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See open rolesPeople do not hate chatbots. They hate chatbots that will not let them reach a person, and chatbots that answer confidently and wrongly.
Both are design choices. We build assistants that answer from your own documented content, say so plainly when they do not know, and escalate with the whole conversation attached so the customer never repeats themselves. The report on what it could not answer is often more valuable than the deflection rate.
Answers drawn from your help centre, policies and product documentation.
When to hand over, and how — with full context passed to the agent.
Website, WhatsApp, or inside your helpdesk. Same brain, right doorway.
What it will and will not discuss, in your voice rather than a generic one.
What it could not handle, which is your content gap list, ready-made.
Sampled weekly at first, so mistakes are caught by you rather than by a customer.
If one of these sounds like your situation, it is worth a conversation.
A large share of contacts are questions already answered in your help centre.
People asking at nine in the evening and hearing nothing until morning.
The same policy questions asked of the same colleague every week.
The difference between a model that helps and one that quietly misleads.
We measure how well the current process does first. Surprisingly often it is the thing to beat, and sometimes it wins.
Held-out, time-split where time matters. An accuracy figure from the training set is not a result.
Confident cases go through; uncertain ones go to a person. That boundary is set by you, not by us.
Data drifts, behaviour changes, accuracy decays. You get the alerting, not just the model.
Machine learning, generative AI, automation and analytics — applied to problems where the numbers justify it, and measured after launch.
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Deliberately front-loaded with the question of whether to proceed at all.
We look at your actual data, establish the baseline, and give you a straight answer on whether this is worth building. Some of these end in "do not".
A working model measured against the baseline on held-out data, with the error cases in front of you rather than averaged away.
Integration into the system where the decision is actually made, with the human-review boundary, logging and rollback in place.
Drift detection, periodic re-evaluation, and retraining when the numbers say so rather than on a calendar.
Tell us what you are trying to do. If AI chatbots is not the answer, we will say so.
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