orchestrated through Workflows and running on a serverless architecture with Cloud Run, this processing pipeline evaluates more than six billion historical financial records in less than two hours, delivering trustworthy root-cause calculations in less than a minute.
This speed is powered by a hybrid AI architecture designed to reduce costs and maintain security. DeepDetect eliminates cloud data movement fees by running its calculations natively inside the client's existing cloud perimeter. Enabled by secure configurations in Google Cloud Identity and Access Management, the application runs directly where the client's data resides, ensuring sensitive corporate records never leave their secure network.
For enterprise-scale operations, the application uses the powerful parallel processing capabilities of BigQuery ML for high-performance variance detection without adding extra infrastructure overhead. Finally, to meet strict regulatory standards, the system is audit-ready by design, combining deterministic mathematical calculation engines with Gemini to help ensure trustworthy AI compliance with the EU AI Act and ISO 42001. This provides a fully justified trail for every performance insight.
“Previously, bottom-up analysis was an economic non-starter, requiring ten times as many analysts with no guaranteed return on investment,” explains David D, FMCG consultant FP&A at Aldecis. “Now with massive parallel computing on Google Cloud, we run the deepest granularity analysis to surface highly operational insights hidden within local business units.”
Transforming business performance analysts into proactive business drivers with BigQuery
By empowering central performance teams to shift from passive reporting exploration to active decision intelligence, Aldecis transforms how its clients manage business and financial performance. In global firms, local subsidiaries often possess the most accurate insights into market shifts and cost pressures.
However, traditional reporting cycles delay these insights, meaning headquarters often only see the data months after margin erosion has occurred. Using real-time analytics in BigQuery, Aldecis provides a single source of truth across all business units. This allows headquarters to empower local teams with autonomy while maintaining continuous monitoring. Instead of relying on static, end-of-month reconciliations, large enterprise users gain global and operational margin visibility on the first hours during a closing cycle, rather than spending a full week gathering and exploring reports.
This synchronized model relies on anticipatory action. Building predictive models with BigQuery ML accelerates performance data analysis by 85%. Manual data processing tasks that traditionally took three to five days now take less than two hours, saving corporate analysts up to 40% of their time. Because the system uses deterministic computation rules within BigQuery rather than running AI models continuously, it also reduces cloud computing costs by 90% to 95% compared to LLM and agentic AI platforms. By using automated anomaly detection directly where the data resides, operational challenges—such as an unexpected surge in regional logistics costs, unintended price discounts, or local pricing slippage—are flagged the moment they deviate from historical performance and budgets.
Ultimately, this approach transforms the finance function from a historical recorder of performance health into an active driver of profitability. Leadership can collaborate with local management to execute targeted corrective action plans, mitigating risks before performance drifts affect the bottom line.
Looking ahead, Aldecis plans to introduce two new complementary applications, DeepRoot and DeepVein, to safely converse with business users and challenge managers on their chosen action strategies. Additionally, the company aims to empower even more enterprise clients with actionable insights by making the DeepDetect application available on Google Cloud Marketplace.
“By combining scalable infrastructure with advanced machine learning capabilities, Google Cloud has allowed us to deliver the business intelligence corporate clients need to protect their performance for the long term,” concludes Benjamin Dez-Perrard, senior finance transformation officer at Aldecis.
By combining scalable infrastructure with advanced machine learning capabilities, Google Cloud has allowed us to deliver the business intelligence corporate clients need to protect their performance for the long term.
Benjamin Dez-Perrard
Senior Finance Transformation Officer, Aldecis
Founded by veteran CFOs and auditors, Aldecis transforms decision intelligence through advanced analytics—delivering automated, standardized, and scalable performance-drift detection powered by its trusted AI application.
Industries: Technology, Professional Services
Location: France
Products: Google Cloud, BigQuery, BigQuery ML, Cloud Run, Workflows, Gemini, Gemini Enterprise Agent Platform, Identity and Access Management
About Google Cloud partner – Aceo Tech
Aceo Tech is a Google Cloud partner specializing in cloud architecture and technical optimization, providing enterprise-grade technical evaluation and system deployment services.
