Meet the 5 AI Agents Watching Your Pipeline 24/7
Reddy is not a single AI assistant. It is a team of five specialised agents, each with a distinct role in your revenue operation. Here is who they are and what they do.
Most AI tools for sales teams work like a smart search engine. You ask a question, you get an answer. You close the tab, and that is the end of the interaction.
Reddy is built differently. It is not one AI — it is five. And they do not wait to be asked.
Each of Reddy’s agents has a distinct role, a distinct focus, and a distinct personality. Together, they cover your entire revenue operation: pipeline health, forecasting, competitive intelligence, rep performance, and data quality.
Here is who they are.
Parker — Pipeline & Deal Strategy
The agent that watches every deal, every day.
Parker is your deal desk at scale. Every morning, Parker runs through every open opportunity in your CRM, calculates activity scores, flags stalled deals, and surfaces the ones that need attention — before your rep realises they are in trouble.
Parker’s core signal is activity decay. Not whether a deal has the right stage or the right amount in the dollar field — those are lagging indicators. Parker watches what is actually happening: emails sent, calls made, meetings booked, proposals opened.
When Parker sees a deal go quiet, it does not wait for your weekly review. It sends a Slack message that morning:
Parker here. Heads up: the Acme Corp deal has had no outbound contact in 11 days. Last email from the rep went unanswered. The Q2 close date is 19 days away. Want me to draft a re-engagement message?
Parker also helps reps think through deal strategy. Ask it about a specific opportunity, and it will pull the activity history, identify the gaps, and suggest the next best action.
Frankie — Forecast & Strategy
The agent that tells you how much confidence to have in your number.
Frankie owns your pipeline forecast — not just the headline figure, but the composition behind it. Every forecast is a portfolio of risks, and Frankie makes those risks visible.
Each week, Frankie delivers a forecast update that goes beyond the rollup. It tells you:
- Which deals are driving the most uncertainty
- How much of your forecast is in deals with declining engagement
- Whether your pipeline coverage ratio is sufficient to hit the number
- How the forecast has changed week-on-week, and why
Frankie also tracks historical accuracy. If your “Commit” stage historically closes at 78%, but you have been booking it at 100%, Frankie will flag the discrepancy. Over time, it learns the patterns specific to your pipeline and builds a model calibrated to your company’s reality — not an industry benchmark.
When you are sitting in front of the board and someone asks “how confident are you in that number?”, Frankie gives you an honest answer.
Maya — Market & Competitive Intelligence
The agent that monitors what is happening outside your deals.
Maya listens for signals that most RevOps functions never track: competitive mentions in deal conversations, market timing signals, champion changes, and organisational moves.
When a rep mentions a competitor on a call, Maya picks it up and adds it to the competitive intelligence feed. Across dozens of deals, this builds a live picture of where you are winning and losing by competitor — and why.
Maya also monitors deal context that lives outside the CRM. When a prospect company announces a major restructure, a funding round, or a new initiative that changes their priorities, Maya surfaces it and links it to the relevant open deals.
The insight Maya delivers most often: “This deal has been quiet since the prospect’s CFO announced a budget freeze two weeks ago. Three other deals in similar situations resolved within 45 days. Here is what worked.”
Riley — Rep Performance Coach
The agent that coaches reps in private.
Riley is different from the others. It does not post in shared Slack channels. It does not surface rep performance data in public briefings. Riley works exclusively in 1:1 Slack DMs with each rep.
This is intentional. Coaching is most effective when it is private, specific, and timely — not when it is a slide in a Monday morning review that the rep has to sit through with their peers.
Riley monitors each rep’s activity patterns and identifies the gaps. Not in a punitive way, but in the way a great manager would if they had unlimited time to watch every call and review every deal.
A rep gets a DM from Riley after a lost deal:
Hey Alex — I noticed the Brightfield deal closed lost today. Looking at the activity history, the last meaningful touchpoint was your demo 18 days ago, and there was no follow-up meeting booked. I’ve seen this pattern in 4 similar deals over the past 6 months. Would it help to look at what the winning follow-up playbook looks like for mid-market SaaS? I can pull some examples.
Riley also captures win/loss data automatically. When a deal closes — won or lost — Riley sends the rep a quick DM asking what happened. Structured, quick, frictionless. The data goes directly into your pipeline intelligence system.
Dana — Data Quality Guardian
The agent that keeps your CRM honest.
Dana is the unglamorous one. Nobody talks about data hygiene at the all-hands. But bad CRM data is the silent killer of every RevOps initiative — forecasts built on stale close dates, risk scoring based on deals that should have been closed-lost months ago, pipeline reports that include zombie opportunities nobody has touched in two quarters.
Dana audits your CRM continuously. It flags:
- Deals with no activity in the past 30+ days
- Close dates that have been pushed out more than twice
- Opportunities missing key fields that affect scoring and forecasting
- Stage mismatches (a deal in “Verbal Commit” with no recorded executive engagement)
Dana does not just flag problems — it suggests resolutions. For each data quality issue, it generates a proposed update and routes it to the rep with context: “This deal has been in Proposal Sent for 47 days with no activity. Is it still active? I can archive it or update the close date if you confirm.”
The result: a CRM that actually reflects reality. Which means every other agent — Parker, Frankie, Maya, Riley — is working with clean data.
How They Work Together
The magic of Reddy is not any one of these agents. It is that they share context.
When Parker flags a deal at risk, Frankie immediately factors it into the forecast update. When Maya surfaces a competitive signal, Parker can incorporate it into deal strategy recommendations. When Dana identifies a data quality issue, the fix ripples through every model that uses that data.
And everything surfaces in the place your team already works: Slack.
You do not need to log into another platform, build another integration, or train your team to use a new tool. The agents work in the background. They surface to you when something needs your attention. The rest of the time, they are watching so you do not have to.
Reddy is currently in closed beta. Join the waitlist at getreddy.io to be notified when spots open.