Systems, tools, and experiments from the frontier of AI engineering.
A test bench for the machinery that feeds documents to a language model. Almost every rule of thumb in this field is advice nobody measured, so this measures it on your own documents and your own questions, and ranks what comes out on quality, speed and cost. The answer key is stored as a stretch of characters in the original document rather than as a chunk number, so a set of test questions written once stays correct even after you change how the documents are cut up.
On PyPI under MIT, still an early release — it turns retrieval folklore into numbers on your own documents.
An agentic AI system built on the Agno framework that analyzes millions of historical support tickets and recommends resolutions in real time.
60% reduction in resolution time, 40% drop in support costs
A reusable framework for deploying config-driven AI agents via REST API, with support for memory, multi knowledge base retrieval, tool use, and enterprise integrations.
Adopted across multiple internal products and teams
A low-code RAG framework that lets teams deploy retrieval augmented apps quickly, adopted organization wide by engineering and product.
Adopted by all internal teams and product managers
A RoBERTa based language model pre-trained from scratch on 60M job descriptions to lift downstream NLP performance in the labor market domain.
5%+ gain on downstream NLP tasks