AI & Automation
Chatbot Development
Assistants that answer from your documentation rather than their imagination, know when to hand over to a person, and get measured on whether they actually resolved anything.
Grounded, not improvised
A chatbot that answers from a general model will eventually invent a policy you do not have. We build on retrieval: the assistant searches your real content — documentation, product data, past tickets — and answers from what it finds, with citations back to the source so a user can verify it.
When the content does not contain an answer, the correct behaviour is to say so and offer a human. We design that path first, because it is the one that protects your reputation.
Measured on resolution
Deflection rate alone is a vanity metric — it counts conversations that ended, not problems that were solved. We instrument for resolution, escalation reason and user satisfaction, and review the transcripts where it went wrong.
What the engagement includes
Content ingestion
Documentation, product data and past tickets, kept in sync.
Retrieval pipeline
Chunking, embedding, ranking and citation.
Guardrails
Scope limits, refusal behaviour, tone and safety.
Human handover
Escalation with full context into your support tool.
Multilingual
The languages your markets actually write in.
Analytics
Resolution, escalation reasons and a transcript review loop.
Technologies we use
Frequently asked questions
How do you stop the chatbot making things up?
By grounding every answer in retrieved content from your own sources, citing that source in the reply, and constraining the assistant to refuse when retrieval comes back empty. It is not a guarantee — nothing is — but it moves the failure mode from confident invention to an honest handover.
Can it work on WhatsApp?
Yes, through the WhatsApp Business API, and the same assistant can serve your website, WhatsApp and in-app chat from one pipeline. For several of our markets WhatsApp is where customers actually are.
What happens when it cannot answer?
It says so and offers a person, passing the full conversation into your support tool so the customer does not repeat themselves. That handover is designed before the happy path.
How much does it cost to run?
Per-conversation cost depends on model choice, content volume and traffic. We model it before build, cache aggressively, and route simple queries to cheaper models — then monitor spend against a budget you set.