u/ponziedd

Hey folks,

As a builder in the B2B tech space, I've noticed how much sprint time is wasted when technical support and eng teams have to play "detective" across 5 different tools just to understand what went wrong with an incomplete tickets.

I'm exploring a tool that does the first part of technical investigation automatically: when a ticket lands in Helpdesk / slack, it pulls the relevant logs/DB records, checks what actually happened vs what the user reported, and hands Engineering a ready-to-fix Jira ticket with zero back-and-forth.

I am targeting late seed stage B2B startups that offer tech solutions,

I really want to challenge my assumptions with people who live this daily:

> How much time does your team actually spend digging for context before escalating a complex ticket?

> What observability / logging tools do you rely on the most (Datadog, Sentry, Grafana, custom internal admin)?

Any feedback or thoughts on this would be hugely appreciated!

Hey folks,

As a builder in the B2B tech space, I've noticed how much sprint time is wasted when technical support teams have to play "detective" across 5 different tools just to understand what went wrong with an incomplete ticket.

I'm exploring a potential solution, a read-only workspace that automatically reconstructs the evidence timeline behind a Zendesk ticket (queries logs/DB, identifies verified facts vs user statements, and drafts a structured escalation for Jira/Linear).

Before building further, I really want to challenge my assumptions with people who live this daily, I would love your perspective and learn from your experience

> How much time does your team actually spend digging for context before escalating a complex ticket?

> What observability / logging tools do you rely on the most (Datadog, Sentry, Grafana, custom internal admin)?

Any feedback or thoughts on this would be hugely appreciated!

I'm validating a problem around support engineers at mid-size companies (30-100 people) and trying to figure out how to actually get my first design partners lined up, people I can work closely with while building this out.

Quick context on the problem: support engineers spend a ton of time turning half-finished tickets into technical cases that engineers can actually dig into. On complex Tier 2 stuff the info you need is scattered everywhere, ticketing system, logs, monitoring tools, docs, past incidents. So they end up manually hunting all of it down before they can even escalate, which can eat 30-45 min per case.

If you've gotten early design partners before, curious how you did it. where did you actually find people willing to talk to you?

Right now my playbook is just manually outreaching my persona on LinkedIn, managing to get them on a call, and if there's a fit, I come back to them a week later to ask if having the company as a design partner is possible, routing me to the decision maker to see if something can be done

I don't know if this is the right way to do it or if there's a better approach out there.

Would appreciate any playbooks or communities that worked for you.

Hey everyone! I'm pretty new to Hermes and was wondering how I can set a fully autonomous workflow where my hermes manage my agency

I run an AI automation agency that serves SMB industrial businesses. I'd like to delegate most of my sales process to Hermes, specifically:

• Managing lead generation
• Running outreach campaigns
• Handling conversations until a lead replies
• Sending me a Telegram notification only when a lead responds, so I can take over the conversation manually

Is this something Hermes can handle? If so, what's the best way to set it up? Any tips, examples, or recommended integrations would be greatly appreciated.

I’m researching how B2B SaaS companies are deploying AI support agents and where those agents get the context needed to handle anything beyond basic questions.

A lot of important support knowledge seems to live across help-center articles, old tickets, Slack discussions, CRM notes, internal tools, and experienced teammates’ heads. Some of it may also be outdated or contradictory.

For companies already using or testing an AI support agent, is bringing this information together a meaningful challenge? Does missing context lead to wrong answers, avoidable escalations, or inconsistent treatment of customers?

I’m also curious about what happens after a human handles an escalation. Does that resolution become usable knowledge for the AI, or does the same type of case continue to escalate until someone manually updates the documentation?

I’m not pitching a product. I’m trying to determine whether this is a real operational bottleneck for growing B2B SaaS companies and how teams currently solve it.

As more companies introduce AI into customer support, I’m trying to understand how CX teams make sure those agents have enough company-specific context to make good decisions.

The official documentation often covers the standard process, but customer situations can depend on previous interactions, account history, special agreements, internal exceptions, recent policy changes, or knowledge held by experienced employees.

When the AI does not have that context, what impact do you see on the customer experience? Does it provide generic answers, make inconsistent decisions, create unnecessary handoffs, or force the customer to repeat information?

When a human eventually resolves the situation, is that knowledge captured in a way that improves future AI interactions?

I’m interested in real experiences from teams using or evaluating AI support agents. Is this a significant CX challenge, or are existing tools already handling it well?

