Pipeline optimisation

Pipeline optimisation is an automation that watches every open deal in your CRM and flags the ones going cold: no recent activity, stuck in a stage too long, or missing a next step. It does the review a sales manager would do by hand, on a schedule, and hands each rep one clear next action on the deals that actually need attention, rather than a report of the whole pipeline. It also keeps the CRM data clean as it goes, so the pipeline reflects reality. The point is not more dashboards. It is making sure no winnable deal quietly dies because nobody followed up in time.

A CRM report shows you the whole pipeline and leaves the judgment to you, so you still have to scan it and decide what is at risk. Built-in reminders fire on rules you set once and then forget, and they treat every deal the same. This is tuned to your sales motion. It reads each deal in context, decides which ones are genuinely going cold, and sends only those, with the specific next action, to the rep who owns them, in Slack or the CRM they already use. Fewer, sharper prompts on the deals that matter, instead of a wall of data or reminders people learn to dismiss.

No. It does not send emails to prospects, change deal stages on its own, or close anything. It prompts a human. Every prompt names the deal, the reason it is at risk, and a suggested next step, and the rep decides what to do. The one thing it does touch directly is data hygiene: nudging a missing next-step field or a stale stage back into shape, and even that surfaces for the rep rather than happening silently. The sale stays with the person. The automation makes sure the person is looking at the right deal at the right time.

That is the honest constraint, and it is why setup starts there. The system is only as accurate as the records behind it. If deals sit in stages that no longer match reality, a dead deal can read as healthy and a live one can read as cold. So part of the build is defining what a clean record looks like for your team and adding the nudges that keep it that way, flagging deals with no next step or a stale stage so they get fixed. The system improves your data quality as a by-product of running, but it is not magic. If nobody logs deals at all, there is nothing to watch, and we will tell you that in the audit rather than sell you a monitor with nothing to monitor.

It reads from your CRM, whether that is Attio, HubSpot, Salesforce, or Pipedrive, and it delivers prompts where your reps already work, usually Slack or the CRM itself. It can also pull activity signals from email and calendar (Gmail, Outlook, Google Calendar) and from call tools like Gong or Fireflies to judge when a deal last had real contact. Most tools with an API can be added. The audit maps your exact stack and picks the signals that best tell a moving deal from a stalling one for your specific sales motion.

Usually one to two weeks. The first days go to connecting the CRM, defining what at-risk means for your sales motion, and setting the data-hygiene rules. Then the prompts run against your live pipeline for a few days so the thresholds get tuned before reps rely on them, because a prompt that fires too often gets ignored and one that fires too rarely misses the save. You are live quickly, and that short tuning window is what makes the prompts worth acting on instead of another notification people mute.

Two parts. Tooling runs as a modest monthly cost for the CRM reading and model usage, typically a per-user range that scales with team size. Implementation is a fixed scope, quoted once the audit maps your CRM, your sales motion, and how you want prompts delivered, so you are pricing a defined build rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, so you are not guessing at effort up front.