AI App Development

Claude API · OpenAI · AI-native products

I integrate AI into real products — not a quick demo, but a system users can rely on every day. I shipped Claude API in production for Aril and ÜRE: prompt design, cost limits, fallback logic, and content moderation all included. As a solo developer, I plan all those layers from the start — I don't bolt them on later.

What's included?

In discovery we settle on the right model and integration architecture together. During the build, prompt engineering, cost analysis, fallback flows, and content moderation are developed in parallel. At handoff, I transfer observability tooling, a cost dashboard, and documentation.

Tiers & Pricing

Why me?

I shipped real Claude API integrations in production: Aril generates personalised gift lists from user preferences, ÜRE produces children's stories with matching audio and visuals — both live, both mine. Prompt design, cost limits, and content moderation were all my responsibility. I design AI as a core layer, not an add-on feature.

Related Work

FAQ

Which AI models do you use?

I use Claude (Anthropic) or OpenAI models depending on the project. I usually recommend Claude Sonnet — it gives the best balance of cost, speed, and quality for most product use cases.

Who pays the API costs?

API costs during development are on me. Post-launch costs are yours. I set up the architecture with your own API keys and document cost estimates and guardrails before handoff.

How is content moderation handled?

User input validation, prompt injection protection, and output filtering are standard in the MVP and above tiers. Additional moderation tooling can be integrated depending on the product's nature.

What happens when AI fails to respond?

Fallback flows are planned during design, not retrofitted. When the model is unavailable or a cost limit is hit, the user gets a meaningful message — the app doesn't crash.

What latency should I expect?

With streaming, the first token typically arrives in 1–2 seconds. Actual latency depends on model choice and prompt length; we measure together and set targets in the prototype phase.