I design and build the systems I recommend.
I am Carlos Quinones, an AI automation consultant based in Orlando, Florida.
In early 2025, I shifted from hobby projects to building commercial AI systems full-time. The catalyst: as open-source models improved dramatically, it became practical to run capable inference on client-controlled hardware, reducing reliance on public model APIs and per-token pricing for suitable workloads. Other projects use managed services when integration, latency, or operating requirements make a hybrid or cloud-connected design the better fit. My portfolio since then includes document processing pipelines for trade associations, community research assistants, desktop AI companions, and automation systems for financial services and insurance.
I focus on businesses where privacy, contractual commitments, and security controls materially affect architecture decisions, including healthcare, legal, and financial-services workflows. When a client's risk analysis or policies favor client-hosted inference over a public model API, I build systems around that requirement. Local deployment is an architectural option, not a compliance certification.
Why local-first
I build working prototypes on my own hardware before you commit to full deployment. You see the pipeline process representative data in real time, not a slide deck. Prototypes typically use synthetic, de-identified, or otherwise approved representative data in an agreed environment.
What I build
I specialize in Python, using it as the foundation for local, agentic AI pipelines before integrating with other systems -- databases, APIs, desktop applications, automation tools. Client deliverables include the documentation and deployment or handover instructions defined in the project scope.
Track record
Upwork
As of July 2026, my Upwork profile shows 100% Job Success and a 5.0 average across three public reviews.
View Upwork profile →Approach
Audit
I look at your data, processes, and constraints. Not a sales call -- an honest assessment of what AI can (and cannot) do for you.
Prototype
I build a working prototype on my own hardware, typically with synthetic or de-identified sample data you approve. You see the pipeline run before committing to full deployment. The production target is agreed during discovery and may be client-hosted, hybrid, or cloud-hosted based on data sensitivity, integrations, security controls, and operating requirements.
Deploy
I deploy to the agreed environment and provide the scoped documentation, training, and support plan. Client-hosted, hybrid, and managed-cloud architectures are all available when appropriate.