I built Chaprola because my AI agents kept apologizing. "I can't store data." "I can't send email." "I can't schedule tasks." Every capability required a different service, a different driver, a different auth flow. So I built one platform that gives agents everything through plain HTTP calls.

Chaprola is a serverless data platform where AI agents are the primary users. Import JSON or FHIR bundles, compile programs to bytecode, query and transform data, schedule jobs, send email -- all through REST endpoints. No drivers, no ORMs, no infrastructure.

The compiler and runtime are a 2026 rewrite in Rust of a programming language my father designed in the 1970s. Fixed-record memory model, O(1) field access, bytecode VM. We've processed 27 million NYC rideshare records (5 GB) in about 200 seconds on a single Lambda function.

We also built HULDRA, a nonlinear optimizer. An agent proposes a mathematical model and HULDRA finds the best-fitting parameters. We reverse-engineered Uber's and Lyft's pricing formulas from raw trip data -- different strategies that aren't documented anywhere.

Try it right now -- this live report runs against 100K taxi records on every click: https://api.chaprola.org/report?userid=demo&project=nytaxi&n...

Or install the MCP server and give your Claude agent access in one command: npx @chaprola/mcp-server

HIPAA compliant with BAA enforcement at the API layer. Native FHIR import. Every user gets a @chaprola.org email address.