We use AI to build products, not demonstrations
Adding an LLM API is easy. Building an AI product dependable enough for real users is harder. UltraApps designs AI systems around model selection, context, tools, structured outputs, evaluation, security, observability and human oversight.
- We choose technology based on the product, team and business problem
- The stack serves the product — not the other way around
- OpenAI
- Claude
Core technologies
What we use in this category
Best for: Managed enterprise AI
Vertex AI
Enterprise AI infrastructure on Google Cloud.
Best for: Knowledge products
RAG & vector search
Ground generation in your documents and systems of record.
What we build
We are model-agnostic
The best model today may not be the best model six months from now. We prefer architectures that allow products to change models without rebuilding the entire application.
- RAG systems
- Vector search
- Tool calling
- Agent workflows
- Model routing
- Structured generation
- AI evaluation
- Human-in-the-loop systems
FAQ
Questions teams ask
Straight answers on stack choices, fit, and how we engage.
It depends on the task, latency, cost and data residency. We evaluate OpenAI, Claude and Gemini against your workload and often route different jobs to different models.
Yes. Where it matters, we design abstraction around providers so you can swap or combine models without rewriting the product.
Structured outputs, evaluation harnesses, guardrails, observability, fallbacks and human-in-the-loop paths for consequential actions.
Next step
Build an AI product with UltraApps
Tell us the workflow. We’ll recommend models, architecture and the controls that keep it production-safe.
Discuss my project