AI agents that work inside a real operation, not inside a demo.
Production voice and chat agents that take orders, qualify, schedule and resolve — with people on the escalation path and every interaction auditable.
Who this is for
For operations already answering more contacts than the team can absorb, where a large share of them are the same three questions.
The problem
Most contact volume is not complex — it is repetitive. Order status, hours, appointment confirmation and simple qualification consume the hours your best agents should spend on the calls that decide a customer relationship. Hiring more people scales the cost linearly. Automating the wrong part scales the errors.
What this covers
Voice agents in production
Agents handling live customer calls end to end, integrated with the systems that actually take the order.
Integrated, not bolted on
Connected to your backend, your catalogue and your CRM, so the agent works with real data instead of a script.
Audited continuously
Calls are reviewed and the rules are rewritten from what went wrong. That loop is the product.
What the agents do
AI voice agentsAgents that answer the phone, understand the request, look it up in your systems and complete it — including taking an order end to end.
WhatsApp and chatThe same logic on the channels where the customer already is.
Appointment schedulingChecking real availability and booking, not promising a callback.
Lead qualificationAsking the questions your sales team would ask, before your sales team spends a minute.
What makes them work
Integration with the system of recordThe agent reads and writes in the backend that actually fulfils the request. An agent that cannot see inventory is a recording.
Human escalationEvery path out to a person is defined, tested and monitored. This is not optional.
Continuous auditingCalls are reviewed and the rules rewritten from what went wrong. That loop is the product, not the model.
Where it is used
Order taking that completesThe agent takes the order, prices it against the live menu, confirms coverage for the delivery address and writes it into the system that fulfils it. No callback, no note for someone else to process.
After-hours coverageThe hours when calls go unanswered are usually the cheapest to automate and the most expensive to lose. The agent answers, resolves what it can and books the rest.
The three questions that eat the shiftOrder status, opening hours and appointment confirmation. Removing them from the queue is what frees a team, not adding headcount.
Overflow instead of voicemailWhen every human line is busy, the alternative is not a better hold message. It is a second line that resolves.
How it is delivered
We run the process before we automate it. First as a service with our own people, measuring where the time actually goes; then a redesign; then automation of the part that earned it. Deployment is incremental — one intent at a time, each one measured against how a person handled it, and reversible.
Discovery on real callsWe listen to what your line actually receives, not to a description of it.
A pilot with a defined exitOne intent, one channel, a date and a criterion to stop.
Escalation before autonomyThe path to a person is built first. Autonomy comes after.
Weekly auditRecordings reviewed, rules rewritten. That loop is the work.
Questions we get asked
Does the caller know it is AI?
Yes. Pretending otherwise is a bad idea legally and worse commercially: people notice, and they resent the discovery more than the machine.
What happens when it does not understand?
It escalates. Every path to a person is defined and tested before launch, and repeated failures on the same intent are a signal to redesign, not to retrain harder.
Is it fully autonomous?
No, and no honest implementation is. Some intents are resolved end to end; others are qualified and handed over. Which is which is a decision we make with you, intent by intent.
Can it work with our systems?
It has to. An agent that cannot read your inventory or write your order is a recording with better manners.