Tools

What it does: When your team opens a pull request on GitHub, CodeReview AI reads your company's actual documentation (security policies, style guides, architecture standards, legal requirements) and checks the code changes against those rules. It's not generic advice. It reviews against your ground truth.

How it's different: Every finding is clearly labeled: either it came from your documentation (with a citation you can verify), or it's the AI's own suggestion (marked as such). A second AI pass re-reads every finding against the source document before posting. If it can't prove the finding from what's actually written, the finding gets killed. The result shows up as a simple traffic light: red means fix before merge, yellow means take a look, green means good to go.

What's next: V2 adds full codebase scoring at install, a baseline strength rating across security, architecture, style, legal, and onboarding. A dashboard tracks how each area trends over time. V1 tells you what's wrong with this PR. V2 tells you what's happening to your codebase.

What it does: An autonomous trading agent for cryptocurrency. The system watches the market continuously using standard math (technical indicators), and when the numbers line up on a promising opportunity, the AI comes online to analyze the situation and decide whether to execute. Every trade must pass eight independent safety checks before any money moves.

How it's different: The math runs first. The AI runs second. And the AI only works with numbers the math already verified. It doesn't get to invent its own data. If the AI says one thing and the math says another, the trade is rejected automatically. The system also learns from four types of feedback: trades that worked, trades that didn't, trades it rejected that would have lost (confirming the safety checks are good), and trades it rejected that would have won (revealing where the safety checks might be too strict). A diagnostic system maps all four feedback types to pinpoint whether problems are in signal selection, execution, gate calibration, or market conditions, not just that something is off.

Safety: Five independent circuit breakers. Daily loss limits enforced in code, not suggestions. Position sizing constrained by three overlapping limits (the most conservative always wins). If any single safety check fails, no trade executes. The system is architecturally incapable of blowing up the account.

Available after paper trading validation

What it does: Ask it about any publicly traded company and it returns a confidence-scored analysis based on live market data and current news. It tells you what it knows, how confident it is, and what it doesn't know.

How it's different: The analysis runs through three separate AI stages, each with a different job. The first stage does deep research without any restrictions. The second stage rewrites the research into something safe and compliant for a user to read. The third stage checks that nothing slipped through. Two gates (one at the front, one at the back) validate that the request makes sense going in and that the response meets quality standards coming out. If it doesn't pass, the system tells you why instead of guessing its way to an answer.

Launch Finance Assistant →

Note: Educational use only. Not financial advice. Demonstrates patent architecture, not investment recommendations.

What it does: Sends a personalized morning email every day at 6 AM with weather, tech news, market data, and curated content. Fully automated with no human intervention required after setup.

How it's different: Each email is generated fresh by AI with live data, but a structured summary of every previous email prevents content repetition across days. The system has no memory between runs. All context is reconstructed from scratch each morning. This means it can't drift, can't accumulate errors over time, and can't develop blind spots from stale cached data. Runs for under $1/month.

Automated, no public interface