TC Dashboard — JARVIS Project Operating System

The description states that TC Dashboard is an AI-native project operating system. At its core is JARVIS, a reasoning and governed-action layer that:

• Connects authorized project evidence.
• Proposes the smallest useful next step.
• Shows exact payload and intended effect.
• Executes supported actions only after user approval.

JARVIS operates within a deterministic replay path to ensure judges can inspect governance reliably. It supports actions like release readiness, risk signal handling, and follow-through on work ownership.

The system treats uploads, OCR, indexed artifacts, email, tickets, tool output, and prior model output as untrusted evidence—not instructions or authorization.

Actions that are consequential use typed schemas, exact payloads, and signed single-use approvals. Execution rechecks current permissions and state.

Evidence The description states this is a React/TypeScript frontend with an Express/MongoDB backend, using project-scoped provider configuration and a typed JARVIS action registry.

Inference The system appears to be built for quality engineering, but the author does not claim it is limited to that domain.

The description states that TC Dashboard changes the relationship between teams and their projects by addressing scattered signals—such as risky runs, release decisions, tickets, and documents—that live in different places. The author claims the system makes the project work for you—not the other way around.

JARVIS is positioned as a governed-action layer that connects evidence, explains why it matters, proposes follow-through, and ensures human control over consequential changes.

Evidence The description states this is an AI-native operating system with a “judge-ready proof loop.”

Inference The positioning implies a shift from reactive project management to proactive, evidence-based decision-making. However, no claims are made about market adoption or competitive differentiation beyond self-description.

The author does not explicitly define a target customer or ideal customer profile (ICP). The description focuses on the operating model and functionality but does not name specific users, roles, or industries.

Evidence Not evidenced. The description does not state who uses this system or what their job is.

The description does not contain any information about pricing, monetization, or business model. It does not state whether the product will be sold, licensed, offered as a service, or used internally.

Evidence Not evidenced. No mention of revenue streams, pricing tiers, or commercial arrangements.

The system is built with:

• Frontend: React/TypeScript
• Backend: Express/MongoDB
• AI Tools Used: Codex, GPT-5.6, Playwright
• Features Implemented During Build Week:
  • Typed actions and untrusted-content isolation
  • Signed single-use approvals
  • Replay and stale-state protection
  • Audit support
  • Security scans

The author claims that the system uses project-scoped provider configuration for model-backed assistance and maintains deterministic replay paths to avoid dependency on live model responses.

Evidence The description states the architecture and features implemented during Build Week, including test coverage (98 frontend / 593 backend tests) and integration verification.

There is no evidence of traction or maturity beyond the author’s own development. No customers, users, or adoption data are provided. The system appears to be a prototype or proof-of-concept built during a hackathon.

Evidence Not evidenced. No mention of real-world usage, customer feedback, or product deployment.

The description does not provide any information about competitors or the competitive landscape. It does not reference existing tools in the project management, quality engineering, or AI governance space.

Evidence Not evidenced. No mention of similar products or market positioning.

• No real-world usage: The system is described as a personal project built by one person and has no evidence of adoption.
• Unverified claims: All descriptions are self-reported, with no independent verification.
• Limited scope: No mention of integration capabilities beyond the author’s own environment.
• Unclear commercialization path: No indication of how this would be monetized or scaled.

Inference The lack of traction and customer data raises questions about product-market fit and viability as a commercial offering.

1. What is the intended user role for JARVIS? Is it for developers, QA engineers, project managers, or executives?
2. How does TC Dashboard plan to integrate with existing CI/CD pipelines or project management tools?
3. Has any external testing or feedback been gathered from users beyond the author?
4. What are the plans for expanding the governed action registry and integration coverage?
5. Is there a roadmap for moving beyond the current prototype stage?