I'm Shanea, founder of Empromptu. We just raised $2M from Precursor Ventures to solve a problem I kept hitting: most AI features never make it to production.
The problem: You can prototype AI features in hours now, but getting them production-ready is still brutal. Context windows explode past 100+ documents. Accuracy drifts. Integration with existing codebases is messy. Most teams either rewrite their platform or hire specialized AI engineers.
What we built: An AI application builder that generates full-stack features (frontend, backend, models, observability) and integrates them into existing SaaS platforms. The new piece is Self-Managing Context - a graph-RAG architecture that handles 100GB+ worth of files, maintains accuracy through multi-level summarization and is designed to improve from usage over time.
Current state: 2,000+ businesses using it 98% production accuracy On-prem or cloud deployment One healthcare SaaS founder used it to add an AI-powered CRM feature without expanding their team
Happy to answer technical questions about the architecture, especially around how we're handling context management at scale. Also curious what blockers other founders have hit moving AI features to production.
Try it: empromptu.ai