HTML with GPT for enterprise work
Twelve browser-native draft artifacts that make AI-assisted output inspectable, interactive, and easier for humans to review. Built with static HTML, CSS, SVG, and a bit of JavaScript.
Inspired by Thariq Shihipar's original HTML effectiveness examples. This site uses original enterprise scenarios, copy, sample data, and design.
Solid lines, dotted paths, and selected nodes stay code-native and inspectable.
| ID | Risk | Owner | Score |
|---|---|---|---|
| R-101 | Data residency | Priya | 12 |
| R-104 | Privileged access | Sam | 8 |
| R-107 | Rollback gap | Lee | 6 |
HTML is not replacing enterprise systems or human judgment. It is a high-signal artifact format for turning AI-assisted thinking into something people can inspect, edit, challenge, and sign off.
Useful when work is cross-functional, decision-heavy, and usually trapped in static notes, decks, or spreadsheets.
Useful when the artifact must explain risk, dependencies, evidence, and review focus without pretending to be the source of truth.
Each sample is a standalone, public-safe HTML artifact with real interaction, richer fictional data, diagrams, prompt cards, and boundary notes.
Weighted criteria, options matrix, stakeholder heatmap, and exportable recommendation.
Open sample →Owner filters, due timeline, decision log, persisted state, and follow-up export.
Open sample →Role selector, process map, review flow, controls, and RACI-style guidance.
Open sample →Upload or paste CSV, quality scorecards, filters, anomaly flags, and cleaned export.
Open sample →5x5 matrix, mitigation timeline, control checklist, and printable risk draft.
Open sample →Branching scenarios, tone comparison, response checklist, and safe-language boundary.
Open sample →Option scorecards, current/target diagram, timeline, consequences, and ADR export.
Open sample →Interactive node graph, risk paths, filters, evidence table, and reviewer checklist.
Open sample →Annotated diff mock, severity filters, reviewer checklist, and ownership matrix.
Open sample →Multi-lane timeline, five-whys, event filters, remediation board, and export.
Open sample →Evidence readiness dashboard, checklist, heatmap, and no-formal-evidence boundary.
Open sample →Toggle matrix, dependency graph, blast-radius score, rollout timeline, and JSON export.
Open sample →The demos rely on ordinary web platform capabilities. Where a capability matters, the page links to browser, standard, accessibility, or security references.
WHATWG HTML Living Standard documents the language itself.
MDN File API and Web Storage API support local inputs and state.
MDN SVG and Canvas API document browser-native graphics.
W3C WCAG 2.2 guides keyboard, contrast, semantics, and inclusive interaction expectations.
OWASP CSP guidance frames browser content as something to review deliberately.
The site demonstrates browser-native interaction, diagrams, local parsing, export, and presentation flow using fictional data.
Created by Vishal Shah with GPT assistance and reviewed as self-contained HTML artifacts. A related Claude-assisted experiment explores the same idea. This is not a model comparison.
LLMs can hallucinate or omit important context. Treat every generated artifact as a draft until accountable people verify facts, adjust wording, and sign off on its use.
These artifacts can make enterprise work easier to review, but they should never blur the line between a useful draft and accountable human decision-making.
Production sign-off still belongs with accountable people and official enterprise systems with identity, logging, ownership, and retention.
All sample content is fictional. Real use needs data classification, privacy, access, and retention review.
The HTML demonstrates artifact design. AI output quality, facts, and accuracy require human evaluation because LLMs can be wrong or incomplete.
Control packs and risk briefs are drafts or review aids, not official evidence by themselves.
Press P or use the Present button for a short keyboard-friendly walkthrough. The same page can be linked from LinkedIn or vishalshah.app.
