Define the question
Start with one company, a comparison, or an industry decision.
AI Product Manager & Builder · Commercial Space & Satellite Internet
Inside LaurenceYang Commercial Space & Satellite Internet Research OS.
LaurenceYang connects commercial-space evidence, deterministic calculations, a controlled explanation layer, and human review in one workflow. Each layer has a clear job, so the final answer can be checked instead of simply trusted.
QuestionEvidenceAnalysisExplanationReview
Five steps. One evidence trail.
The workflow starts with a business question and ends with a report that keeps its sources, assumptions, calculations, and missing information visible.
Collect evidence
Bring together market data, filings, news, and industry sources.
Run the tools
Normalize the data, cross-check sources, and calculate valuation or risk signals.
Write the narrative
The explanation layer organizes evidence into clear language but does not rewrite the numbers. The public release uses fixed, labeled examples rather than a live model.
Check and save
Review assumptions, gaps, and counter-evidence before keeping the report.
Four layers with clear responsibilities.
The architecture separates evidence, calculations, language, and delivery. This makes the system easier to test, explain, and adapt.
Evidence
Quotes, filings, company news, and industry events enter through traceable data sources.
Analysis
Deterministic tools clean identifiers, compare sources, and calculate financial or industry signals.
Explanation
An AI-ready layer can turn structured results into readable research while leaving calculations unchanged; the public snapshot shows fixed examples.
Delivery
The Workbench brings the evidence, comparisons, reports, and follow-up analysis into one place.
Controls that keep the output trustworthy.
The product is designed to show uncertainty and preserve human judgment, especially when data is incomplete or an AI-assisted explanation needs review.
Tools own the numbers
Financial calculations run outside the language model. The model may explain a result, but it cannot silently replace it.
Missing stays missing
If a source does not provide a value, the product shows the gap instead of inventing a complete-looking answer.
People make the decision
Sources, assumptions, risks, and counter-evidence remain visible so the final judgment stays with the reviewer.
Portfolio contribution
AI Product Lead & Co-Developer
I led the product requirements and research framework, source collection, front-end experience, testing, deployment, and continued iteration. The product was co-developed with one other human developer.
- Requirements and scope
- Research workflow design
- Source and data collection
- Front-end product experience
- Testing and release
- Deployment and iteration
See the workflow in the product.
Follow the guided demo for the full research loop, or open the Workbench to explore the working product.