CUSTOMER SERVICE, HANDLED
AI agents for customer service. Built and run for you.
Machine-like builds and operates custom AI customer service agents for small businesses. Each agent handles an agreed support workload using existing tools, approved information, and clear rules for human approval.
Hand off the ticket sorting, account lookups, routine replies, and record updates that fill your team’s day. We build the agent and keep it working. Your people keep the complex cases and the decisions that need them.
Create a free account and describe your support workload. We scope and build the agent before your 14 days of live evaluation begin. No credit card. No setup or integration fees.
One workload starts at $249/month after the trial. Month-to-month. Cancel anytime.
01 / Customer evidence
A smaller backlog. More attention for customers.
Machine-like’s published fitDEGREE case study reports 82% of Tier 1 support tickets automated, a 68% reduction in average first-response time, and a 54% decrease in ticket backlog. The engagement covered triage, customer-context retrieval, responses, record updates, and escalation within fitDEGREE’s existing systems.
Tier 1 support tickets automated
Reduction in average first-response time
Decrease in ticket backlog
These are reported customer results, not a forecast for your business. “Automated” does not mean every ticket was resolved without human involvement. The published case study does not provide ticket counts or absolute before-and-after baselines.
Routine tickets stopped requiring a person at every step. The team could spend more attention on complex issues and customers who needed help beyond the standard process.
Read the fitDEGREE case study“Our partners at Machine-like gave us a more responsive support operation without forcing a massive migration.”
02 / Direct answer
Stop doing the same support work on every ticket.
AI agents for customer service are software systems that interpret requests, retrieve relevant information, and carry out permitted steps in connected tools. Machine-like combines those capabilities with a team responsible for building and operating the agreed workflow.
A short customer question can create a long chain of work. Open the account. Read the past tickets. Find the policy. Draft the reply. Update the record. Decide who should take over.
When ticket volume climbs, that repeated work competes with the cases that need your best people. We help you delegate the repeatable steps while keeping the judgment calls where they belong.
03 / The work you can hand off
Give the recurring work a permanent owner.
Machine-like scopes customer service agents around repeatable requests, accessible systems, and verifiable completion. Each workload specifies the inputs, permitted actions, approval points, and handoff destination before launch.
Ticket triage
Read incoming requests, identify the topic and urgency, attach account context, and route each ticket to the right queue.
Order and account questions
Retrieve current records and prepare an accurate status update or next step, using the information the connected systems actually provide.
Routine answers
Use approved policies and help content to answer covered questions. Start with drafts; allow automatic replies only for categories you explicitly authorize.
Returns and billing requests
Gather the relevant transaction and policy, check the request against the agreed rules, and prepare the action or approval packet.
After-hours intake and follow-up
Handle permitted requests, collect missing details, and prepare exceptions for your team according to the agreed coverage.
Ticket updates and handoffs
Record the work, preserve the conversation and sources, and give the next person the decision they need to make.
Start with the request your team sees every day. The first workload should have a clear finish line, whether that is a completed routine request or a correctly routed case with the lookup already done.
04 / Exceptions
See what happens when a request needs a person.
Machine-like defines both completion and escalation paths for each customer service workload. The following billing example illustrates the proposed process; the example is not a customer production record.
The request: “I think I was charged twice. Can you refund one payment?”
- 01
Retrieve the context.
The agent uses the approved verification process and authorized access to find the customer’s account, relevant transactions, and ticket history.
- 02
Check the source.
The agent checks the billing records and refund policy. Two charges alone do not establish that a refund is owed.
- 03
Prepare the decision.
The agent assembles the evidence and drafts the reply. A refund that requires approval stays on hold.
- 04
Hand off the whole case.
The responsible teammate receives the request, supporting records, applicable policy, proposed action, and reason for escalation.
- 05
Record the outcome.
The approved next step and ticket update follow the agreed workflow. Missing information or a failed action stays visible as an exception.
Your teammate starts with the facts instead of repeating the investigation. The customer is not promised a refund before the required decision has been made.
05 / Approvals and permissions
Keep control of what reaches your customers.
Machine-like agents begin in draft-and-approval mode. Automatic customer-facing actions require explicit authorization for the relevant category of work, with written limits and a record of the action.
- Authorization recorded: order-status replies, written limit
- Authorization recorded: routine answers from approved content
- Refunds stay behind the approval gate
Illustrative. Select a cell to try a setting.
