Modelence Launches Batteries-Included Platform to Streamline Agentic AI Development
Modelence, a Y Combinator Summer 2025 company, has emerged from stealth with an open-source, full-stack platform designed to eliminate the repetitive infrastructure work that slows teams down when moving AI applications from weekend demo to production. Backed by the founders’ decade of experience scaling one of the world’s largest skill-assessment platforms, Modelence offers developers a batteries-included framework that abstracts away the boilerplate common to 95% of modern production applications.
The company was founded in 2025 by Aram Shatakhtsyan and Eduard Piliposyan, a pair of engineers whose credentials speak directly to the problem they are solving. Shatakhtsyan previously co-founded CodeSignal, the skill-assessment platform valued at approximately $500 million and used by Uber, Zoom, Netflix and other major enterprises. He spent a decade leading engineering there and was named to the Forbes 30 Under 30 list in Enterprise Technology. Piliposyan served as the founding engineer and Director of Engineering at CodeSignal and brings 18 years of industry experience, including prior principal engineering roles at Intuit. Together, the pair watched countless teams rebuild the same foundational pieces – authentication, databases, hosting, real-time events, cron jobs, email, monitoring, analytics and admin dashboards – every time they wanted to ship a new product. Modelence is their answer.
“You built the demo in a weekend. Now you need auth, a prod-grade database, real-time events, AI observability, cron jobs, email, monitoring – and you needed them yesterday,” the Modelence team notes on its website. The AI web app builder bundles all of those components – authentication, databases, real-time events, cron jobs, email, monitoring, LLM observability, and prompt management – into a coherent stack with first-class support for modern agentic workflows. Developers can focus on the product logic that actually differentiates their application and deploy to production without reinventing the infrastructure wheel.
The timing is notable. In recent weeks, AI researcher Andrej Karpathy has publicly highlighted the same gap Modelence addresses, first in a widely shared post on X and later during a YC AI Startup School talk. As more teams prototype agentic applications, the leap from demo to real-world deployment has become the industry’s most persistent bottleneck. By open-sourcing its framework, Modelence is positioning itself as the default infrastructure layer for the next wave of AI-native products, while also lowering the barrier to entry for smaller teams that lack the engineering capacity of large incumbents.