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AI for Sales

Pipeline & Forecasting

Forecast day has a familiar shape. The number comes out of the CRM, everyone knows it is wrong, and the next hour goes to a call where each rep talks through their deals and the leader mentally applies a discount. The deals that hurt are not the ones marked at risk. They are the ones sitting at 80 percent with no contact in three weeks and a champion who stopped replying in April.

The signals were all sitting in tools nobody cross-referenced. Skynet’s unified memory puts the CRM, the calendar, and the email threads in one place, so an agent can compare what a deal claims to be against what has actually happened on it — and raise a hand before the quarter closes.

How it works

step 01

Read the pipeline against reality

The agent pulls every open opportunity and checks it against the evidence: days since last contact, whether a meeting is on the calendar, whether the economic buyer has ever replied, how the stage compares to deals that closed.

step 02

Flag the slippage early

Deals that have gone quiet, skipped a stage, or lost their champion get surfaced with the reason. Not a red dot — a sentence explaining what changed and when.

step 03

Build the forecast from evidence

Rather than summing stage probabilities, the agent weights each deal on how it compares to your own closed-won history. You get a range and the assumptions behind it, so you can push back on the parts you disagree with.

step 04

Deliver it before the meeting

A weekly digest lands ahead of the forecast call: the number, the deals that moved, the deals at risk, and what each one needs. The call becomes a decision, not a data-gathering exercise.

Build it from a prompt

Ask for the review you wish someone had already done.

You get a forecast you can defend and a list of deals that need attention while attention still helps. The agent does the cross-referencing every week without being asked; the calls go to deciding what to do about it.

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