Managed AI support teammates
Add an AI teammate to your support team— not another platform.
Loqum builds and operates a support teammate in the tools you already use. We train it on your policies and past tickets, limit the cases it can handle, and expand its role after its work meets the standard you set.
The problem
Polished replies are cheap. Company-specific judgment is the hard part.
LLMs can produce fluent replies in seconds. They still do not know your refund exceptions, order state, or when a customer needs a human. The work is connecting the right information, defining the limits, and improving the system from cases it gets wrong.
Each alternative carries a different cost:
The demo is a commodity. The implementation is the product.
Inside your operation
It works from the same queue as your team.
Assign it tickets in the tools you already use. Keep your rules, reporting, escalation paths, and supervisors. There is no helpdesk migration.
You keep
- Your queues and assignment rules
- Your supervisors and escalation paths
- Your reporting and SLAs
- Your policies and final approval
- Which cases the teammate receives
The teammate gets
- Cases it is approved to handle
- Approved company knowledge
- Narrowly scoped, least-privilege tools
- A controlled, reversible autonomy boundary
How it works
How the role expands.
First we map the operation and test against past tickets. Then the teammate handles a narrow set of cases under review. Production failures show us which policy, tool, or memory it needs next.
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01
Discover
Discover the operation
Map ticket types, knowledge sources, tools, escalation paths, and your standard for a good response. The result is an implementation specification.
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02
Test against past tickets
Test against past tickets
Run the teammate against a representative sample of your historical tickets before it touches live work, and build the first evaluations from how cases actually resolve.
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03
Start with a bounded role
Start with bounded responsibility
Start in draft-only, shadow, or a narrow autonomous role for clearly defined, low-risk case types.
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04
Observe failures
Observe failures and missing information
Watch where responses fall short, where customers stay unhappy, and where the right answer needs data that is not in the documentation.
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05
Build narrow tools, update memory
Build narrow tools, update memory
When a recurring case depends on live information, add the smallest useful tool, such as a read-only order-status lookup. Add validated decisions and failure patterns to operational memory.
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06
Expand on evidence
Expand only on evidence
Add case categories and reduce required human review only when the evaluation results support it. Reverse course if quality drops, policy changes, or a model update causes a regression.
Trust & control
Trust is earned — and it can be withdrawn.
Before rollout, you decide which tickets it may receive, what it may read, and when it must hand off. We test those boundaries against past cases and review them whenever the work, policy, or model changes.
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01
Restricted case scope
It only receives the case types you approve. Autonomy is not binary; a teammate can be autonomous for one category, draft-only for another, and prohibited from a third.
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02
Least-privilege access
It gets only the access its role needs: narrow, read-only tools by default, rather than broad database or administrative permissions.
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03
Retrieval before invention
When an answer depends on live data, it retrieves from an approved source rather than guessing. If the information is missing or contradictory, it asks or escalates instead of inventing.
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04
Explicit escalation
The teammate hands off when information is missing, policies conflict, a request has serious consequences, or a message appears to be manipulating the system.
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05
Higher-risk work gets a second check
Where a proposed action carries greater risk, it can be evaluated in a separate context before it is sent. That keeps the customer's message from directly steering the check, and the acting model from grading its own work.
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06
Auditability
We retain enough of what the teammate received, retrieved, proposed, and sent to investigate what happened, while minimizing the data kept.
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07
Reversible autonomy
Capabilities can be restricted, paused, or rolled back if quality drops, policies change, or a model update causes a regression.
No AI support system is error-free. We agree which errors are tolerable, which require immediate escalation, and how performance will be measured against your current process.
Fit
Built for lean teams whose support queue keeps growing.
Strong fit
- A small, growing company whose support queue has become difficult to manage.
- Most support arrives asynchronously through tickets, email, or a shared inbox.
- A ticketing tool, shared inbox, or helpdesk where work can be assigned.
- Historical tickets available for implementation and evaluation.
- An internal owner who can take part in setup and review.
- Recurring questions, including some that require your operational data.
- A need for more support capacity without equivalent growth in headcount.
Probably not a fit
- Very low volume: if one person handles support in a fraction of their week, the economics do not work for a managed implementation.
- Platform shoppers: if you want a new helpdesk, CRM, or a custom application, that is not what we build.
- Zero-error requirements: if you require a guarantee of zero errors, fully unsupervised write access from day one, or a set-and-forget bot, we are not the right partner.
Design-partner program
Ten design partners. Six weeks. No implementation fee.
During the sprint, we map the operation, analyze historical tickets, define evaluations, build any necessary read-only tools, and run a supervised live rollout.
Applying does not guarantee a place. Ongoing service fees, included usage, start dates, and post-sprint terms are agreed separately. We limit the cohort so each implementation gets direct attention.
Apply for a design-partner sprint
Tell us how support works today and where it is breaking down. We review every application; if the operation looks like a fit, we will arrange a short call.
Application received.
Thanks. We have your application. If the operation looks like a fit, we will contact you to arrange a short call.
FAQ
What operators ask before rollout.
01 What exactly is this?
A custom AI support teammate, built and operated for your business and deployed inside the support tool you already use. Loqum manages it; there is no separate platform to configure.
02 Do we need to migrate or change tools?
No. The teammate works inside your existing queues, assignment rules, and workflows. You keep your workplace; we add a teammate to it.
03 How autonomous is it on day one?
Deliberately limited. It typically starts draft-only or in a narrow, low-risk case type with human review. Its role expands only as its work meets the evaluation standard, and it can be reduced if something changes.
04 What does the six-week sprint involve?
Operational discovery, historical-ticket analysis, evaluation design, configuration, any narrowly scoped read-only tools, pre-production testing, and a supervised live rollout. It is an observation and calibration window, not six weeks of continuous engineering.
05 What do we need to provide?
An accountable internal owner, access to historical tickets, your policies and documentation, timely clarification of edge cases, and the narrowly required access to your support environment.
06 What happens if it makes a mistake?
Depending on severity, the teammate may escalate the case, lose permission for that category, or return to human review. We investigate the failure, add it to the evaluation set, and restore responsibility only after the fix passes.
Keep your helpdesk. Add a teammate.
Keep your queues, rules, and reporting. Loqum adds a teammate whose role grows only after its work has been tested.