Code.B AI Planner: scaling efficiency with generative AI and cloud architecture
ST IT CloudFebruary 27, 20266 min read
Generative AI applied to planning is radically transforming how companies structure campaigns, allocate budgets, and make data-driven decisions.
In a scenario where marketing, media, and sales operate across multiple channels simultaneously, planning has stopped being a manual and fragmented process.
Today, this dynamic has evolved into an intelligent, automated, and highly scalable flow, guided by integrated data.
It is in this context that the Code.B AI Planner emerges — a solution developed by Code.B in partnership with ST IT Cloud and supported on AWS (Amazon Web Services) infrastructure.
The objective is clear: integrate CRM, media, and e-commerce data, process it in a serverless architecture based on AWS, and generate optimized plans in minutes, drastically reducing operational time that previously took days.
More than automation, this is a structural change in how planning is done.
The new paradigm: Generative AI applied to strategic planning
Traditionally, campaign planning requires consolidating data from multiple sources, statistical analyses, simulations, and manual validations.
This process is susceptible to errors, rework, and human limitations in analyzing large volumes of information.
With Generative AI applied to planning, the model evolves. Technology begins to:
Analyze large volumes of historical and behavioral data;
Identify conversion patterns and seasonality;
Simulate different investment scenarios;
Generate strategic recommendations based on real context.
According to the State of AI in the Enterprise research by Deloitte, about two-thirds of organizations already report concrete efficiency and productivity gains from AI initiatives.
This data shows that artificial intelligence has stopped being an isolated experiment and started generating measurable impact in strategic and analytical business areas.
However, for these gains to be real, robust, scalable, and secure architecture is indispensable — and this is exactly where ST IT Cloud acts as a strategic partner.
Data integration: CRM + media + e-commerce as the intelligence foundation
No generative model delivers value if data is fragmented. Therefore, the technical differentiator of Code.B AI Planner begins in data integration (CRM + media + e-commerce).
Why is this integration critical?
CRM provides relationship history, customer profiles, and LTV.
Media platforms offer investment metrics, CPC, CPA, and conversions.
E-commerce brings real transactional data, average ticket, and purchase behavior.
By unifying these sources into automated pipelines, the solution creates a 360° view of the funnel, from click to conversion and retention.
This consolidation allows AI not only to generate plans based on isolated metrics, but on real business impact.
Furthermore, integration eliminates operational silos and reduces data inconsistencies, increasing the reliability of generated recommendations.
AWS-based serverless architecture: scalability with governance
The technical backbone of Code.B AI Planner is an AWS-based serverless architecture, designed to support intensive data processing with high elasticity.
Among the main architectural components are:
AWS Bedrock for Generative AI
Amazon Bedrock enables access to foundational AI models through managed APIs, ensuring:
Corporate security;
Data isolation;
Access control via IAM;
Automatic scalability.
This way, generative AI operates on structured data, maintaining governance and compliance.
Serverless services
The architecture includes services such as:
AWS Lambda for on-demand processing;
Amazon S3 for scalable storage;
AWS Glue for automated ETL;
Amazon Athena for analytical queries;
Amazon API Gateway for secure integration.
As a result, there is no need for manual server provisioning.
Infrastructure scales automatically according to demand, reducing fixed costs and increasing operational efficiency.
From days to minutes: operational time reduction with AI
One of the most relevant impacts of the solution is operational time reduction.
Processes that previously involved:
Manual data extraction;
Spreadsheet consolidation;
Manual statistical modeling;
Iterative reviews.
Are now executed automatically.
In a case published by AWS Brazil, Code.B reported an increase of up to 80% in Google Ads campaign planning efficiency using AI.
This gain is directly related to the automation of data analysis and strategic insight generation.
In practice, this means:
Plans that took days are now generated in minutes;
Greater frequency of strategic reviews;
Near real-time adjustments;
Better exploitation of opportunity windows.
Consequently, teams stop acting as spreadsheet operators and begin acting as data-driven strategists.
Scalability and operational time reduction as competitive advantage
Beyond the immediate productivity gain, the combination of scalability and operational time reduction creates a long-term strategic effect.
First, scalability allows the model to be applied to multiple accounts, regions, and product categories without a proportional increase in operational cost.
Second, automation frees teams to focus on creativity, experimentation, and growth.
Finally, the ability to quickly simulate scenarios increases strategic agility — a critical factor in highly dynamic digital markets.
Companies that operate with shorter decision cycles tend to capture opportunities before competitors, elevating ROI and media efficiency.
Security, governance, and data compliance
When handling sensitive CRM and e-commerce transaction data, security is not optional.
The architecture implemented by ST IT Cloud incorporates:
Encryption at rest and in transit;
Granular access control (IAM);
Monitoring via AWS CloudWatch;
Event auditing with AWS CloudTrail.
Furthermore, environment segmentation ensures isolation between operational data and AI models.
This approach ensures compliance with regulatory requirements and corporate governance best practices.
Generative AI applied to planning: technical maturity and ROI
Adopting generative AI without adequate architecture can generate isolated, unscalable experiments. However, when combined with a structured cloud foundation, technology becomes a strategic asset.
The technical maturity of Code.B AI Planner lies precisely in the convergence between:
With this, the solution evolves with the business.
Furthermore, ROI measurement becomes more precise, as decisions are based on integrated data rather than isolated metrics.
ST IT Cloud, Code.B, and AWS: strategic partnership for scalable innovation
The joint campaign between ST IT Cloud, Code.B, and AWS represents more than a technology integration — but a value architecture.
Code.B brings expertise in performance and campaign modeling. AWS offers scalable and secure infrastructure. ST IT Cloud connects strategy, data architecture, and governance.
This convergence ensures that Generative AI applied to planning is not just a conceptual promise, but an implementable, scalable solution oriented toward real results.
The future of planning is intelligent, integrated, and scalable
The evolution of data-driven marketing requires more than dashboards. It requires intelligence capable of interpreting context, crossing variables, and proposing strategic actions.
In this scenario, Code.B AI Planner demonstrates how the combination of generative AI and cloud architecture can redefine operational efficiency.
Companies that adopt this model not only reduce time and cost. They gain strategic agility, predictability, and the capacity to scale safely.
The question, therefore, is not whether generative AI will be part of corporate planning, but when and with what level of architectural maturity.
And for organizations that want to accelerate this journey with a solid technical foundation, ST IT Cloud acts as a strategic partner in building this new era of intelligent planning.
All of this with end-to-end scalability, governance, and automation, in partnership with the AWS ecosystem.
