From market noise to
product decisions.
Raydar is an AI-assisted commerce intelligence system designed to turn fragmented trend, market, and product evidence into structured, explainable decisions.
AI interprets.
Humans decide.
More information was not the answer.
Running a digital product business creates a constant stream of trend signals, competitor observations, seasonal timing, product ideas, and performance data. The real bottleneck was turning those scattered inputs into a consistent decision about what deserved production time.
A decision system, not a trend dashboard.
Raydar structures evidence, scores opportunities with deterministic logic, surfaces the reasoning behind the score, and then moves owner-approved opportunities through a product workflow from research to launch and performance review.
One continuous decision loop.
Deterministic where it should be.
Numerical scoring is handled by code rather than an LLM. That keeps recommendations reproducible, inspectable, and easier to debug. AI belongs at the interpretation layer: summarizing evidence, identifying patterns, and helping a person understand why an opportunity may matter.
The model does not get the final vote.
Raydar intentionally separates assistance from authority. The system can organize, compare, score, and recommend. A human owner still decides whether an opportunity moves forward, changes direction, or gets rejected.
Explainable opportunity scoring
Dimensions are scored 0–100 before weighting.
Evidence that buyers are actively looking for or responding to the idea.
Whether the signal is strengthening across the observed period.
Potential to extend the concept into a repeatable product family.
How naturally the opportunity supports personalization or variants.
Alignment with the business, production model, and product catalog.
Room to differentiate rather than simply follow a crowded market.
Strength and consistency of the evidence behind the recommendation.
A penalty when strong demand is paired with excessive competition.
Built as a portfolio-safe public system.
The public demo is implemented with Next.js, TypeScript, React, Recharts, and Vercel. It uses synthetic and anonymized data, does not expose paid AI endpoints or credentials, and does not persist visitor actions.
Private operations, public proof.
The production concept was developed for a real commerce workflow, but the public version was deliberately separated from private competitor intelligence, real sales records, and owner data. The goal is to demonstrate the system without compromising the business behind it.
The hardest part of AI product design is deciding where AI should stop.
Raydar reinforced a principle I want to keep applying: useful automation is not measured by how much human judgment it removes. The better system is often the one that improves the quality, speed, and consistency of the decision while keeping accountability with the person making it.
Portfolio case study. Demonstration data is synthetic/anonymized and does not represent live business performance.