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AI Cockpit: AI-Driven Software Engineering Platform

AI Cockpit is a software engineering suite powered by Generative Artificial Intelligence that helps development teams accelerate the creation of digital products, software, and applications. The solution applies AI in all phases of the development lifecycle – from requirements gathering to deployment and maintenance – measuring the performance of each project and offering automation tools at every stage. In other words, it goes beyond the isolated use of models like ChatGPT: AI Cockpit orchestrates multiple AI tools and intelligent assistants to optimize the entire development process in an integrated way.

Dozens of companies from various sectors (such as retail, finance, manufacturing, and agriculture) already use AI Cockpit to boost their projects, proving its effectiveness in real-world scenarios. In addition to its own tools, the platform also connects to the best generative AI and automation tools available on the market, expanding its capabilities with the most advanced solutions in the ecosystem. All of this is made available in a unified interface – a “cockpit” – that provides full visibility of progress and allows for the agile identification of bottlenecks or opportunities for improvement.

AI Cockpit One​

AI Cockpit One is the main entry point for users to access AI agents, conversations, organizational settings, budgets, telemetry, and connected applications from a single workspace. It works by active organization, so menus, cards, reports, and administrative actions change according to the selected organization and the user's permissions.

Key capabilities:

AI Cockpit Lens​

AI Cockpit Lens is an application for organizing AI agents, knowledge bases, workflows, projects, and integrations in one workspace. Teams use it to create reusable agents, prepare document context, run guided workflow executions, and manage the resources available to each active organization.

Key capabilities:

  • Review the active organization's environment from Home.
  • Create, configure, and test agents.
  • Build knowledge bases with documents and project context.
  • Create and run workflows that combine agents, context, and delivery adapters.
  • Organize resources through projects.
  • Manage organization context, preferences, and integrations.

AI Cockpit Smart Engineering​

The Smart Engineering module focuses on accelerating the modernization of legacy systems and the automated understanding of complex codebases. It applies AI to analyze code in traditional languages (COBOL, Clipper, PHP, etc.), extract business rules, and generate detailed documentation, as well as proposing modernized versions in current languages and architectures.

Key capabilities:

  • Automated documentation of legacy systems.
  • Reverse engineering with generation of specifications in natural language.
  • AI-assisted refactoring for modern languages.
  • Increased team productivity with less manual effort.

AI Cockpit Reasoning​

The Reasoning module is the “brain” of AI Cockpit, concentrating Generative AI capabilities to support modernization, development, and testing activities. It functions as an intelligent assistant capable of understanding requests in natural language, accessing the project context, and generating artifacts in an automated and guided manner.

Key capabilities:

  • Code generation and review, including automated code review.
  • Creation of unit and integration tests.
  • Root cause analysis in failures and incidents.
  • Intelligent code documentation, explaining snippets in natural language.
  • Accelerated modernization and development, reducing time and cost by up to 80%.

AI Cockpit Intelligent Requirements​

AI Cockpit Intelligent Requirements helps product, business, and technology teams create, organize, review, and evolve requirements with AI support. It turns ideas, documents, business goals, and project context into backlog items ready for review, refinement, knowledge-base storage, or publication to providers such as Jira, BusinessMap, and Azure DevOps.

Key capabilities:

  • Organize requirements work through workspaces connected to projects, knowledge bases, and providers.
  • Use Lens knowledge bases as context for requirement generation.
  • Generate structured backlog items from scope, goals, problems, or supporting documentation.
  • Review and refine generated cards manually or with AI support.
  • Save generated requirements back to a knowledge base or publish approved items through provider adapters.

AI Cockpit Metrics​

The Metrics module provides real-time visibility into software engineering performance. It collects data from various sources (work management tool boards, Git repositories, development metrics, etc.) and presents dashboards to support strategic decisions.

Key capabilities:

  • Monitoring the efficiency of the development flow.
  • Team productivity metrics.
  • Software quality and product management indicators.
  • Quantification of the impact of using AI (e.g., acceleration of requirements, development, and tests).
  • Over 40 indicators to support continuous improvement and justify investments.

Third-Party Applications​

AI Cockpit integrates with specialized third-party platforms that extend its capabilities beyond the core modules. These applications are accessible through AI Cockpit One and complement the platform with focused solutions for AI adoption monitoring, cost analysis, and product team workflows.

AI Adoption​

AI Adoption is a monitoring and analysis application that helps organizations understand how AI is being used across repositories, contributors, and delivery activity. It combines version control data with telemetry information to provide a consolidated view of AI adoption.

Key capabilities:

  • Adoption indicators, charts, and contributor comparison tables.
  • Repository-level and file-level usage views.
  • Synchronization of version control data for selected periods.
  • Configuration of provider connections and identity mappings.

AI/R Watch​

AI/R Watch is a monitoring and analysis platform for AI investments and cloud costs. It gives engineering leaders, finance teams, and platform administrators a consolidated view of how much the organization is spending on AI tools, language model APIs, and related cloud infrastructure — broken down by category, product, vendor, and time period.

Key capabilities:

  • Consolidated spend overview with KPI cards and trend charts.
  • Detailed breakdown of LLM API costs by cloud provider and model.
  • Cost analysis by product, vertical, and usage metrics.
  • AI-powered finance chat assistant for natural language spend queries.
  • Month-over-month trend monitoring and annual spend projections.

Product AI​

Product AI is an interface for product teams to work with AI agents. Designers, product managers, and analysts use it to query agents, review conversation history, and manage configurations. Admins and managers can create and update agents directly from the same interface.

Key capabilities:

In summary, AI Cockpit integrates cutting-edge AI technology with human expertise to revolutionize the way software is developed. By combining AI Cockpit One, the Smart Engineering, Reasoning, Intelligent Requirements, and Metrics modules, and third-party applications like AI Adoption, AI/R Watch, and Product AI, it enables faster, more efficient, and higher-quality deliveries, while ensuring control and visibility of the entire process. The result for organizations is an optimized development cycle, capable of generating more business value and reducing costs compared to traditional approaches.