AI with direction, not loose prompts.
Integrations of generative or conventional AI where AI is one more tool among the ones you already use, not a marketing trick or empty promise. Pipelines, internal tools, automation that understands your business.
I've been using generative models in own and client projects for years. NIXIA, Vex Muse and Valentina Serrano are three parallel practices in my studio where AI is central, and that has taught me what works and what doesn't in real production, not in demos. The difference between a loose prompt and a serious pipeline is huge, and that's where most of the value is.
With clients I work in three main directions. First, internal tools: your team needs to generate copy variants, translate documents, classify tickets, extract data from PDFs. Those are problems solved today with APIs like OpenAI's or Anthropic's, but they need to be wired well into your workflow. Second, visual content generation with direction: image, video, audio, kept coherent within a brand or IP. Third, local pipelines: when there's sensitive data or you're going to generate large volumes, I set up inference on your own hardware (or my workstation with RTX 4070 + 64GB for fast experimentation) using ComfyUI, SwarmUI or custom flows.
I don't sell magic. Generative AI has real limits: it hallucinates, makes typos, varies between runs, and for critical output a human review is always needed. I say that from the start of the project. If your value proposition depends on "100% automated with no supervision", I'm not your person: either we change the scope, or I recommend you wait six months for the tools to mature further.
How we work
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01
Use case diagnosis
Not every problem is best solved with AI, and some are solved with AI but worse. Phase one is understanding what you're trying to solve, seeing if AI is the right tool, and if so, choosing between commercial API or local pipeline.
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02
Quick prototype (1–2 weeks)
Before investing in serious integration, I show you a working prototype with your real data so you can judge actual quality. If it's not convincing, we stop. If it is, we move to definitive architecture.
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03
Integration and hardening
Connection to your systems (CRM, CMS, internal app), error handling, cost control, auditable logs, fallback when the API fails. The boring but critical part of going from prototype to production.
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04
Training and handoff
Your team needs to know how to operate the tool, adjust prompts when needs change, read logs when something fails. Living documentation and practical training are delivered.
What's included
- Use case analysis and technical viability
- Working prototype with your real data before committing further
- Integration with commercial APIs (OpenAI, Anthropic, Replicate) or local pipeline
- ComfyUI/SwarmUI pipelines for image/video generation
- Cost control and rate limiting
- Logs and traceability of every generation
- Operational documentation for your team
- Evolution support (the models change quickly)
When we're not the right fit
- "An AI that does it all by itself" If you expect to remove human supervision 100% for critical tasks, today it's not possible and promising it would be dishonest.
- Training models from scratch I'm not an ML researcher. Training your own foundation model is outside my scope, and almost certainly outside yours. If you really need it, I can point you elsewhere.
- Implementing AI "because it's trending" If you don't have a use case with measurable value, save the money. I'll tell you on the first call.
Stack & tools
- Anthropic (Claude), OpenAI, Replicate as main APIs
- ComfyUI + SwarmUI for local image/video pipelines
- Models: Flux, Stable Diffusion, Wan, Veo, Kling, Seedance as appropriate
- PHP/JS for integration with your existing systems
- Hardware: own workstation with RTX 4070 Super for experimentation
- RunPod or client cluster for local production with GPU
- Structured logs (JSON) for auditability
If you have a real use case where AI can deliver measurable value, tell me what it is and we'll look together at whether it makes sense before moving a single line of code.
Let's talk →