Plain, the API-first customer infrastructure platform for B2B SaaS, built this guide for the engineer who didn't sign up to be a support agent, but somehow became one.

You know the pattern. A customer DMs an engineer who helped them last month. The engineer answers because it's faster than explaining where to go. It happens again. Then again. Suddenly half your engineering team is doing untracked, unmeasured, invisible support work and customer messages are still falling through the cracks.

In analysis of 619 B2B sales conversations in 2025-2026, the single most common pain trigger was exactly this: in roughly 40% of cases, teams adopted a support platform because customer messages were getting lost — in Slack DMs, buried threads, and inboxes nobody was watching. No ownership, no tracking, no system.

This guide walks you through how to fix it. Step by step, without alienating customers or turning support into a bureaucratic mess.

Why Do Customers DM Engineers Instead of Using Support Channels?

It starts with a reasonable impulse. Your customer had a problem. They knew an engineer by name. A DM felt faster and more personal than filing a ticket into a void.

According to Forrester's 2025 CX Index, 80% of B2B interactions now happen digitally, and a growing share happen inside messaging platforms customers already use daily. When your buyers live in Slack, and 54% of B2B buyers now prefer live Slack communications for resolving issues, DMing the person who onboarded them isn't rude. It's rational.

The problem isn't the customer's behavior. It's the absence of a better path. If there's no obvious place to go, customers will find the person who last helped them and send a DM. Every time.

What Does Ad-Hoc Support Actually Cost Your Engineering Team?

The cost isn't obvious because it's measured in lost focus, not dollars, until you do the math.

Dr. Gloria Mark's research at UC Irvine found that it takes 23 minutes and 15 seconds to regain deep focus after a single interruption. Developers average 31.6 interruptions per day. The median engineering team achieves just 4.2 hours of focused work per day — barely half of an eight-hour workday, according to LinearB's 2026 benchmarks analyzing 8.1 million pull requests across 4,800 teams.

It gets worse. An IDC report found that application development accounts for only 16% of developers' time. The rest goes to operational and supportive tasks. Interrupted tasks take twice as long to complete and contain twice as many errors. And Atlassian's 2025 survey of 3,500 engineers ranked context switching between tools as the #3 productivity killer for developers.

The financial impact: at an average developer cost of $83/hour, losing 1-2 hours per day to support interruptions adds up to roughly $50,000 per year per engineer. For a team of five engineers each fielding a few customer DMs per day, that's a quarter million dollars in lost engineering output.

Across 619 sales conversations in 2025-2026, 59% of B2B support teams reported high-severity pain with their current tools or lack thereof. The pain is real and widespread.

The 5 Signs Your Team Has an Ad-Hoc Support Problem

Before you can fix it, you need to see it. These are the patterns that surface consistently across B2B SaaS teams:

1. Engineers answer customer questions in DMs more than once a day. If it's happening daily, it's not a one-off, it's an unacknowledged support channel.

2. You've lost a customer issue because it was buried in a Slack thread. A DM from two weeks ago, never followed up on. A thread in a shared channel that nobody was assigned to. These are the "cracks" that messages fall through.

3. Nobody knows who "owns" a customer problem until it escalates. When a customer follows up and asks "any update?", the team scrambles to figure out who was handling it.

4. The same questions get answered differently by different engineers. Without a knowledge base or shared context, every answer is improvised. Customers get inconsistent information.

5. You can't report on support volume, response times, or resolution rates. If your support happens in DMs, you have zero data. You can't staff for what you can't measure, and you can't improve what you can't see.

If three or more of these sound familiar, you don't have a people problem. You have a systems problem.

How to Move From DMs to a System (Without Alienating Customers)

This is the practical part. Five steps, roughly in order, each one building on the last. Most teams can get through Steps 1-3 in a week.

Step 1: Audit Where Support Actually Happens Today

Before you change anything, map the reality. Open Slack and look at where customer conversations are actually happening:

• DMs between individual engineers and customers

• Shared Slack Connect channels (if you have them)

• Internal channels where engineers discuss customer issues

• Email threads to support@ or individual inboxes

• Linear, Jira, or GitHub issues filed by customers or on their behalf

Count it. How many support conversations happen per week? Which engineers are handling the most? What percentage happens in DMs versus channels?

This audit is usually eye-opening. Among B2B SaaS teams we spoke with, channel fragmentation, support scattered across Slack, email, and project management tools with no unified view, was a factor in nearly a third of support tool evaluations. Most teams don't realize how fragmented their support is until they map it.

