LLM-native apps for Automotive & Aftermarket.
Product surfaces built from the ground up around LLMs — chat, co-pilot, structured generation — with streaming, tool-calling and graceful degradation. Applied to vehicle marketplaces, aftermarket parts catalogues, build journals, service-pro hire layers and enthusiast communities — engineered for high-media, high-engagement automotive culture brands.
Production AI, not demoware.
Product surfaces built from the ground up around LLMs — chat, co-pilot, structured generation — with streaming, tool-calling and graceful degradation. We wrap it in the evals, observability and cost ceilings that make llm-native apps viable for automotive & aftermarket operators — not just impressive in a demo.
- Multi-model routing (GPT-5, Claude 4.5, Gemini 3)
- RAG grounded in your data
- Cost ceilings and rate-limit handling
- Offline + online evals
- PII redaction and safety layering
- Full observability on every LLM call
FAQ
Can Origami build llm-native apps for automotive & aftermarket?+
Yes. Origami has been shipping llm-native apps for automotive & aftermarket operators since 2020. We ground every AI feature in real product surfaces — no demoware.
Which models do you use?+
We are multi-model. Typical stacks include OpenAI GPT-5, Anthropic Claude Sonnet 4.5, and Google Gemini 3 — routed per workload with cost ceilings and evals wrapped around every call.
How do you handle data and privacy for Automotive & Aftermarket?+
RAG-grounded, tenant-isolated retrieval; PII redaction where required; regional processing where regulations demand it; and full observability on every LLM call.
How quickly can we go live?+
A scoped llm-native apps pilot typically ships in 4–8 weeks. Production hardening (evals, rate limits, cost caps, monitoring) is another 2–4 weeks.