Project cockpit
One screen with budget, schedule, resource, procurement and risk health — plus a readiness check that tells you whether the project is ready for steering.
MVP in validation — demo environment available
Project control built on evidence, not hand-assembled reports.
ControlRoom connects budget, costs, earned value, risks and decisions in one operating cockpit. Every number traces back to its source; what is missing stays declared, not hidden. The guided demo tests fit against your real context.
Working modules
What ControlRoom does today.
Not a roadmap promise: these modules already work end-to-end in the demo environment, on a real backend with real data.
Budget and cost control
Budget lines, actual costs, commitments and forecasts in one economic panel, with total exposure and variance against Estimate at Completion.
Earned Value Management
BAC, PV, EV, AC, CPI, SPI, EAC and VAC computed deterministically from your data: same input, same result, always verifiable.
Risks, issues and decisions
A log with owners, impacts and rationale. Relevant signals — an open high risk, a stalled decision — surface directly in the cockpit.
Portfolio dashboard
Cross-project rollup with weighted CPI and SPI, a ranking of projects needing attention and explicit reporting of missing data.
Reports and snapshots
Immutable project and portfolio snapshots, PDF export, email delivery and side-by-side comparison of two reports to see how KPIs move.
Compared to what
What you’re comparing ControlRoom against
Before ControlRoom, most teams in this position patch together one of these.
- Excel and PowerPoint — a static export, not a shared system of record: every update lives in whoever’s copy is open, and reconciling two versions is manual work.
- Generalist task managers (Monday, Asana, Jira) — built to organise tasks and work; each documents its own features, including financial ones (Monday, for example, describes budget and expenses). The question is not whether a budget exists, but whether plan, economics, risks and decisions are connected and readable together in a review: that is what the ControlRoom demo lets you check.
- Enterprise suites (Primavera, Procore) — enterprise tools covering budget, schedule and portfolio governance; the right choice depends on the size and context of your organisation.
From project to portfolio
The same discipline, across every project.
The portfolio view introduces no new numbers: it aggregates facts already verified in each project cockpit. If a project is missing procurement or progress data, the dashboard says so — instead of producing an average that looks complete but is not.
- Ranking of projects needing attention, with the reasons made explicit.
- CPI and SPI weighted by budget, not simple averages.
- An always-visible “data gaps” section: what is missing is part of the picture.
Governed AI
AI interprets the numbers. And shows its evidence.
AI reports are an optional layer that is still in validation: no interpretation is presented as already validated. The design principle: start from immutable backend-calculated snapshots, not free text; have every statement cite the evidence it derives from; reject numbers that do not exist in the source data; audit every generation. The default engine stays deterministic, and the external LLM provider is an explicit choice, not a default.
It is the same principle I apply in my AI and governance consulting: a verifiable data foundation first, assisted interpretation second.
Product principles
A cockpit that is readable, verifiable and useful in decisions.
Connected sources
Every signal traces back to the record it comes from: a budget line, a cost entry, a risk, a decision.
Explicit limits
Missing data and uncertainty remain visible. Where there is no data, the cockpit says “unknown” instead of inventing an average.
Operating decisions
The view is organized around what needs attention, ownership or a decision — not around producing yet another report.
FAQ
Frequently asked questions
Where does project data reside, and who has access to it?
Project data lives in ControlRoom’s own hosted database, isolated per workspace. Today, only the founder — the person operating the product — can access it, for direct technical support. No project data is shared with or sold to third parties.
Does an external LLM provider see my data? How do I turn it off?
AI reports are an optional layer that is still in validation. The design principle is that by default they use the deterministic engine and do not call an external LLM; using an external LLM provider is an explicit, opt-in choice. Ask me for the current status during the demo.
What is the minimum data needed to start, and how long until I see a first cockpit?
The guided demo starts from the projects, data sources and reviews you describe, not a fixed data template. I have not yet measured a reliable minimum-data or time-to-first-cockpit figure across enough real projects to publish one. Ask me directly during the demo and I will give you an honest, case-by-case estimate.
How do I export my data, and what stays accessible if I stop using the product?
Today, reports export to PDF and can be delivered by email — every report you generate stays yours, outside the product. A broader structured data export beyond those PDF reports is not available today and is not on the current near-term roadmap.
What happens if the product is run by a single person?
ControlRoom is built and operated by one person today: there is no dedicated support team and no formal SLA. That is a real limit, not a hidden one: do not rely on ControlRoom where guaranteed uptime or support response times are a hard requirement. What you keep regardless: every report you generate exports to PDF and stays usable outside the product.
What is NOT included today?
Not presented as available in the demo today: enterprise identity (SSO, MFA, invitations and email verification), integrations or imports from the tools you already use, and document workflows beyond PDF export. See the public roadmap for what is available now, what is in validation, and what comes next.
Behind ControlRoom
Who built this, and why
I’m Vinicio Lupo, an AI Process Intelligence advisor working across engineering, project management and automation. I built ControlRoom after seeing the same pattern in different contexts: teams with data and tools that still struggle to see what is really happening, where budgets and risks drift, and which decisions remain open. I apply here the same approach I use in advisory work: structure first, automation second.
My professional backgroundControlRoom
Where we are: a working MVP, validation in progress.
The project-control modules described on this page can be demonstrated end-to-end in the demo; AI reports are an optional layer still in validation. Not presented as available in the demo: integrations and imports from existing tools, enterprise identity (SSO, MFA, invitations and email verification) and richer document workflows. The public roadmap is honest about what is available, what is in validation and what comes next.
Read the roadmapControlRoom applies the AI Process Intelligence method to project governance. If you need a process assessment before any software, see how I work or get in touch.
Want to test the fit on your project?
Request a free demo prepared on your context: projects, data sources, reviews. No price and no outcome is promised before validation.
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