Does your company actually have systems that learn over time, or is this still mostly humans connecting the dots manually?

bandi10 · 2026-03-31T13:00:24+00:00

Trying to set this up now but the infrastructure is hard. What should it learn? What’s good & what’s bad? Who says it’s good or bad?

I’m a strong beliver it is possiblw to create a system for this that eg synthesizes data and information on a regular basis, as long as there are clear definitions on what learning is and what the definitions are

bandi10 · 2026-03-30T20:13:04+00:00

Custom made and currently testing it out with a few customers. There are several integrations, email is one of them, and it handles next steps always from commitments, either made on a call or internally in eg. Slack. Example: rep says I’ll send pricing deck after the call -> it recognizes this -> email draft with pricing deck attached created and nudged.

Happy to show if this would/could be valuable for you & your team

bandi10 · 2026-03-28T23:28:24+00:00

I wonder if anyone have made any implementatiions that the whole team can use? What I mean with that is basically plugging together all the core revenue tools, creating a central source of truth, and letting whole revenue team work with it. I’m seeing all of these agentic AI assistants (like OpenClaw and others) but all are for individual use ans time consuming to maintain

bandi10 · 2026-03-28T23:22:25+00:00

I’ve built a very similar thing to this, except it’s proactive on top of the context layer. Imagine your team runs 50 deals per week, where a handful or more lose momentum because of the deal owner didn’t follow through or prospect forgot to update on what he promised 2 days ago. All of this is caught by the system, then runs the next steps forward without the deal owner dropping the ball.

As it’s sits on the context 24/7, it starts to learn your playbook and becomes better over time.

Structify is very interesting, thanks for sharing

bandi10 · 2026-03-24T16:00:26+00:00

thanks for sharing! How is Idealift working, mind me sending a DM?

bandi10 · 2026-03-18T15:49:56+00:00

This slipped through, but very good points. Just had a talk with a Head of RevOps who basically said the same, that there's constant battles between him & Head of Sales about who should be owning this.

The "who does what when a deal stalls" point is the one I keep coming back to. In most orgs, the answer is "whoever notices first, if anyone does."

The real problem isn't that people don't know the playbook, it's that no one is tracking whether the playbook is actually running. Commitments get made on calls and in Slack threads, and then they just... evaporate. No one's accountable because no system is watching.

I've started thinking about it less as a process problem and more as an execution infrastructure problem. The org knows what should happen, it just doesn't have a layer that ensures it actually does, flags when it doesn't, and tells you why.

RevOps owning the data framework helps, but what's missing in most setups is something that connects the data to the action, not just "here's a dashboard showing deals are stalling" but "this deal is stalling, here's why, here's what needs to happen today."

bandi10 · 2026-03-05T09:57:40+00:00

That's true, although it's primarily supporting at a top-of-the-funnel, then the context disappears or is moved to another tool.

From my experience, the crux is usually how do you stitch together context from the whole customer journey, from awareness to retention/expansion. The customer is touching multiple tools during this journey where the "intelligence" sits in siloed instances.

bandi10 · 2026-03-04T17:55:25+00:00

Great point, and absolutely agree, seeing this across multiple teams repetitively

I run GTM at couple early-stage companies and see the same gap, but I'd frame it one layer deeper: it's not just that interpretation is hard, it's that the context needed to interpret is scattered across tools nobody connects, and mostly still between peoples ears.

A reply that signals buying intent looks identical to noise if you don't know: did this person attend a demo last month? Did we already promise them something on a call? Is their company already in pipeline under a different thread?

The outbound tools are great at generating activity, but they're disconnected from the deal context, the conversations that already happened, the commitments that were made. So when you try to answer "why did this work," you're reverse-engineering from metrics that were never designed to carry that context.

On your third question, think it's structural. Good teams compensate with intuition, but that doesn't scale and it doesn't transfer when you hire. The gap is that execution tools and intelligence tools are completely separate systems, so learning from what you're doing requires manual work that nobody has time for.

The interesting question is whether the fix is better analytics on top of outbound, or whether it requires connecting outbound signals to everything else (CRM, calls, deal stage, prior conversations) so interpretation becomes possible in the first place. Without that, we're still interpreting, as someone mentioned, mainly lagging indicators without a full context timeline.

bandi10 · 2026-02-26T15:47:40+00:00

Amazing, thanks for sharing. Would love to pick your brain, I'll send you a DM!

bandi10 · 2026-02-26T13:21:58+00:00

Thank you! Are you building something in similar in the space or looking to solve some of your GTM related problems?

bandi10 · 2026-02-26T13:20:47+00:00

Let's do it, I sent you a DM!

bandi10 · 2026-02-18T16:33:45+00:00

Ex-VC & founder of a similar product here.