Step 2: Create a Single Intake Point Customers Will Actually Use

The goal is simple: give customers a place to go that's better than DMing an engineer. "Better" means faster responses and visible accountability.

Option A: Dedicated Slack Connect channels. One channel per customer (or per customer account). This is the most common pattern for B2B SaaS. The channel is visible to your whole support team, not just one engineer.

Option B: A shared support channel. For smaller teams or products with many smaller customers, a single #support channel with clear guidelines works well.

Option C: Email + Slack. Some customers prefer email. The system should handle both: messages from any channel land in the same queue.

The key is making the new path the path of least resistance. If your shared channel responds in 10 minutes but a DM to an engineer gets a reply in 2 hours (because the engineer is coding), customers will learn fast.

The redirect message. When a customer DMs an engineer, the engineer sends something like:

"Hey — I want to make sure this gets the right attention and doesn't get lost in my DMs. Can you drop this in #[channel-name]? The whole team can see it there and you'll get a faster response. I'll keep an eye on it too."

This works because it frames the redirect as better for the customer, not as a bureaucratic requirement.

Step 3: Add Structure Without Adding Friction

A channel alone isn't a system. You need three things on top of it:

Ownership. Every customer message gets assigned to someone. This is the single most important change. When nobody owns a problem, everybody assumes somebody else is handling it, then nobody does.

Status tracking. Is this open, in progress, waiting on the customer, or resolved? Even a simple status prevents the "any update?" follow-up loop.

Routing. Which messages need an engineer, and which can be handled by someone on the support team (or by an AI agent)? A basic triage layer keeps engineers focused on the problems only they can solve.

You can build this manually with Slack workflows, emoji reactions, and discipline. But most teams outgrow the manual approach within a few weeks. A dedicated tool turns messages into tracked threads with assignments, SLAs, and status without asking customers to change their behavior.

For teams doing Slack-based B2B support, the third option tends to win once volume exceeds a few dozen conversations per week.

Step 4: Protect Engineering Time With Explicit Boundaries

Moving support into a channel solves visibility. But engineers still get pulled in for escalations — and without boundaries, "escalation" means "every hard question."

Set up office hours for engineering escalations. Designate specific windows (e.g., 10-11am and 3-4pm) when engineers review and respond to escalated support threads. Outside those windows, non-urgent requests wait. This gives engineers predictable, protected focus blocks.

Research backs this up: 90% of developers who get 2+ hour uninterrupted blocks report both higher productivity and better code quality. The office-hours model protects those blocks.

Define tiers clearly. What the front-line support person handles (account questions, known issues, configuration help) versus what needs an engineer (bug reproduction, architecture questions, integration debugging). Write it down. When tiers are explicit, fewer things get escalated unnecessarily.

Default to async. Engineers don't need to respond to support in real-time. Batch processing (reviewing escalated threads twice a day) is far less disruptive than responding to each ping as it arrives. A well-structured support thread with customer context, reproduction steps, and prior troubleshooting means the engineer can resolve it in one focused pass.

This is the shift from interrupt-driven support to multithreaded, team-to-team support — where the whole team has context and engineers contribute without being the first responder.

Step 5: Automate the Questions Engineers Should Not Be Answering

Once you have a system, you'll see patterns. The same questions come up every week: "How do I reset my API key?" "What are your rate limits?" "How do I connect to our SSO provider?"

These are not engineering problems. They're documentation problems. And in 2026, AI handles them well.

Gartner predicts that conversational AI will reduce contact center agent labor costs by $80 billion by 2026. Salesforce's 2025 State of Service report found that 30% of support cases are now resolved by AI, with that number expected to reach 50% by 2027. For routine questions with clear answers, AI deflection is no longer experimental, it's table stakes.

Options for automating repetitive support:

• AI agents that answer from your docs. Tools like Plain's Ari, or bring-your-own-agent (like Parahelp, Inkeep) using the API and machine users, can respond to common questions directly inside Slack based on your knowledge base.

• Canned responses and templates. For questions that need a human touch but follow a pattern, pre-written auto-responses save time and ensure consistency.

• Self-serve knowledge bases. A searchable docs site reduces inbound volume before it reaches any channel.

Plain's AI Help Center includes an AI chat surface to find answers quickly, full documentation with articles generated from resolved threads, and auto-responders with escalation.

