Risk signals
Find the slip in week three, not week eleven.
A deal that dies quietly in month one still sits in the roll-up in month three. Every open opportunity carries the specific reason it is exposed, sorted by what it costs you.
You put your name on a number every quarter and then spend the quarter defending it. CommitControl scores every deal in the roll-up against the deals your team has actually won, and shows the working line by line. Run it twice on the same pipeline and you get the same answer twice. Read-only from Salesforce. Your reps never log in.
Plans from €249 a month, whole team included. Month to month, cancel any time.
The big forecast platforms are good at what they do, and they score your pipeline with probabilistic models. You get a number, but not the audit trail behind it. So when the board asks why, the honest answer is that the model said so. Deterministic scoring turns that around. Every input, weight and calculation is open to inspection, including yours.
Every figure is derived from your own Salesforce records by a fixed calculation. The language layer sits on top of that arithmetic and describes it. It cannot produce a number, alter one, or change how anything is weighted. Try it.
Real output from the demo tenant, shown as an illustration.
CommitControl is a deterministic revenue intelligence platform. The score you see is not a single model guessing. It is an ensemble that blends three signals in a fixed proportion that does not change between runs.
Because the second signal is drawn from deals as they close, the scoring keeps tracking how your business is actually behaving. It sharpens the longer CommitControl runs against your pipeline. The proportions themselves do not drift, so the calculation stays reproducible and auditable while the evidence behind it deepens.
CommitControl reads your Salesforce history and learns what a won deal looks like in your market: your stages, your buyer behaviour, your sales cycle.
Every active opportunity gets a live Win Score built from buyer engagement and stage-gate momentum. Each score arrives with the reasoning that produced it, so your team can argue with it instead of taking it on faith.
You decide what makes commit. CommitControl flags the single-threaded, stalled and slipping deals so your managers step in before the close date moves.
A deal that dies quietly in month one still sits in the roll-up in month three. Every open opportunity carries the specific reason it is exposed, sorted by what it costs you.
Every deal in the commit carries a score and the stated reason for that score. Where your history is thin you get a wide confidence band and a note saying how few deals it is working from, rather than a confident figure built on twelve opportunities.
The same standard applies to every deal on the call. The conversation moves from who sounds most certain to what the evidence supports. You still make the call. You just stop making it against the loudest person in the room.
It is the number you stake your credibility on. CommitControl gives you the discipline to set it precisely, the evidence to defend it under pressure, and the early warning to keep it honest from week one to the last hour of close.
What CROs, VPs of Sales and RevOps ask before they connect anything. Straight answers, including the ones that are limitations.
The calculation is fixed and repeatable. Put the same pipeline through CommitControl twice and you get the same scores twice. Nothing is sampled, nothing is random, and no model re-rolls between runs.
It also means the language layer has no vote. The Ledger reads the arithmetic back to you in plain English. It cannot produce a figure, change one, or alter how anything is weighted. If a number is on the screen, a calculation on your data put it there.
Because a benchmark tells you how deals close on average across other companies, and you do not sell to average. Your stage definitions, your cycle length, your buying committee and your qualification bar are yours. A model trained across tenants will confidently score your enterprise deals against somebody else's SMB motion.
Calibrating on your own closed-won and closed-lost history costs me the ability to claim I learn from thousands of companies. It buys you a score that is actually about your business, and it means your data never trains anything another customer sees.
Two things, both of them visible. Where there is not enough history to score confidently you get a wide confidence band and a note saying how few deals it is working from, rather than a precise-looking number built on twelve opportunities. Where fields are missing or stale the deal is flagged on that dimension instead of quietly defaulting to an average.
Being straight with you: most Salesforce instances are messier than their owners expect. If your stage history is thin, the first few weeks will tell you more about your CRM hygiene than about your pipeline. That is still worth knowing, but it is worth knowing going in.
Every score opens to the reasoning that produced it: which signals fired, which way each one pushed, and how the deal compares to similar deals your team has already closed. Your RevOps and security teams get the methodology documentation covering what is read from Salesforce, how each signal is derived, and how they combine.
The connection is read-only, so you can verify from the Salesforce side exactly what was queried. If a number cannot be traced back to a record in your own CRM, that is a bug and I want to hear about it.
Intelligent enough to be useful, and deliberately not autonomous. Underneath the scores is a genuine ensemble: a win-probability model calibrated on your closed deals, your own observed win rates by segment and stage, and the structural weight of the forecast category a deal sits in. It sharpens as more of your deals close, because more of your own history is feeding it.
What it will not do is make the call. It does not move deals, message your reps, or decide your commit. It assembles the evidence and shows the working. The judgement stays with you, and that is the design, not a gap in it.
CommitControl inherits your Salesforce permissions and business rules. Your admin authorises the connection once on the integrations page. It is scoped to your tenant and bounded by that profile, so you constrain it at your end using the controls your team already administers. There is nothing new to authorise, nothing to re-audit, and nothing to configure seat by seat. The whole team is covered from day one. The connection is read-only, so the ceiling on what it can do sits lower than the profile allows anyway.
CommitControl reads. It does not write to your CRM, it does not sit in any payment path, and it never records a call or an email. Most of what follows you can verify yourself without talking to us.
Security documentation and DPA| Access | Read-only. There is no create, update or delete path to Salesforce anywhere in the codebase. Object access is governed by the profile of the user who authorises the connection, so you constrain it at your end. |
| Residency | Hosted in AWS eu-central-1, Frankfurt. Your pipeline data does not leave the EU. |
| In transit | TLS 1.3 with HSTS preload. Verifiable against commitcontrol.com before you speak to us. |
| Tenant isolation | Enforced at the database level with row-level security, not in application code. |
| Model privacy | No cross-tenant training. Scoring runs against your tenant only. |
| Communications | Activity is read as metadata only: type, date and which opportunity it belongs to. Subject lines, message bodies and call content are never queried or stored. |
Pick a plan and connect a read-only slice of your Salesforce. Your first deterministic commit review is ready inside 24 hours, on your own pipeline. If you would rather see it before you decide, book a call and I will take you through the scoring and the methodology on the demo tenant, and answer honestly whether it fits how your pipeline behaves.
From €249 a month. Read-only Salesforce connection. Month to month, disconnect at any time.