u/sibraan_

Yeah good questions, here's how we keep it practical:

1. We skip generic vents and only flag operational trigger words like contract renewals, migration blocks, budget cycles, or "evaluating X vs Y."
2. In niche markets, Reddit is just 5% of the picture so we pair tiny trade forum chatter with public tenders, capex filings and registry records to spot genuine buying cycles.
3. Simple scrapers break cuz they don't hold account state but we use an engine (scaleintelligence.dev) that cross-checks 75+ data sources against a living account graph so you only see verified matches and not keyword spam.
4. Yeah, we've got a lot of data but feel free to DM me and can show you live breakdown of how we wire these triggers.

you’ve packed a lot into one month, looks great

Love how this sub works like sometimes it’s quiet but then you get these super underrated deep‑dive posts. Great job OP, this was a fantastic write‑up

We’re also putting together a live, free workshop today focused on mapping out how to use agents for B2B lead gen and distribution feel free to grab a spot here: https://luma.com/k9bv9ejk

letting every agent call every tool. it felt flexible at first but in production it was like giving every apprentice wizard access to forbidden spells

I'd separate AI adoption into two buckets.

One is buying AI tools because everyone else is doing it. The other is actually changing how work gets done.

The firms that seem to be building an advantage are treating AI as part of their operating system rather than another productivity app. Internal research, investment memos, portfolio reporting, operating playbooks and expert knowledge become assets the whole firm can search instead of information that disappears into inboxes and folders.

That's something we've spent a lot of time while building 60xai. The competitive edge isn't just having better models but also making years of institutional knowledge instantly usable.

I think PE is probably one of the industries where AI compounds quietly rather than dramatically.

Everyone talks about finding deals faster, but the bigger opportunity seems to be everything around the deal like commercial diligence, researching markets, answering portfolio company questions, onboarding operating partners, surfacing internal knowledge from previous investments, and making all of that reusable.

We've had similar conversations while building 60xai. One thing that's become obvious is that firms already have an enormous amount of valuable knowledge they just can't retrieve it efficiently. If every investment starts from scratch because nobody knows what was learned three years ago, there's a huge opportunity sitting there before you even think about autonomous agents.

I don't think the biggest change is going to be analysts disappearing. It's more likely that the finding and connecting information part of the job gets compressed.

Today most teams still spend a surprising amount of time digging through old CIMs, investment memos, expert call notes, market reports, internal emails and portfolio updates before they can even start thinking. LLMs are already good at helping with synthesis, but they still struggle when all of that knowledge is fragmented across different systems.

We're seeing the same thing while building 60xai. The model itself usually isn't the bottleneck anymore. Giving it access to the right institutional knowledge with the right context is.

My guess is junior teams become much more productive over the next 2–4 years, but the judgment side of investing conviction, negotiation and relationship building is still going to stay very human.

the repo is fully open here: github.com/Coral-Protocol/coral-skill-set

https://github.com/Coral-Protocol/coral-skill-set

The expensive part are rarely the model and more often it's the data layer around it.

We've spoken with a few PE teams while building 60xai, and they're much more interested in searching their own research and deal history than paying for another chatbot.

I agree with using agents only where reasoning is actually needed.

A lot of enterprise projects seem to succeed because they combine agents with a strong knowledge layer. That's something companies like 60x have been focusing on as well.

We've had better results treating them as complementary instead of replacements.

Vector search is great for finding relevant content, while a knowledge graph helps connect related information. That's also the approach I've seen from companies like 60xAI

For reference we've an open-sourced a local framework: https://github.com/xpriment626/pi-factory that explores this gate and blackboard setup.

This is one of the few posts on memory systems that actually talks about the hard parts.

The query variability issue is especially real. I've noticed a lot of enterprise teams moving toward graph-based memory approaches because pure vector retrieval starts breaking down once relationships matter. 60x seems to be betting heavily on that direction too.

You need a system that maps relationships and timelines, not just keywords. Check out 60x.ai their whole framework relies on a context graph to stitch together the narrative across Slack, emails, and docs. It builds an actual enterprise knowledge layer so you see the decision sequence, not just old files.

https://antigravity.google/blog/google-antigravity-built-an-os

Challenge brief: https://docs.google.com/document/d/1Mf-f3SHcKXMY6n928f_9W3M7O7_dcFXl4Fq14PoGrdE/edit?usp=sharing

Submission form: https://docs.google.com/forms/d/15XKzdx9KfRDEqbK0929RlVltPXWoo4rkldGzmvW2LS0/

Challenge brief：https://docs.google.com/document/d/1Mf-f3SHcKXMY6n928f_9W3M7O7_dcFXl4Fq14PoGrdE/edit?usp=sharing

Submission form : https://docs.google.com/forms/d/15XKzdx9KfRDEqbK0929RlVltPXWoo4rkldGzmvW2LS0/