From business requirements to production-ready AI systems.
Ingenarte is an independent AI solutions engineering and product development practice.
We work with founders, executives, and technical teams to transform business requirements into secure, maintainable, and production-ready systems.
Our work starts before implementation. We analyze the operating model, users, workflows, data, constraints, expected outcomes, and investment context before defining the architecture.
Business problem
↓
Discovery and requirements
↓
Solution architecture
↓
Product implementation
↓
Deployment and observability
↓
Continuous evolution
We do not begin with a model, framework, or technology.
We begin with the business problem that the system must solve.
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Production-oriented AI capabilities integrated into real products and workflows.
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End-to-end software products designed around real users and operating processes.
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Reliable automation that connects systems, data, people, and decisions.
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Technical foundations for systems expected to operate and evolve.
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| Phase | Engineering objective | Typical outputs |
|---|---|---|
| 01 · Discovery | Understand the actual business and operational problem | Requirements, workflows, constraints, risks, success criteria |
| 02 · Architecture | Define a solution that balances value, cost, and complexity | System design, data model, integrations, infrastructure decisions |
| 03 · Implementation | Deliver working software through controlled increments | Frontend, backend, AI pipelines, APIs, automated tests |
| 04 · Production | Operate the solution under real conditions | Deployment, security controls, observability, documentation |
| 05 · Evolution | Improve the system using evidence from production | Performance improvements, new capabilities, technical roadmap |
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Technology is selected according to the result it must produce. |
Business, software, infrastructure, users, and operations are treated as one system. |
Security, maintainability, performance, and observability are design concerns from the beginning. |
Architecture and implementation remain connected throughout delivery. |
AI-assisted content intelligence and publishing platform
A multi-stage system that monitors source content, extracts knowledge, verifies facts, generates bilingual articles and media, manages approvals, and coordinates publishing workflows.
Source monitoring
→ Content ingestion
→ Transcription
→ Topic segmentation
→ Fact verification
→ Article generation
→ Media composition
→ Human approval
→ Multi-platform publishing
Engineering areas: AI orchestration, LLM pipelines, RAG, asynchronous processing, GPU workloads, content operations, observability, cloud infrastructure.
AI-enabled systems for structured scientific and operational workflows
Product engineering across user interfaces, backend services, authentication, structured research data, deployment decisions, and AI-assisted engineering workflows.
Engineering areas: requirements analysis, frontend architecture, backend integration, research data, authentication, product delivery.
Custom product configuration and automated order workflow
A web product for creating custom sticker designs, processing uploaded images, generating production assets, and coordinating order execution.
Engineering areas: React, Django, REST APIs, Auth0, RBAC, image processing, automated workflows.
Privacy-first browser extension
A Chrome extension that converts LinkedIn profiles into clean, print-ready PDF documents using local browser processing.
Engineering areas: browser APIs, DOM processing, client-side rendering, document generation, product UX.
Reusable React game engine and component package
A configurable Tetris component with typed state management, collision logic, rendering, tests, documentation, CI/CD, and npm distribution.
Engineering areas: React architecture, TypeScript, reusable components, state machines, testing, package engineering.
Cross-platform workflow automation tool
A portable automation application for testing and repetitive operational processes, with graphical and command-line interfaces.
Engineering areas: desktop automation, cross-platform behavior, workflow portability, system interaction.
Ingenarte is a strong fit when an organization needs to:
- determine whether a problem requires AI, conventional automation, analytics, or standard software
- convert an ambiguous idea into a technically feasible product plan
- integrate AI into an existing application or operational workflow
- replace manual processes with controlled and observable automation
- design the architecture for a new SaaS or internal platform
- restructure a system that has become difficult to maintain or scale
- connect product requirements with real implementation and deployment decisions
Ingenarte is led by Franco Mariano Rodrigo, an Industrial Engineer and AI Solutions Engineer with experience across industrial engineering, electromechanical systems, software products, cloud infrastructure, automation, and applied AI.
This multidisciplinary background supports an engineering approach that connects:
Operations
+ Product requirements
+ Software architecture
+ AI capabilities
+ Infrastructure
+ Delivery economics