You decide which requests can move automatically, which actions need a signature, and which situations go straight to a person. A routine order-status reply can have different permissions from a refund or sensitive account change.
- 1
Approved sources
Define the policies, documentation, and customer records the agent may use.
- 2
Scoped access
Authorize the systems and actions needed for the workload.
- 3
Explicit exceptions
Route missing information, conflicting policies, and out-of-scope requests to a designated person.
- 4
Inspectable work
Review the sources, actions, outputs, and approvals associated with a run.
06 / Managed Agent Operations
Get the support capacity without another system to run.
Managed Agent Operations is Machine-like’s service for designing, deploying, monitoring, and maintaining custom agents. The service includes repairs and agreed changes within the existing workload and tools.
A new support tool still needs someone to turn your process into working instructions, test the exceptions, review failures, and update the workflow when things change. With Machine-like, that operating responsibility has an owner.
We handle: workflow design, connections, testing, deployment, monitoring, failure investigation, and maintenance.
You handle: business context, current policies, authorized access, and the approvals or customer decisions assigned to your team.
We confirm the exact tools and actions during scoping. An integration is useful only if it can access the information and perform the steps your workload needs.
Bring one recurring support job. We will establish the scope and what finished work should look like before building.
07 / Operating models
Choose how much of the work you want to own.
Customer service AI can be purchased as software, built internally, or delivered as a managed service. Machine-like provides the managed-service model: a scoped workload with build and ongoing operation included.
The comparison below describes operating models. Individual software vendors may also sell implementation or managed services.
| Decision | Self-operated software | Internal build | Machine-like |
|---|---|---|---|
| Workflow setup | Internal administrator configures vendor features | Internal team designs and builds the workflow | Machine-like builds the agreed workload |
| Ongoing operation | Internal owner operates the configured workflow | Internal team owns monitoring and repairs | Machine-like monitors and maintains the workload |
| Available actions | Vendor capabilities and configured integrations | Actions implemented and maintained internally | Actions confirmed and authorized during scoping |
| Business decisions | Customer team sets policy and handles exceptions | Customer team sets policy and handles exceptions | Customer team sets policy and handles exceptions |
| Cost to assess | Software fees plus internal operating time | Engineering, infrastructure, and ongoing operating time | Agreed workload price plus remaining customer review time |
| Best fit | Team with a capable internal automation owner | Team requiring direct ownership of custom development | Team seeking delegated build and daily operation |
If your current helpdesk already solves the problem and someone owns the operation, use it. Machine-like fits when you want the recurring work handled and the agent operated for you.
08 / Pricing
Start with one workload. Know the price before it runs.
Machine-like’s Operator tier costs $249 per workload per month for one or two workloads. Growth costs $199 per workload per month for three to five workloads; six or more workloads receive custom pricing.
Operator
1-2 workloads
$249
per workload / month
Growth
3-5 workloads
$199
per workload / month
Command
6+ workloads
Custom
Scoped pricing
Published rates. Estimate only.
A workload is one agreed recurring job. Support triage and response can include classification, context lookup, reply preparation, record updates, and escalation when those steps belong to the same scoped job.
The service includes the build, hosting, monitoring, and fixes. There are no setup or integration fees. A different job is scoped and priced separately before your bill changes.
Your free 14-day evaluation starts after the agent is built and deployed. Keep the workload if the finished work earns its place. Cancel anytime.
09 / Evaluation
Judge the work on your own queue.
Machine-like’s trial evaluates a defined support workload against agreed completion criteria. Useful measures include correct routing, remaining human handling time, reply quality, reopened requests, and correctly handled exceptions.
Start with a baseline from the same kinds of requests you plan to delegate. During the trial, distinguish work the agent completed from work it prepared for a person. Count review, corrections, and repeat contacts in the result.
A faster first reply is useful. A correctly completed request with less total handling is the stronger test. A customer who stops replying is not automatically proof of resolution.
- 1
Describe the job.
Create a free account and share the recurring requests, tools, policies, and examples of good work.
- 2
Agree the boundaries.
Machine-like scopes the workflow, access, approvals, handoffs, and definition of done.
- 3
Build and test.
Machine-like connects the required tools and tests representative requests before live operation.
- 4
Evaluate real work.
The 14-day trial begins after deployment. Review the outputs and remaining effort before deciding to subscribe.
10 / Fit assessment
Start where repetition is costing the most.
Machine-like is designed for businesses with recurring support work and a clear process for handling exceptions. A useful first workload has enough repetition to evaluate and a result that the customer team can inspect.
