Service
LLM agents and RAG pipelines grounded in your data
A model writing from memory is fast and quietly wrong. I build LLM systems that retrieve the right passages from your own documents first, score the result before anything expensive runs, and keep working when one provider fails.
Discuss a project →What you get
- Ingestion of your documents, PDFs and books into a search index
- Answers and scripts written only from retrieved passages
- A quality gate before paid generation, and cost logging per call
- Several LLM and TTS providers with automatic fallback
Where I have already done this
AI pipeline · RAGAI Video StudioFrom a library of books the system makes a narrated, illustrated video on its own.8 stages, no manual step1 quality gate before paid generationPersonal tool · LLMJobHunterA personal trainer for getting hired: voice mock interviews, a study course with an AI mentor, and a job feed that picks the vacancies worth the effort.6+ sources in one feed0–100 explainable score for every posting
Questions
- Which models do you use?
- OpenAI, Anthropic Claude, Gemini and Together, switchable in settings; the pipeline does not depend on one vendor.
- Can it run end to end without people?
- Yes. One of my pipelines goes from a book library to a published video in 8 stages with no manual step.