Hey everyone,

I’m curious about how teams using Intercom Fin manage company-specific knowledge that goes beyond public help-center content.

For example, how does Fin handle customer-specific arrangements, exceptions approved by the team, changing policies, product quirks, internal workarounds, or decisions buried in old conversations and Slack threads?

Does missing or outdated context cause incorrect answers or escalations that a human could have avoided with the right information?

I’m also curious about the feedback loop. When a support agent takes over and resolves a complex conversation, does that resolution help Fin answer a similar question in the future, or does the team still need to manually turn it into an article or guidance?

I’m not selling anything. I’m trying to understand whether this is a genuine operational bottleneck for teams using Fin.

Hey everyone,

For those using or testing Zendesk AI, how do you handle cases where the answer depends on internal context that is not clearly documented?

I’m thinking about things like billing exceptions, previous ticket decisions, customer-specific promises, product workarounds, internal policies discussed in Slack, or knowledge that only senior agents have.

Does Zendesk AI have enough context to handle these cases accurately, or does it mostly work well for straightforward questions and then escalate anything more complex?

When an agent corrects an AI response or resolves an escalated case, does that knowledge become reusable for similar tickets, or does someone have to manually update the help center or another knowledge source?

I’m researching this workflow and would really appreciate hearing what works, what does not, and where the biggest maintenance burden currently sits.

Hey, I’m researching how Customer Support and Customer Success teams at B2B SaaS companies are using AI support agents.

I’m especially interested in what happens when the AI needs company-specific context that is not clearly documented, such as exceptions discussed in Slack, decisions from previous tickets, customer-specific agreements, product workarounds, changing policies, or knowledge that only experienced teammates have.

For teams already using or testing an AI support agent, how does it access this kind of context today? Does scattered or outdated knowledge cause incorrect answers or unnecessary escalations? What types of questions does the AI struggle with most?

I’m also curious about what happens after an escalation. When a human resolves a complex case, does that resolution help the AI handle a similar case next time, or does the same issue keep happening?

Is maintaining the agent’s knowledge a meaningful operational bottleneck for your team, or do your current tools handle it well?

Hey folks,

I’m currently validating a simple business idea: helping AI support agents access the context they are missing.

Today, important customer support knowledge is scattered across Slack, old tickets, wikis, CRMs, and people’s heads. Weppo would collect that knowledge, structure the relevant rules, exceptions, and decision logic, and make it reviewable and usable by the company’s AI support agent.

I’m still in the validation phase and currently building a lead list, but I’m struggling to identify and reach the right people.

Do you have any feedback on how I could improve my approach?

Cheers!

Exploring this and want honest take

The hypothesis > B2B support/CS teams have a context problem. When a complex escalation lands, writing the answer is easy, the hard part is figuring out what already happened: past tickets, what sales promised, whether eng already knows about the bug, who should even own this. That context lives across Zendesk, Slack, the CRM, Notion, and some senior person's head. So someone burns 20-45 min doing internal archaeology before anyone can respond.

The idea > ainternal layer that assembles all of that into an escalation packet, history, account context, past commitments, missing info, suggested owner, drafts the internal note, creates follow-ups, and writes the resolution back somewhere durable so the same digging doesn't happen next time. Explicitly NOT customer-facing automation.

Where I am > doing cold outreach + interviews with CSMs to validate.

What I'm unsure about: is the pain acute enough to pay for, or is this a "nice to have" t

Hey everyone, I’m trying to understand what actually sucks in CS day to day, not the polished LinkedIn version lol.

For people working in customer success, support, customer ops, etc, what are the problems that keep coming back again and again?

I keep hearing stuff around docs being outdated, reps always asking the same senior person, weird customer edge cases where nobody really knows the “official” process, messy escalations with product or eng, onboarding taking forever, Slack threads becoming the real source of truth, all that kind of stuff. But maybe I’m totally wrong.

What’s actually painful for you atm? Like what makes you think “why tf are we still doing it this way” during the week?

Joined a ~30 person B2B startup 4 months ago as a PM. The product is 3 years old, built entirely by the founders and eng team before I got there.

Every week I hit some version of the same wall. Last week a big customer asked why we handle permissions the way we do (it's genuinely weird). I spent close to 2 hours digging: Slack search, old Linear tickets, a Notion page last edited in 2023 that turned out to be describing a version of the feature that no longer exists. Eventually I just asked our CTO, who explained it in 3 minutes, turns out there was a solid reason involving an early enterprise deal. Nowhere written down. The guy who negotiated that deal left last year.

