OpsProof: Stress-Test Changes Before They Reach Customers

The description states that OpsProof is a TypeScript application built with Next.js, React, Zod, Web Workers, Vitest, and Playwright. It includes a deterministic discrete-event simulation engine called FlowTwin, which models arrivals, FIFO queues, shared resource capacity, routing, outages, service slowdowns, utilization, SLA performance, Monte Carlo experiments, and bounded optimization.

The tool:

• Processes local Fulfillment or Support event logs.
• Validates data and calibrates a reviewed Operation Pack.
• Stress-tests demand spikes or outages.
• Identifies bottlenecks.
• Finds the lowest-cost tested recovery plan that meets an SLA.
• Produces a Decision Proof output with normal, stress, and recovery evidence across 100 fixed random seeds.

The system runs entirely in the browser, with no external API calls, databases, or runtime dependencies. It is described as a local event-log-to-decision workflow, not requiring an API key.

Inference The product appears to be a simulation-based decision-support tool for operations teams, focused on pre-deployment testing of changes using historical data and deterministic models.

The author states that OpsProof was built around the question:

“What is the lowest-cost tested change that can protect an operational SLA before a modeled disruption reaches customers?”

This reflects a shift from intuition-based decision-making to data-driven, simulation-based planning, especially in response to situations like demand spikes or system failures.

The positioning evolved:

• From an LLM-powered process-mining tool (which was removed).
• To a bounded, deterministic simulation engine that requires validated inputs and explicit assumptions.
• To a transparent, auditable decision-making framework that refuses to recommend anything without evidence.

The author emphasizes:

• No runtime AI dependencies.
• Clear refusal states for unsupported inputs.
• Reproducible simulations using fixed random seeds.
• A narrow focus on two reviewed operation types.

Inference OpsProof positions itself as a low-risk, high-transparency tool for operational planning, not a broad AI platform or workforce scheduler.

The description states that OpsProof is designed to help:

• Operations teams with timestamped event data.
• Teams who currently rely on intuition and discover bottlenecks only after customer impact.

It supports two operation types:

• Fulfillment
• Support-center operations

The author notes that the tool is intentionally bounded, not a universal platform, and targets specific operational domains.

Inference The ICP appears to be operations teams in logistics, customer support, or service delivery, who have historical event logs and need to test changes before deployment.

The description does not provide any information about:

• Revenue streams
• Pricing models
• Monetization strategy
• Customer acquisition plans

It only states that the tool is a self-reported MVP with no external dependencies, and that it runs locally in the browser.

Inference No evidence of a business model or pricing structure exists in the description. The project appears to be a personal or hackathon effort, not yet commercialized.

The system is built using:

• Next.js, React, TypeScript
• Zod for validation
• Web Workers for simulation
• Playwright and Vitest for testing
• FlowTwin engine: discrete-event simulation with deterministic behavior

Key technical features include:

• Deterministic simulations with 100 fixed random seeds.
• No runtime AI API calls or external dependencies.
• Local processing in the browser.
• Clear refusal states when data is incompatible or no tested option qualifies.
• Export of a Decision Proof containing model, scenario, candidate set, and evidence.

The author notes strong verification practices:

• Unit tests
• Browser E2E tests
• Worker-runtime tests
• Accessibility checks
• Mobile layouts
• Reduced-motion behavior
• Production builds

Inference The tool is technically well-engineered for a local simulation environment, with clear validation, reproducibility, and testing practices.

The description provides no evidence of:

• Revenue
• Customers
• Adoption
• Usage metrics
• Product-market fit
• Market traction

It only states that the project is an intentionally bounded MVP, not a universal platform.

Inference There are no traction or maturity signals in the provided description. The product is described as a proof-of-concept or hackathon output, with no evidence of real-world usage.

The description does not mention:

• Competitors
• Market positioning relative to others
• Existing tools in the space (e.g., simulation platforms, process mining tools, operational planning systems)

Inference No competitive context is provided. The tool appears to be self-contained, with no reference to existing solutions or market dynamics.

1. No traction or adoption evidence: The project is described as a self-reported MVP with no external validation.
2. Limited scope: It supports only two reviewed operation types, which may limit its commercial viability.
3. No monetization strategy: No pricing, revenue model or customer acquisition plan are mentioned.
4. Self-reported and unverified: All claims are from the author, with no third-party corroboration.
5. Narrow focus: The tool is intentionally bounded — this could be a strength or a risk depending on market demand.

Inference The main risk is that the project may not have real-world relevance or commercial traction, despite its technical sophistication.

1. What specific operational domains (e.g., logistics, support centers) are you targeting for adoption?
2. Have you tested this tool with real teams or in live operations?
3. How do you plan to scale beyond the current two operation types?
4. What is your path to monetization or customer acquisition?
5. Do you have any feedback from early users or domain experts?
6. What are the limitations of the deterministic simulation engine in real-world scenarios?