Agent · support
Support scales by quality,
not by adding operators.
The typical support story: more tickets — hire people; SLA drops — hire more. An endless race where headcount, budget and mistakes grow but quality doesn't. The support agent closes typical questions itself using the knowledge base, classifies inbound, prepares drafts for operators and escalates complex cases with full context. Operators stay on what really requires their expertise.
Typical questions closed by the standardNot «luck of the operator», but a single quality answer — even at 3 AM on a weekend.
You see causes, not countsThe support lead finally sees «what clients are aching about», not «how many tickets we closed this week». A business signal, not an operational metric.
Operators — only on the hard stuffBefore: 80% of the day on «how to reset my password». Now: 80% on problems where a human is really needed. Churn drops, expertise grows.
Why we built this — and why «more operators» doesn't work.
Support in many companies is organized the same way: lots of tickets → hire people → churn → hire more. This isn't scaling, it's endless compensation for leakage. Costs grow linearly with volume, operator churn rises, answer quality drops (new hires), and the leader sees ticket count and concludes «we're at capacity».
At the same time the load structure everywhere is the same: 60-80% of inbound is typical, with answers already in the knowledge base. The operator types them by hand, gets tired, makes small errors, loses focus after 4 hours and escalates what shouldn't be. Hard cases drown under a flood of simple ones.
The support agent does what shouldn't be human work in the first place: closes typical questions by the standard and escalates the hard ones with full context. The operator stops being «the machine part that types» and becomes what they should be — an expert for hard cases. Answer quality stops depending on who's on shift today.
And why now
Support after a year with the agent — is already a customer pain map.
Over a year the agent accumulates a unique asset — a structured picture of what your customers are aching about. Not «ticket counts», but patterns: which product features generate questions, which documentation is stale, where employee training is weak. This is support analytics that used to require a special research project. You get it as a side effect of daily work.
Six effects — at the company level.
The support agent doesn't change an operator's «productivity». It changes the scaling logic of the entire support department: quality over quantity, causes over tickets, expertise over churn.
SLA doesn't depend on load
When inbound doubles, it's not «hire two more operators urgently» — it's «the agent handles the main flow, operators focus on complexity». Response time doesn't scale with load.
Answer standard is always the same
Before: client A got a polite accurate reply from a senior, client B — short and dry from a tired junior. Now: equally correct, with sources, in one voice. Clients feel it — that's the company's reputation.
Support cost per ticket drops
Typical is closed by the agent — no operator needed. Fewer operators are needed, but they become more expensive (because more expert). Per-ticket support cost drops 2–4× depending on the share of typical.
Real causes of inbound are visible
«Where did the export button go» — 80 times → UI changed without explanation. «Why is the May invoice bigger» — 40 times → opaque tariff recalculation. These are business signals for product, marketing, billing — not «load on support».
Operator churn drops
The main reason operators quit — burnout from routine. When the operator works only on complex and interesting cases — they stay. Hiring stops being «plug the holes», becomes «grow experts».
The knowledge base becomes a living asset
The agent sees which questions it can't confidently answer, which documents contradict, which are stale. This feedback flows automatically to base owners. After a year the base is a working tool, not «a storage of stuff written 5 years ago».
Five changes — for the support operator.
Churn in support is company money and human burnout. The support agent changes the operator's daily work in five ways — and that's what reduces churn.
Work only on the hard stuff
Typical questions («how to reset password», «where's the invoice») are closed by the agent. The operator gets cases where a human is really needed: non-standard situations, emotional clients, integration errors, billing conflicts.
Context already gathered, no searching
When it reaches the operator — there's already the client's history, past tickets, current state, what the agent already tried. Not «hello, start from scratch», but «I see the issue, let's solve it».
Less copy-paste = less burnout
The main reason operators burn out — typing the same thing 200 times a day. When repetition goes to the agent — every case feels new, the operator stays fresh to the end of shift.
Learn faster on complex cases
Before: a new hire spent 80% of the day on «how to register», expertise grew slowly. Now: a junior immediately sees complex cases (with agent hints), learns fast. A junior in 6 months — what used to take a middle a year.
Clear «when am I needed»
Before: unclear what to escalate further. Each time — the operator's call. Now: the agent clearly hands over complex cases with a reason. The operator's scope is visible to both them and the lead.
Eight actions — from first line to cause analytics.
So the effects don't sound abstract — eight specific actions, in four zones.
· First line
Answers typical questions from the knowledge base
Passwords, tariffs, how to do X, order status, instructions, policies. With source attribution and a confidence score.
Classifies inbound
Problem type, product, client segment, priority, emotional tone. Not «one of 20 tags», but structured metadata for routing and analytics.
· Operator assistance
Drafts replies for the operator
When a case is complex — the operator gets a prepared draft with sources. Accept, send or edit — in seconds.
Hands over complex cases with full context
Escalation isn't «hi, figure it out from scratch» — it's «here's the client history, what I tried, why I'm handing over». The operator spends time on the solution, not on gathering.
· Flow management
Sets categories and statuses
In the helpdesk the ticket arrives already with the right category, priority, product link. No time wasted on manual triage.
Holds SLA across channels and segments
Premium client — higher priority, faster response. Complaint — higher priority. Standard channel — standard SLA. No manual sorting.
· Support analytics
Aggregates common questions and base gaps
Which questions lacked a confident answer? What new topics appeared? Where do documents contradict? Not «ticket count» — it's a map of where the knowledge base is stale.