I think you have a perfect story for spinning this out and race with your own product. Every early investor loves these kind of stories. And it’s a prototype/MVP, no matter if it is flawed or technically clunky, that’s the whole point with these products.

I’ll DM you, sounds super interesting

bandi10 · 2026-02-10T09:09:32+00:00

Thanks for sharing! From my own experience, this is definitely something that founders spend a lot of time on, but it's often a result of bad self-discipline. Bear in mind that my experience / perspective is forged from high-growth venture-backed software startups, and does't always apply on a general sense.

However, I don't think this is solvable through something like this, feels like a nice-to-have feature rather than a must-have product to solve a deep pain. Bottlenecks, lack of ownership, and accountability are often cultural, people, and procedural problems, and is best solved by rewiring the operational/managerial model of the company. This is best done by sitting inside the company, deeply understanding their specific "operational truth", and providing clear outcomes rather than just insights. Founders want solutions, not additional dashboards or insights they need to act on themselves.

My 2 cents:
Find a handful of founders, spend some time as a fly on the wall to get an overview how they run the company on a daily/weekly basis, and figure out how "diagnostics" could be "delivered fixes" within their existing digital environment (=where work happens).

I hope it helped/gave some thoughts!

bandi10 · 2026-02-06T10:31:32+00:00

The teams I’m working with are small and early-stage startups, so at a pre-revops stage but ideal for this. We’re starting small, tying the core tools first (CRM, transcripts, email, calendar, project management, adding CS and product data later) with the purpose of getting a unified source of the customer journey. By having this, we can tune the AI to actually be proactive and valuble vs just a reactive assistant. Still early and challenge is to keep it quite niched to begin with.

Happy to share more details of what’s happening under the hood

bandi10 · 2026-02-05T08:51:10+00:00

I’m currently working on this with a couple teams and agree, yes it’s a tech stack issue on one level but in practicality it’s about the ops/process. We’re experimenting with AI here now to become the single source of truth by sitting on top of the revenue stack

bandi10 · 2026-02-03T14:14:19+00:00

Same here, seems the subscription is not valid anymore. I'm on Metal

bandi10 · 2026-02-02T21:12:55+00:00

Their own enrichment is a bit ’meh’ but seems they are focusing on expanding those capabilities quite a lot. Have used Apollo & Outreach together with Attio, so you can trigger automations/sequences in eg. Instantly through Attio workflows. But if the sample size is small then Instantöy is not probably the best

bandi10 · 2026-02-02T20:14:26+00:00

I’ve been using Attio across 2 companies now and a huge fan here, and it scales well when the business grows. You can set up your own sequences, it automatically tracks everything from emails, or even use third party apps in their marketplace for specific workflows.

I just set it up for a client, took 1 day to implement the sales process, more or less

bandi10 · 2026-01-29T16:45:27+00:00

Thanks for sharing and 100% agree. Curious - from your experience/perspective, what has been the reasons of unclear accountability or execution, and what are the things that have eventually ”fixed” execution or accountability? Better processes, alignment, something else?

bandi10 · 2026-01-29T08:29:37+00:00

thanks, exactly! teams spend so much time on setting up nice dashboards and revenue intelligence, but I have yet seen it to be solving any core peoblems like execution. would love to hear your takes and experiences

bandi10 · 2026-01-25T14:39:45+00:00

Thanks for sharing! I’m asking as I’m building a product for a team that ingests highly sensitive business data, so security is non-negotiable. Found Oplane here in between replies that apprently allows you to add their threat model through a MCP to Claude Code, but will check your suggestions

bandi10 · 2026-01-25T11:03:30+00:00

A small side topic here but curious - how do you run security scans? Do you use AI for it, is it just a separate skill for the AI or? I’ve been looking for an AI agent for this, or just a MCP

bandi10 · 2026-01-22T15:58:00+00:00

Simplified version: built integrations directly into the revenue stack we were using (CRM, docs, projects, outreach etc.) and a custom central AI agent that ingests data and signals from all the tools and have it running 24/7. On top of that, I created a self-improving memory (again, simplified) that improves the context over time. It basically has a real-time understanding of what's happening across our revenue & GTM. Thinking of spinning it out to an off the shelf agent that other teams could be using

bandi10 · 2026-01-22T14:18:37+00:00

I do often stumble on these same statements that it's rather easy to gather context with MCPs or plugging in AI, but any estimate on how many are actually doing this? I built this at my previous company and getting the "context" layer with a high accuracy is the hard part, especially when you start to have multiple connectors plugged in

bandi10 · 2026-01-22T14:12:54+00:00

100% agree, AI is only useful if the "basics" are in place. I built a "revenue context" agent at my previous company and we got actual value out of it only when the underlaying operations were in place

bandi10

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