Cutting support handling time by two thirds
Illustrative example. Figures are representative targets, not a specific client claim.
Picture a support inbox on a Monday morning: a few hundred tickets banked up over the weekend, most of them variations on the same two dozen questions, and a handful of genuinely urgent ones buried somewhere in the pile. The team clears it by brute force, and by the time the tricky cases surface, the customer has already waited a day. This playbook is how we'd take that weight off a B2B SaaS support team without letting a model loose on your customers unsupervised.
Where the time actually goes
In a team fielding around 400 tickets a week, most of the day disappears into low-complexity, high-repetition work: password resets, billing and invoice questions, plan changes, order and shipping status, and the same handful of how-to questions the docs already answer. Each one is a two-minute job, but at a few hundred a week those minutes add up to entire full-time roles spent on work that never gets more interesting.
The expensive part isn't the volume, it's the queue structure. When everything lands in one undifferentiated inbox, a first-response SLA that matters, an angry churn-risk customer, an enterprise account with a real bug, sits behind a stack of routine password resets. First-response time slips not because the team is slow but because the important tickets can't be seen through the noise.
So the goal isn't to replace agents. It's to clear the routine layer automatically and hand your people the tickets that actually reward human judgment, with the context already gathered.
What we'd build, and how it works
We put an LLM triage-and-draft layer in front of the queue. Every incoming ticket is classified by intent and urgency, then, for the common categories, we retrieve the relevant answer from your own help center and past resolved tickets and draft a grounded reply in your tone. The draft, the classification, and the sources it used all land on the ticket so an agent can see exactly why the model said what it said.
Grounding is the whole game. The model doesn't answer from its own memory; it answers from your content via retrieval (RAG), and if it can't find a confident source, it says so and routes to a human rather than inventing an answer. That single rule is what keeps this from becoming a hallucination machine. Tickets it can't classify confidently, or that touch billing disputes, cancellations, or anything with legal or account-security weight, are routed straight to a person, never auto-answered.
Everything runs through your existing help desk via its API, so agents keep working in the tool they already know. There's no new interface to adopt, the automation just makes drafts appear and the queue arrive pre-sorted.
Rollout: earning trust before it sends anything
Nothing auto-sends on day one. We start in a monitored draft mode: the model classifies and drafts, an agent approves or edits every reply, and we log agreement rates per category. This does two things, it protects your customers from a cold-start model, and it produces the accuracy data that tells us which categories are actually safe to automate.
Once a category clears an agreed accuracy bar on your own tickets, over a real sample, not a demo, it graduates to auto-send, while everything else stays in draft. Scope widens category by category as the evidence supports it, so automation grows into the tail of easy questions first and never reaches the sensitive ones by accident.
The 60–65% auto-handled figure is a target this pattern reaches for, not a guarantee. It depends on how repetitive your ticket mix really is and how good your help content is; part of the early work is measuring your actual distribution so the projection is grounded in your data.
Results we build toward, and when not to do it
The outcomes we design for are concrete: a large share of routine tickets resolved or drafted automatically, first-response time cut sharply because the routine layer no longer blocks the queue, and agents redeployed onto the complex, retention-critical work that was previously waiting in line. Deflection and first-response time are measured from day one, so the value is visible rather than asserted.
We're also honest about the ceiling. If your ticket volume is low, or every ticket is genuinely bespoke, the math doesn't work and we'll tell you so. If your help content is thin, the grounding has nothing to stand on, and fixing that content is the first project, not the model. And any category where a wrong answer is expensive, security, legal, financial disputes, stays human by design. This is a human-in-the-loop system that removes drudgery, not accountability.
You own the whole build: it runs in your help desk and your own automation accounts, it's documented so your team can adjust the prompts and routing rules themselves, and there's no lock-in to us. If you part ways with us, the workflow keeps running.
A support team was manually answering ~400 repetitive tickets a week, with slow first responses hurting CSAT.
We deployed an LLM triage-and-draft workflow: classify by intent and urgency, auto-draft replies for common cases, and route the rest to humans with full context.
Around 60–65% of tickets handled automatically, first-response time down sharply, and agents freed for complex work.
Week 1 — Map the queue
We pull a real sample of your tickets, cluster them by intent, and measure the actual distribution so we know which categories are worth automating and what deflection is realistic.
Week 2 — Prototype triage & grounding
We build the classification and RAG layer against your help center and resolved tickets, and test drafts offline on historical tickets before anything touches the live queue.
Weeks 3–4 — Draft mode in production
The workflow goes live in monitored draft mode. Agents approve or edit every reply while we log per-category accuracy and tune prompts and retrieval.
Weeks 5–6 — Graduate & hand over
Categories that clear the accuracy bar move to auto-send; the rest stay in draft. We document the build, set up monitoring and alerts, and hand it over so your team owns it.
- Triage and draft first, auto-send only after per-category accuracy proves out on your real tickets.
- Ground every reply in your own help content via retrieval, so the model cites sources instead of inventing answers.
- Keep billing, security, and legal categories human by design, this removes drudgery, not accountability.
- Measure deflection and first-response time from day one, and you own the whole build with no lock-in.
Common questions
Will customers get answers written by a bot without anyone checking?+
Not unless you decide they should, and only after the model has proven itself. Everything starts in draft mode with an agent approving each reply. A category only moves to auto-send once its accuracy clears an agreed bar on your own tickets, and sensitive categories stay human permanently.
How do you stop it from making up answers?+
The model answers only from your help content and resolved tickets via retrieval, not from its own memory. If it can't find a confident source it routes to a human instead of guessing, and every draft shows the sources it used so agents can verify.
Do we need to switch help desk tools?+
No. The workflow connects to your existing help desk through its API, so agents keep working where they already do. Drafts appear on the ticket and the queue arrives pre-sorted, with no new interface to adopt.
What if our help documentation is out of date?+
Then that's the first thing to fix, and we'll say so honestly. Grounding is only as good as the content behind it, so we'll flag the gaps we find during the queue mapping. Often the automation project doubles as the push that finally gets the docs current.
Not sure which applies to you?
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