The engineers on your team are the real 10x engineers when they're building product, not when they're answering the same configuration question for the fifth time this week.

What Tools Help You Make This Transition?

If you're evaluating tools to structure Slack-based support, here's a factual comparison of the main options as of February 2026:

Plain is an API-first customer infrastructure platform that unifies Slack, Teams, Discord, email, and in-app support. Used by Vercel, n8n, and Raycast, Plain's GraphQL API gives engineering teams full programmatic control over their support workflows — including custom integrations, data pipelines, and bringing any AI model. If your team values building on top of infrastructure rather than being locked into a vendor's workflow, Plain is built for that.

ClearFeed is a Slack-native support platform focused on converting Slack conversations into trackable tickets. It's strong for teams whose support is primarily Slack-based with less need for other channels.

Slack native (Workflow Builder + emoji reactions + manual processes) works for very early-stage teams handling fewer than 10-20 support conversations per week. Beyond that, the lack of SLAs, reporting, and assignment usually becomes the bottleneck.

For a deeper comparison of Slack support tools, see our guide to the best Slack apps for B2B support.

How Teams Like Vercel, n8n, and Raycast Handle Support at Scale

These are engineering-led companies with highly technical user bases, exactly the kind of team where engineers get pulled into support.

Here's how they structure it.

Fly.io consolidated support across multiple channels into a single platform, giving their team a unified view of every customer conversation whether it came from Slack, email, or in-app. Resolution time for the Enterprise Tier customers fell from 6 days to 2 hours.

n8n is an open-source workflow automation platform with both a community edition and enterprise customers. Their support team handles a mix of community questions and enterprise accounts, using Plain to route and prioritize across channels. The API-first approach lets them build custom workflows that match how their engineering team already works.

Raycast builds a productivity tool for developers — meaning their users are engineers themselves. Their support team uses Plain to manage conversations across channels while keeping response times fast. When your customers are developers, they notice slow or disorganized support immediately.

All three teams use Plain as their support infrastructure. What they have in common: they moved away from ad-hoc Slack conversations to a system with tracking, ownership, and routing — without sacrificing the speed and informality their customers expect.

FAQ

How do I redirect customers from DMs without being rude?

Send a brief, friendly message: "Hey — I want to make sure this gets the right attention and doesn't get lost in my DMs. Can you drop this in #[channel-name]? The whole team can see it there and you'll get a faster response." Most customers appreciate it once they see faster replies. The key framing: you're doing this for them, not to them.

Should engineers do any customer support at all?

Yes — for L2 and L3 escalations that require deep technical context. The goal isn't to remove engineers from support entirely. It's to route the right questions to them through a system, not through ad-hoc DMs. Engineers should handle the problems only they can solve, and a triage layer should handle everything else. As we wrote in 2026 is the year of the support engineer, the role of the technical support person is becoming more important, not less.

What's the fastest way to set this up if I'm a small team?

Start with Slack Connect channels (one per customer) and one tool that turns messages into tracked threads with ownership. You can get a basic system running in a day. Add SLAs, automation, and AI deflection as volume grows. The most common path to a dedicated support platform isn't switching from another tool — it's teams that never had a system at all, running support through Slack DMs, Linear issues, and shared inboxes.

How do you measure whether this is working?

Track four metrics: median first response time (are customers waiting less?), median resolution time (are problems getting solved faster?), engineer interruptions per week (are engineers getting focus time back?), and percentage of requests resolved without engineering escalation (is the triage layer working?). If the first three go down and the last one goes up, the system is working.

Can AI really handle technical B2B support questions?

For 30-50% of common questions, yes. Salesforce's 2025 State of Service report found that 30% of support cases are now resolved by AI, with that number expected to reach 50% by 2027. Complex debugging, architecture questions, and edge cases still need humans. But routine questions about configuration, billing, API usage, and known issues are ideal for AI deflection. The key is connecting your AI agent to your actual documentation and knowledge base — not deploying a generic chatbot.

For a deeper look at AI-powered support options, see our guide to AI support tools for B2B SaaS.

What if my team is too small for a dedicated support person?

That's exactly when structure matters most. A system doesn't require headcount — it requires a single intake point, tracked ownership, and basic routing. Many B2B SaaS teams run effective support with engineers on a weekly rotation, as long as the work is visible, assigned, and measurable. The rotation model works because each engineer knows when they're "on support" and can protect their other days for deep work.