Custom AI Agents
for Customer Support
We build support agents that plug into the helpdesk you already run — Zendesk, Intercom, Freshdesk, HubSpot or your own stack — and resolve tier-1 tickets end to end, not just deflect them into a different queue.
30 minutes. We look at your last 90 days of tickets and tell you what share an agent could realistically handle — including if the answer is "not enough to be worth it."
Your support queue is 60% repeat questions
Look at last month's tickets. A large share of them are the same handful of questions: where's my order, how do I reset this, why was I charged twice, can you change the address on this booking. Your team answers them accurately every time, and answering them is most of what your team does.
The usual fix is a chatbot on the website. It intercepts some of those questions, answers them from a help article, and hands over anything it doesn't recognise. Volume drops for a month or two. Then the pattern shows up: repeat contacts climb, customers rephrase the same question until they reach a human, and the tickets you thought were deflected come back through another channel.
That's the difference between deflection and resolution, and it's worth being precise about it. Deflection counts conversations a human never touched. Resolution counts problems that were actually solved. A system can report a very high deflection rate while solving far less than that — the two get conflated constantly in this category, including by vendors publishing their own numbers.
What actually removes work from your queue is an agent that can look up the order, check the policy, issue the refund, update the record, and confirm it back to the customer — then escalate cleanly when it can't. That requires access to your systems and permission to act in them. It's an integration problem, not a chat interface problem.
What a custom support agent does that a chatbot can't
A chatbot responds. It matches a question to a scripted flow or a help article and returns text. If the answer isn't in its content, the conversation ends or a human takes over.
A support agent reasons and acts. It reads the ticket in context, checks the customer's actual account, calls the systems that hold the answer, takes the action the resolution requires, and writes the outcome back where your team can see it. When it isn't confident, it escalates with the context already gathered, so the human doesn't start from scratch.
Reads your real data
Order status, subscription state, ticket history, delivery tracking — whatever the answer actually depends on, not just your help centre.
Writes back
Issuing a refund, updating an address, cancelling a subscription, tagging and routing the ticket. A chatbot tells the customer how to do it. An agent does it.
Knows what it doesn't know
Confidence thresholds decide what it handles and what a person handles. That boundary is a design decision we make with you, not a setting we hope holds.
What we build
Ticket classification and routing
Every inbound ticket read, categorised against your actual taxonomy, tagged, prioritised and routed to the right queue or person. This alone removes a measurable amount of manual triage before any ticket is auto-resolved.
End-to-end resolution with system write-back
For the intents you approve, the agent completes the job: retrieves the record, applies your policy, executes the action in your system, replies to the customer, and closes the ticket with an audit trail of what it did and why.
Escalation and human handoff
The agent escalates when confidence drops below your threshold, when the customer asks for a person, when sentiment turns, or when the intent is on your never-automate list. The human receives the full context — what was asked, what the agent checked, what it found, why it stopped. Escalation design is where most support AI deployments quietly fail, so we treat it as a first-class part of the build rather than a fallback.
Multichannel coverage
Email, live chat, phone and social — one agent, one set of policies, one knowledge source. [VERIFY: confirm all four channels are genuinely in production today. If phone is via a separate voice build, say so and link /solutions/ai-voice-agent rather than implying it's included.]
Knowledge grounding
The agent answers from your documentation, past resolved tickets and system data — with the source attached — rather than generating from general training data. This is the single largest determinant of accuracy, and it's why the first thing our audit looks at is the state of your knowledge base.
Works with the helpdesk you already have
You've likely already trialled your helpdesk's native AI. It's usually good at answering from your help centre and limited at anything that requires reaching outside the platform. That gap — between answering a question and completing a task across your systems — is where we build.
Zendesk
We build on Zendesk's APIs and app framework so the agent lives inside your existing ticket lifecycle: reading tickets, applying your macros and business rules, updating custom fields, and triggering actions in the systems Zendesk doesn't own — your order database, billing platform, or internal admin. [FILL: name one specific thing you've built on Zendesk for a real client, in one sentence.]
Intercom
Intercom sells Fin, and for content-based answering it's capable. Teams come to us when they need the agent to act in systems outside Intercom, or when their resolution logic depends on business rules that live in their own application. We build against Intercom's APIs so the conversation stays where your customers already are. [FILL: one specific Intercom build.]
Freshdesk
Ticket automation on Freshdesk, extended with retrieval from your own data sources and write-back to your operational systems. [FILL: one specific detail or example.]
HubSpot Service Hub
Support agents that read and write CRM context — so a support conversation updates the same customer record your sales and success teams work from, rather than living in a parallel silo. HubSpot is already in our stack. [FILL: confirm depth of HubSpot experience before claiming it.]
Salesforce Service Cloud
Custom agents working alongside your Service Cloud configuration, respecting your existing objects, permissions and org structure. [VERIFY: only claim Service Cloud experience if you have it. If you don't, cut this H3 entirely — it's the weakest term in the integration set and a false claim here is expensive.]