Multiply that by pricing decisions, killed features that sales keeps asking to revive (nobody remembers why they were killed, so we're about to re-litigate one of them for the third time), and me basically doing archaeology before every roadmap conversation.

What I've tried so far is

Start a "decision log" in Notion. I'm the only one who writes in it and I've already fallen 3 weeks behind

> Asking the founders to braindump, useful but they don't have time and they forget half of it until something triggers the memory

> Just accepting that I'll interrupt the CTO 5x a week (he's very patient but I can tell it's a tax on him)

Honestly starting to wonder if this is just... the job? Like this is what being the first PM at a startup IS and I should stop fighting it?

For those of you who joined an existing product rather than building from scratch, how long did it take before you stopped hitting this? Did anything actually work, or did you just slowly become the new tribal knowledge holder? And has anyone actually seen a decision log survive longer than a quarter?

Curious, What AI tools do you use for product management?

Hey, I recently started doing a market research learning about workflows of dental clinics in france, I am doing irl research going on local clinics and learn how they work, ect I am doing a mom test with them, I planned to do 10-15 irl interviews before taking a decision, then find 1-2 pilot users I can work closely with to build a tailored solution to their workflow, I am on the right path? I also fear that dental clinics have very regulated process so building a tailored solution can be challenging, maybe not, but I overthink a lot

would love your opinion, if I need to improve something on my process

Hey, I recently started doing a market research learning about workflows of dental clinics in france, I am doing irl research going on local clinics and learn how they work, ect I am doing a mom test with them, I planned to do 10-15 irl interviews before taking a decision, then find 1-2 pilot users I can work closely with to build a tailored solution to their workflow, I am on the right path? I also fear that dental clinics have very regulated process so building a tailored solution can be challenging, maybe not, but I overthink a lot

would love your opinion, if I need to improve something on my process

I’m trying to figure out if this is just something we keep running into, or if other people building agents are dealing with it too.

The agent side feels like it’s getting better. Tools like LangGraph, CrewAI, OpenAI Agents and others make the actual logic easier to build than before. But the annoying part for us is still getting the agent into Slack or Microsoft Teams in a way that feels natural for the team using it.

Every time we start a new project, we end up rebuilding a lot of the same stuff. Thread context, DMs versus channels, streaming responses, user permissions, OAuth, approvals before the agent takes actions, logs for debugging, and all the small things that make the agent actually usable inside a team chat.

It feels like the hard part is not always the agent anymore. A lot of the work is in the layer where the agent actually talks to people.

For anyone here building agents for real teams, have you had to build this layer yourself? Was the Slack or Teams integration more painful than expected? What part wasted the most time? Thread context, auth, streaming, permissions, deployment, or something else?

Hey, I’m exploring a side project idea and would love honest feedback.

The idea is for independent experts: coaches, consultants, course creators, paid community owners, workshop creators, etc.

The problem I’m looking at:

A lot of experts already have valuable knowledge spread across calls, courses, PDFs, Notion docs, community posts, templates, newsletters, and their own head.

But when clients/students/members get stuck, they often come back with:

quick clarification questions

repeated questions

“where’s that resource again?”

ect ect

The expert then becomes the manual support layer,

The idea is quite simple, is to turn the expert’s existing content, frameworks, calls, and resources into a branded AI copilot their clients/students/members can use 24/7.

Not to replace the coach or expert.

More like a structured support layer that helps users find the right resource, get basic guidance, understand the expert’s framework, and know what should wait for the next human session.

Would appreciate your feedback.

Hey, I’m exploring a side project idea and would love honest feedback.

The idea is for independent experts: coaches, consultants, course creators, paid community owners, workshop creators, etc.

The problem I’m looking at:

A lot of experts already have valuable knowledge spread across calls, courses, PDFs, Notion docs, community posts, templates, newsletters, and their own head.

But when clients/students/members get stuck, they often come back with:

quick clarification questions

repeated questions

“where’s that resource again?”

ect ect

The expert then becomes the manual support layer,

The idea: turn the expert’s existing content, frameworks, calls, and resources into a branded AI copilot their clients/students/members can use 24/7.

Not to replace the coach or expert.

More like a structured support layer that helps users find the right resource, get basic guidance, understand the expert’s framework, and know what should wait for the next human session.

Would appreciate your feedback.