70% of routine support tickets, resolved without a human.
Impressly is an agentic AI support system we designed, built, and operate. It connects directly to the customer database and business APIs, so it doesn't just answer questions, it takes action and closes tickets on its own.
- routine tickets resolved autonomously
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70%+
routine tickets resolved autonomously
- average first response time
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Seconds
average first response time
- coverage without adding headcount
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24/7
coverage without adding headcount
Support that scaled with headcount, not with software
Customer support was growing linearly with headcount. Every new cohort of customers meant another support hire, another training cycle, and a longer queue in between. The economics only got worse with growth.
The obvious fix, a chatbot, made things worse. Traditional rule-based bots could only match keywords to canned answers. They couldn't see the customer's account, couldn't take action, and couldn't tell when they were out of their depth. Users learned to type "human" as their first message.
The real requirement wasn't a bot that deflects tickets. It was a system that resolves them: one that can look up an order, issue a refund, update a subscription, and know exactly when a case needs a person.
An agent that acts, not a bot that replies
We built Impressly as an agentic system, not a chatbot. At its core is an LLM-driven agent running on a directed graph architecture: each incoming ticket moves through classification, context retrieval, action planning, and resolution, with explicit checkpoints at every stage.
The agent connects directly to the customer database and business APIs with scoped, auditable permissions. When a customer asks "where's my order?", it doesn't paste a tracking FAQ, it queries the order, checks the carrier status, and answers with the actual state of that shipment. When action is needed, it executes it.
Every capability sits behind guardrails. Actions are permission-scoped per ticket type, financial and account-critical operations route through human approval gateways, and anything below the agent's confidence threshold escalates to the team with full context already gathered, so a human picks up a summarized case, not a cold ticket.
- Direct API integration
- The agent reads and writes against live systems, orders, subscriptions, account data, instead of guessing from a knowledge base.
- Directed graph orchestration
- Every ticket follows an explicit, inspectable path through the agent's reasoning. No black-box replies.
- Human-in-the-loop gateways
- Sensitive operations pause for one-click human approval. The team decides where autonomy ends.
- Confidence-based escalation
- The agent knows what it doesn't know. Low-confidence cases hand off to humans with context pre-assembled.
The queue stopped scaling with the business
Over 70% of routine tickets are now resolved automatically, end to end, with no human touch. First response times dropped from hours to seconds, around the clock, and customer satisfaction rose.
The support team's role changed shape: instead of clearing a queue, they handle the genuinely hard cases, with every escalation arriving pre-investigated. Support headcount stopped being a function of customer count.
Frequently asked questions
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Yes, when it has real access to act. Impressly resolves over 70% of routine tickets autonomously because it connects directly to the customer database and business APIs. It can look up orders, issue refunds, and update accounts, not just answer from a script. The remaining cases escalate to humans with full context attached.
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A chatbot matches questions to prepared answers. An agentic system reasons about the case, retrieves live account data, plans a sequence of actions, and executes them through APIs. The practical difference: a chatbot tells the customer how to request a refund; the agent issues it.
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Layered guardrails. Every action is permission-scoped by ticket type, financial and account-critical operations require human approval before executing, and the agent escalates any case below its confidence threshold. Decisions run on a directed graph architecture, so every step is inspectable and auditable.
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No. No confidential customer data is ever used to train public models. Workloads run on isolated, encrypted infrastructure, built for LGPD and GDPR compliance.
Want your support queue to stop scaling with headcount?
We build agentic support systems around your stack: your APIs, your rules, your escalation paths. Book a call and we'll map which of your ticket categories an agent could own first.