Sales forecasting

Sales forecasting here is an automation that builds and maintains your sales forecast from the real deals in your CRM, instead of a spreadsheet someone rebuilds from memory each week. It reads every open opportunity with its stage, age, activity, and close date, weighs each one against your own historical close rates and the signals that predict a real deal, and rolls the pipeline up into an honest number with a range. It flags the deals most likely to slip or sandbag so you see the risk early. It is a grounded starting point for the number a person commits, not a black box you trust blindly.

A spreadsheet is only as fresh as the last time someone rebuilt it, and it usually reduces the whole pipeline to one optimistic figure. Built-in CRM forecasts often apply a flat percentage per stage, which treats a deal that has sat untouched for two months the same as one with a call booked tomorrow. This weighs each deal on real signals: age, activity, deal characteristics, and your actual close rates by stage, tuned to how your business really closes. And it shows its reasoning and a range instead of a single false-precise number, so you can see why the forecast is what it is and where the risk sits.

More honest than a gut-feel spreadsheet, and honest about its own limits. It does not promise a magic exact number, because a forecast is a projection and it is only as good as the CRM data behind it. What it does is weigh every deal on consistent, real signals instead of mood, and give you a range with the reasoning attached rather than one figure that looks more certain than it is. Accuracy improves as the underlying CRM hygiene improves, and part of the setup is tuning the model against your real past deals, both won and lost, so the range reflects how your pipeline actually behaves.

Yes, always. The automation gives you a grounded starting point: a weighted roll-up, a range, and a flagged list of at-risk and sandbagged deals. The sales leader reviews it and decides the number to commit. The forecast is a recommendation with its reasoning shown, not a verdict, and you can override any deal's weighting when you know something the data does not. This is deliberately not a black box that hands leadership a figure to repeat. It surfaces the signal and the risk so the person who owns the commit makes a better-informed call.

On the CRM side, Attio, HubSpot, Salesforce, Pipedrive, and Close, so it reads the deals where your reps already work. For deal and activity signals, tools like Salesloft and Gong. For delivery, it can post the forecast and flags into Slack, a Google Sheet or Excel file, or your BI dashboard, wherever leadership already looks. Most tools with an API can be connected. The audit maps your exact stack and picks the signals worth weighing for your market and sales motion.

Usually two to three weeks. The first days go to connecting your CRM, mapping your stages, and pulling your historical deals, both the ones that closed and the ones that did not, so the model learns your real close rates rather than a generic template. Then the forecast runs alongside your existing one for a stretch so you can compare the weighted roll-up against what actually lands. You go live once the number and its range hold up against your own quarters, which is what makes the forecast worth committing instead of second-guessing.

Two parts. Tooling runs as a monthly cost for the model usage and any deal-signal sources, typically a per-seat or per-pipeline range that scales with your deal volume. Implementation is a fixed scope, quoted once the audit maps your CRM, stages, historical deals, and how you want the forecast surfaced, so you are pricing a defined build rather than an open-ended retainer. The audit is where the scope and price get set against every other opportunity in your business. Priced as part of the audit.