AI4ALPHA

Scale agents.
Ship intelligence.

AI4ALPHA builds agents, structured data, executable skills, and evaluation loops for domain work that has to be correct.

Overview

Agents evolve in three layers.

UcoWorker is the agent. The data layer gives it domain memory. The evaluation layer makes improvement measurable.

Agent runtime

UcoWorker Agent

A general coworker users can run in the browser, on desktop, and in cloud jobs.

The runtime users touch

Domain memory

Data Layer

Structured records and executable skills that turn a general agent into a domain worker.

The compounding asset

Correctness loop

Evaluation Layer

Benchmarks and review sets that measure whether agents answer correctly, not just fluently.

The feedback loop

Thesis

Models get smarter.
Domain data gets better.

The durable layer is source-linked data, repeatable skills, and evaluation sets that improve as agents work.

Agents + data + evaluation

Data
compounds

Every normalized record becomes reusable context for future agents.

Workflows
compound

Repeatable procedures make domain work less dependent on one prompt.

Evaluations
compound

Every test set makes correctness measurable across models and agents.

Domains

First domains:
finance and legal.

We start where freshness, structure, and correctness matter. Each domain reuses the same agent, data, skills, and evaluation pattern.

01

First public domain

US Finance

119M holdings, SEC filings, prices, fundamentals, and market records.

02

Pilot domain

Chinese Legal

Mandarin legal workflows with Chinese legal sources and structured procedures.

03

Same pattern

Future Domains

Same playbook by request: data, skills, evaluations, and runtime.