Something else, or nothing yet
Custom helpdesk, homegrown ticketing, or shared inboxes with no system at all — we've connected agents to all three. If it has an API, or a database we can reach safely, it can be integrated.
How we build it
Audit to production in 2–6 weeks.
Free audit (week 1)
We look at your last 90 days of tickets and classify them by intent and volume. You get back a breakdown of which intents are automatable, roughly what share of volume they represent, and an honest read on whether the numbers justify a build. Some audits end here, with us saying no.
Scope and guardrails
We agree what the agent handles, what it never touches, what it's permitted to do in each system, and where the escalation thresholds sit. Refunds above a value, account closures, anything regulated — these stay human by default unless you explicitly decide otherwise.
Evaluation set before launch
We take real resolved tickets with known-correct outcomes and build a scored test set. It's how we know whether the agent is improving, and how you know what you're getting before it touches a customer. Without one, every change after launch is guesswork.
Build and integrate
Connect the helpdesk, the data sources and the systems the agent acts in. Ground it in your documentation and past tickets.
Shadow mode
The agent drafts responses your team reviews before sending. You see exactly what it would have done, on live tickets, before it does anything unsupervised. This is where the confidence thresholds get tuned to your real traffic.
Go live on a scoped slice
Start with the highest-volume, best-understood intents. Expand once the numbers hold.
Monitor and tune
Deflection rate, true resolution, repeat-contact rate, escalation rate and CSAT, tracked from day one — because a deflection number that looks good while repeat contacts climb is a system redistributing work, not removing it.
What it costs
Almost nobody in this category publishes a price. We do, because the variables that move it aren't a secret and a discovery call shouldn't be the price of finding out.
What moves the price: how many systems the agent has to act in, whether your knowledge base is usable as it stands, how many intents you want automated, channel count, and compliance requirements.
What's not included: model and infrastructure usage, billed to your own accounts so you see the real per-ticket cost, and third-party licences.
Results you can expect
We'd rather give you the honest range than a headline number.
Independent aggregate benchmarks put median tier-1 deflection at around 41% across enterprise CX programmes, with the top quartile near 59% and the bottom quartile near 22% — figures aggregated from Zendesk CX Trends 2026 and Salesforce State of Service data. Deflection varies enormously by intent: structured, high-volume queries like password resets and account access consistently clear 70%, while billing and order-status queries sit in the 50–70% band.
True end-to-end resolution — problems actually solved, not conversations contained — runs lower than deflection almost everywhere. Realistic industry ranges for 2026 sit around 30–50% for early deployments, 50–70% as workflows mature, and 70–85% for deeply integrated agents that are allowed to take action on well-scoped use cases. Our builds target that last category, which is why we insist on system access and a narrow initial scope rather than a broad content-only bot.
On cost: per-ticket savings on AI-eligible tickets are large, but the realistic net reduction across a whole support organisation lands nearer 20–35% in year one, once infrastructure spend and the remaining complex tickets are counted. Anyone quoting you a whole-org saving above that is quoting the eligible-ticket number and calling it the total.
[FILL: one real deployment with client-verifiable numbers. A single sourced case study outperforms every benchmark paragraph above it. Until you have one, this section is honest but unproven — and the competitor with a named client will beat it.]
Built for your industry
E-commerce
WISMO, returns and exchanges, cart recovery, address changes, order modification. The highest-structure intents in support, and the ones AI handles best.
SaaS
Account and access issues, billing and plan changes, onboarding questions, bug triage with routing to engineering.
Healthcare
[VERIFY BEFORE PUBLISHING: do not claim HIPAA compliance unless you can substantiate it — a BAA, the specific safeguards, and a hosting arrangement that supports it. This is YMYL. If you can't substantiate it today, cut this line and the healthcare keyword rather than soften it.]
Security and data handling
Retention
[FILL: state your zero-retention position precisely — whose retention, at which layer, and with which providers.]
Deployment
Cloud, your own VPC, or on-premise. [FILL: confirm which you genuinely offer.]
Training
Your customer data is not used to train models. [VERIFY this holds for every provider in your stack, contractually, and say so in one line.]
Access control
Least-privilege credentials per system, scoped keys, and a full audit log of every action the agent takes.
Human gates
Refunds above a threshold, account deletion and anything you designate stay behind human approval by default.
Prompt injection
Ticket content, email bodies and attachments are treated as data, never as instructions. Injection attempts are part of the pre-launch test suite.
Certifications
[FILL: certifications you actually hold. If none, describe the practice and skip the badge line. An honest "no certification yet, here's what we do" beats an implication you can't back.]
Frequently asked questions
Find out what your queue could actually automate
Book a free AI audit. We'll classify your last 90 days of tickets, show you which intents are automatable and what share of volume they represent, and give you a price from the table above — including an honest "not yet" if the numbers don't support it.
Written by Siddhant [FILL: surname], [FILL: role] at Agents Chef
Google Cloud Professional Machine Learning Engineer
[FILL: 2 sentences — years building production systems, support agents shipped, domains worked in. Verifiable only.] [VERIFY: confirm the exact certification name as issued before publishing]