Reports «what clients are aching about»
Not «closed 1240 tickets this week», but «300 asked about feature X (worth improving?), 80 about regular failures in product Y (there's a real issue)». Business signals to product and marketing.
What we plug in — and what you get
The Support agent stands on first line — left are customer channels, right is the outcome for the support team and the customers themselves.
Support 24/7
Holds first line: answers routine, escalates complex, keeps history per customer
- Receives the request from any channel instantly
- Looks up the answer in the knowledge base
- Replies to the client citing the source
- Escalates to a specialist if the question is complex
- Keeps history and tracks SLA by topic
Customers get answers faster, specialists handle only the complex. Customer history is visible — every next reply is in context.
Where we plug in — six points of the support stack.
The support agent works inside your existing systems — no need to change helpdesk or client channels. Start with one channel and a limited base, expand after the pilot.
Helpdesk systems
Where tickets, history and operators live. We connect to your existing system, not replace it.
Customer channels
Where clients write from. We start with one channel in the pilot, expand on implementation.
Knowledge base
Where the agent draws answers from. If you don't have one — we'll help build it, but that's a separate stage.
Quality control
So mistakes don't reach clients and the lead sees the picture.
CRM and client history
So the agent sees who the client is and their history (if your policy allows).
AI stack
Models are chosen for the task: classification, answer generation, sentiment understanding.
Three levels — not three pricing tiers.
Simple comparison. An additional support operator costs the company 80–120K/month with taxes. Over a year — 1.5M per one person. And they'll need to be replaced after 8 months because of churn. The support agent handles 50-80% of the typical load and works 24/7 — no weekends, lunches, or holidays.
Minimum working contour
One support channel. Limited knowledge base / FAQ. Inbound classification. Reply drafts. Operator handoff for complex cases. Tested on typical inbound.
Pilot goal — in 2-3 weeks you see the share of typical inbound the agent closes confidently. If less than 30% — we'll say so, the agent won't pay off.
Support for the whole company
- ▪Full integration with helpdesk / CRM
- ▪Extended knowledge base with RAG
- ▪All major client channels
- ▪Ticket statuses and reply templates
- ▪Logs and quality control
- ▪Escalation scenarios by case type
- ▪«What clients ache about» reports for product and marketing
Personal data, SLA, multiple products
If support works with client PII, financial information, contractually binding SLA, multiple product lines or multiple support tiers — that's enterprise. Private deployment, local model, audit logs, fine-tuning for your product.
Footnote · maintenance
From 40K RUB/month
The product changes, new inquiry types appear, documentation updates. Maintenance covers: scenario updates for product changes, confidence-threshold tuning based on feedback, adding new channels, quality monitoring, the local model within the limit.
When the support agent isn't your fit.
If you don't have a knowledge base — you need to build one first. The support agent answers from what exists; without a base it either doesn't answer or hallucinates. That's not our format. First an FAQ or basic docs, then the agent.
If you have 10 tickets a week — the support agent is overkill. This is a tool for scale: hundreds of tickets per day, multiple channels, several operators. At low volume an operator handles it faster than agent setup pays off.
If you expect «100% of tickets handled with no operators» — this isn't our format. Complex cases, emotional clients, non-standard situations, product errors — always need a human. A realistic figure is 50-80% of typical inbound; the rest goes to an operator with prepared context.
If your «support» is actually «sales disguised as support» — that's a different agent. Sales agent for qualification and follow-up; support agent for real client problems. Different tools with different goals.
If any of the above describes you, mention it on the first call. We'll propose a different configuration or honestly point you to a different approach.
We don't just deploy — we teach people to use AI properly.
In 2–3 years AI tools will be the standard in engineering and business teams. Whoever starts learning now will gain a competitive advantage that latecomers won't have. That's why we have a separate direction: training client teams, so that AI tools and agents don't become «a toy for 3 months», but turn into part of the working process.
Corporate programs for adopting AI in development
For company engineering teams that want to switch to the AI-native approach. A 2–4 week program: review of the team's stack, selecting AI tools for the tasks, training on using them, ramp-up to regular production use. Not a «theoretical course», but a real shift in how the team works.
CTO, tech leads, developers · teams 5–50+
Client team onboarding for working with AI agents
Once we've deployed Sales, Personal, Support, RAG or another agent — we train your team to work with it well. How to adjust scenarios, write prompts for typical tasks, handle complex cases, give the agent feedback for improvement. Without this even a well-deployed agent gets «abandoned» in 3 months.
Client operational teams · typically after agent deployment
«Where to start with AI» mentor sessions for executives
Short individual sessions with CEOs, CTOs, business owners: which AI tools actually work, where to start in your company, how to link AI to business goals, how not to burn budget on «let's try something trendy». Sessions — 90 minutes, with a concrete roadmap as the output.
CEO, CTO, owners · 90-minute sessions
Open learning materials in the Telegram channel
A free column in our Telegram channel @dxaiblog. AI tool reviews, practices from real projects, mistakes and how to avoid them, reviews of new models and platforms. Anyone can subscribe — engineer, owner, product manager. This is the public part of our education, no commitment.
Anyone exploring AI · public channel · free
Training cost is discussed individually — depends on team size, format (online/hybrid/corporate visit), program depth, and whether you need a process built from scratch or accompaniment of an existing one.
If you want to start small — subscribe to the Telegram channel @dxaiblog. All materials there are free. That's our «step zero» of training.
Describe your support — we'll respond with an analysis within 2 hours
We reply within 2 hours during business hours. In the pilot call we'll show where the agent pays off and where it shouldn't be tried.