Best fit
- Small support teams repeating the same lookups, replies, and ticket updates.
- Businesses with accessible helpdesk, account, order, or billing information.
- Teams with approved policies and a named owner for judgment calls.
- Businesses seeking an external owner for agent setup and ongoing operation.
Start with a narrower scope when
- Most requests require new judgment: prioritize triage and assisted drafting.
- Policies or system access are incomplete: resolve the gaps before automatic action.
- Ticket volume is low: compare remaining handling time and service cost before subscribing.
11 / FAQs
What to know before handing over the work.
Machine-like scopes customer service agents around each business’s systems, support policies, and approval requirements. These answers explain the service, the limits, and the evaluation process.
What are AI agents for customer service?
AI agents for customer service are software systems that understand requests, find relevant business information, and perform permitted steps toward resolution. Those steps can include looking up an order, drafting an answer, updating a ticket, or escalating a case. Machine-like builds and operates the agent around an agreed support workload.
How are AI agents different from chatbots and copilots?
A customer service AI agent can carry out a workflow; a basic FAQ chatbot primarily provides answers, while a copilot primarily assists a human operator. Product labels overlap, and some chatbots also take actions. Evaluate the actual workflow, permissions, and operating responsibility rather than relying on the label.
Can an AI agent work with our existing helpdesk?
Machine-like builds customer service agents around existing tools when the required access and actions are available. The scoping process confirms the helpdesk, customer records, knowledge sources, and specific steps the agent needs. You do not need to assume a helpdesk replacement before discussing the job.
Can the agent answer customers automatically?
Machine-like agents can send customer replies automatically only for explicitly authorized categories within agreed limits. New agents begin in draft-and-approval mode. You review the work before authorizing more autonomy, and exceptions continue to follow the approval rules.
What happens when the agent cannot resolve a request?
Machine-like routes unresolved or out-of-scope requests to the designated person with the available context and the decision needed. The workflow specifies the destination, relevant records, attempted steps, and reason for escalation. After-hours escalation does not imply an on-call human unless that coverage has been agreed.
Can the agent issue refunds or change customer accounts?
Machine-like agents may prepare or perform refund and account workflows only when system access, policy, and explicit authorization permit the action. Money movement and sensitive changes are held behind the applicable approval gates. A request outside policy reaches a person rather than becoming an automatic exception.
What information do we need to provide?
Machine-like needs representative support requests, approved answers and policies, authorized access to relevant systems, and clear escalation rules. Current help articles and examples of well-handled tickets are useful starting points. Missing or contradictory information must be resolved or routed for review before the affected work runs automatically.
How is customer information protected?
Machine-like scopes access to the systems required for the workload and applies approval rules to sensitive actions. Customer teams authorize the connections and can revoke access. Specific data handling, retention, and compliance requirements should be established during scoping before customer information enters the workflow.
Does Machine-like replace our support team?
Machine-like takes on agreed recurring support work while the customer’s team retains exceptions, approvals, and relationship-sensitive decisions. A workload may automate a routine request or prepare a complex case for a person. The goal is less repetitive handling and more attention available for customers.
How much does a customer service AI agent cost?
Machine-like charges $249 per workload per month in the Operator tier for one or two workloads. Growth pricing is $199 per workload per month for three to five workloads, with custom pricing for six or more. Build, monitoring, and maintenance are included; the scope is agreed before enrollment.
How long does setup take, and when does the trial start?
Machine-like’s free 14-day evaluation starts after the agent is built and deployed, not when an account is created. Setup timing depends on the agreed workflow, available access, source material, and testing requirements. The schedule is established during scoping rather than treating every support integration as an instant launch.
Who maintains the agent when our policies or tools change?
Machine-like owns monitoring, maintenance, and agreed updates within the existing workload and tools. Your team supplies policy or process changes and continues to make the assigned business decisions. A separate stream of work is scoped and quoted before it changes the subscription.
Your next support queue
Take one recurring support job off your team’s plate.
Machine-like offers a free 14-day evaluation of a scoped customer service workload after build and deployment. The evaluation lets the customer team inspect real outputs before choosing a monthly subscription.
Bring the ticket category you keep answering, the account lookup you keep repeating, or the queue that needs sorting every morning. We will turn the agreed process into an agent and take responsibility for keeping it running.
Create a free account and describe the work. No credit card. No setup fee. No obligation to continue.