Custom AI support agents that answer routine tickets from your own data, take simple actions inside your product, and pass everything else to a person with the context attached. Fixed price, agreed up front, in production in 2–4 weeks for a focused scope.
By Dnyanesh Chandewar, Founder, CyberCore
When ticket volume grows faster than revenue, the usual answer is another support hire. That works until it does not: each rep needs onboarding, response-time targets still slip, and your best people answer the same questions every day. The better question is what share of your tickets could be answered correctly by something that can read your docs and query your systems. In most queues it is more than people expect, and rarely 100%; any vendor who promises that is selling a demo. We covered the cost side in The Real Cost of a Support Backlog.
Classifies every ticket against a fixed set of intents agreed with you, such as login and SSO issues, plan and invoice questions, how-to questions, integration errors, bug reports and cancellation requests, each with a confidence score. Answers from your product, account and billing data, using retrieval over your docs plus read-only calls into your own systems, so replies are specific to the account. Takes actions where you allow it, such as resending an invite or opening a correctly labelled bug, with every action logged and limited by rules you control. Escalates anything it is not sure about, with a summary, the account context and its draft attached, so your rep starts at step three instead of step one.
Buying is often the right call. Help-desk AI add-ons work well when your answers live inside the help desk and your workflow is standard. A custom agent earns its cost when resolving a ticket needs data from your product database or billing, when you need actions the help-desk vendor cannot take, when per-resolution pricing gets expensive at your volume, when customer data must stay in your cloud, or when you want to own the code. Read our neutral guide first: Build vs Buy an AI Support Agent.
The agent works inside the tools your team already uses, connecting to your help desk through its API so replies and escalations appear in the inbox your team already works in. Knowledge sources include your help centre, product docs and past resolved tickets. System integrations are scoped per project, for example your product database or admin API, billing, CRM, and Slack for escalations. We use Claude, OpenAI or Gemini, chosen per task; see AI agent development services.
Scoping (free): you share an export of recent tickets; we cluster them by intent, estimate which an agent could resolve safely, and agree scope, guardrails and a fixed price in writing. If the numbers do not justify a build, we say so. Build: we connect the help desk, knowledge sources and agreed system APIs and build the classifier, answer and action layer and escalation rules. Evaluate, shadow, hand over: the agent drafts replies for your team to review before anything reaches a customer, we measure accuracy per intent against a test set, and switch on autonomy one intent at a time. You receive the code, the evaluation suite and a runbook, with 30 days of post-launch support. Our method is in How to Evaluate an AI Agent Before Production.
Our most detailed support build was for an e-commerce brand handling more than 1,000 tickets a day. The triage agent resolved 78% of tickets without a human touch and average response time fell from 4.1 hours to 8 minutes. That client was not a SaaS company, but the design carries over: a fixed intent set, confidence thresholds, answers grounded in real account data and escalation by default. Read What 78% Ticket Deflection Actually Looks Like or the case study.
Good fit: 15–150 people, a support team of two or more, a few hundred tickets a week or more, and a meaningful share of tickets that need account or product data to answer. Not yet: you get a handful of tickets a day, your help centre is thin or out of date (fix that first), or you need the agent to handle complex billing disputes on day one.
CyberCore quotes a fixed price after a free scoping call, based on the number of intents, the systems the agent connects to and whether it takes actions or only replies.
2 to 4 weeks for a focused scope: one help desk, a defined set of intents and a few system integrations. The agent runs in shadow mode before replying to customers on its own.
No. It handles repetitive tickets so the team can resolve complex ones faster. Anything uncertain is escalated to a person with context attached.
The agent connects to your help desk through its API, so replies and escalations appear inside the tool your team already uses. We confirm compatibility with your specific help desk during scoping.
The agent is deployed in your own infrastructure, and we document every data flow for your security review. Model provider terms are reviewed with you during scoping.
You own the code, the evaluation suite and the runbook, and 30 days of post-launch support are included. An optional AgentOps retainer covers ongoing monitoring and tuning.
Send us a sample of recent tickets and we will tell you, honestly, what share an agent could handle and what it would cost. No pitch deck, no hourly meter.
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