Support automation means an AI agent reads every inbound ticket, classifies intent and priority, and answers the repetitive 40–60% from your own knowledge base — sending the reply or leaving it drafted for an agent, whichever level of control you want. Genuine edge cases escalate to a person with full context. Typically live in 4–5 weeks.
40–60% of tickets are the same handful of questions answered over and over.
Customers wait hours for a reply that a model could draft in seconds.
Tickets pile up overnight and across time zones, inflating SLAs.
Senior agents spend their day on copy-paste instead of hard cases.
Tickets arrive from email, chat, and forms into one queue.
The agent detects intent, priority, and language.
It drafts or sends an answer grounded in your knowledge base.
Genuine edge cases route to a human with full context.
We take 6–12 months of resolved tickets and build the classification from what your customers actually asked, not from a generic taxonomy. Accuracy is measured against a held-out set before anything goes live.
Replies are generated from your help centre, your macros and past resolutions, with the source attached — whoever reviews a draft can check where every sentence came from.
Confidence thresholds, refunds and account actions, VIP segments and anything legally sensitive route to a human by rule, not by the judgement of the model.
A dashboard with deflection, reopen rate, CSAT on automated replies, and the questions the agent handled worst last week.
API or an account in Zendesk, Intercom, Freshdesk or HubSpot — whichever you run. Read access is enough to start.
6–12 months of resolved conversations. That is what the model learns your tone and your edge cases from.
Someone who can decide what the agent may answer alone and what always goes to a human. A few hours a week while we build.
Under roughly 200 tickets a month the saving will not pay for the build — a better macro set will.
If your answers rest on judgement that is not written down anywhere, the agent has nothing to ground on. Document it first.
We do not automate refunds, account deletion or anything with legal consequence — those stay with a person, by design.
Each page states the workflow, the systems it integrates with, what it costs and when it is the wrong choice.
Classifies and routes inbound tickets and drafts the reply, with a confidence threshold that sends the rest to a person.
Sorts mixed incoming documents by type and routes each one to the queue, folder or system that owns it.
Turns sales and support calls into structured notes, objections, next steps and CRM updates.
no-shows
Client under NDA. The figures are the client’s own, comparing the periods before and after launch.
Full audit + a working pilot on one slice of the process. Fully credited to the build.
Full rollout with monitoring, escalation, and documentation. Priced on scope.
Ongoing tuning, new scenarios, and monthly reporting. Cancel anytime.
You choose. Many teams start with AI-drafted replies an agent approves, then graduate high-confidence categories to fully automatic with sampling-based QA.
We don’t propose an off-the-shelf product either. The audit is there precisely to see where your process differs from the ordinary one, and the build is shaped around that difference. And if it turns out a standard tool covers you well enough, we’ll say so plainly — the process map stays with you in any case.
It’s a fair thing to ask, and responsibility matters more here than accuracy. A person answers for it, and the system is built so that they can: a doubtful case is never posted quietly, it goes to a review queue. Low confidence isn’t an error for us, it’s a route. Around 70% clears straight through and a person looks at the rest — and where exactly that line sits is yours to decide.
That’s a common requirement, and a reasonable one. We fit the setup to your data rules: if nothing may leave, the model runs locally inside your own perimeter, and the documents never cross it.
That’s not unusual, and it’s fine. Integrating without an API is the most underestimated line in a quote, which is why integration surface comes first among the four cost drivers, and why we don’t name a fixed price before the audit. Working without an API is perfectly possible — files, exports, email — it simply costs more, and it’s better to know that early. Which is why, fairly often, we build the API for you.
It happens often, and usually for one reason: the pilot was measured on the happy path and the exceptions were left for later — though the exceptions are where most of the work turns out to be. So we look at the share that clears without a person rather than at extraction accuracy, and we agree in advance who handles the rest, and how.
No, that isn’t what this is about. What goes is the retyping, not the people: decisions stay with a person — the disputed document, the non-standard transaction, the conversation with the client, the signature under the reporting. On one accounting project, closing a client month went from three days to four hours, not because anyone was let go, but because a qualified specialist stopped keying in details by hand. The time that frees up most often goes into growth: more clients with the same team, without the costs rising alongside.
It’s a fair question, and sometimes doing it yourselves really is the right answer. We say so when the volume doesn’t justify a build: below roughly 300 documents a month the arithmetic usually doesn’t work. The calculator on this site includes running costs, so you can weigh that up before you ever talk to us.
They do change, and that’s exactly what we build for: the model is a replaceable part here, not the foundation. The source of truth is our own database — the document, the counterparty, the entry. The model is attached to the side, and we update it whenever something better appears; for you that’s planned maintenance, not a rebuild.
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