AI customer support chatbot
Deploy a support assistant that answers from your help centre, policies, and ticket history—escalating when confidence is low or when a user requests a human.
The Problem
Generic chatbots frustrate users with vague answers. The fix is retrieval-augmented generation with citations, plus workflows that open tickets with full context.
Our Approach
We implement LangChain (or equivalent) pipelines over approved content, add evaluation on real customer questions, and wire n8n or Python services to your CRM so conversations become records, not dead ends.
How it works
We map every authoritative answer source — help centre, policy PDFs, past ticket resolutions, internal wikis — and rank by trust and freshness.
Documents are chunked semantically, embedded, and stored in a vector index with metadata filters (product, region, customer tier) so retrieval respects access boundaries.
Each user question triggers retrieval first; the LLM is instructed to answer only from retrieved chunks and to cite source titles inline.
Sub-threshold confidence, sensitive topics, or explicit human requests open a ticket in your CRM with the full conversation transcript and the retrieval trace already attached.
We build a gold Q&A set from your real ticket history; every prompt or model change runs through it before reaching production users.
Feature-flagged release on a low-risk channel (web chat) first, then widening to email triage and in-app once metrics hold for 14 days.
Weekly review of escalations and low-confidence answers feeds prompt tweaks, source updates, and new evaluation cases.
Frequently asked questions
How long until the chatbot is live?+
Three weeks from kickoff for a single-product, single-channel deployment, assuming source documentation is reasonably organised.
Will it hallucinate?+
The system is constrained to retrieved chunks and instructed to refuse questions outside its knowledge base — but no LLM system is hallucination-proof. Our evaluation harness and confidence-based escalation catch the residual risk.
Which channels does it support?+
Web chat, email, in-app widgets, WhatsApp Business API, and Slack are common. The retrieval and reasoning layer is channel-agnostic.
Can it handle multilingual support?+
Yes. We size the embedding model and prompts for the target languages and run a separate evaluation set per language.
Does it integrate with my existing helpdesk?+
Yes — Zendesk, HubSpot Service Hub, Intercom, and Salesforce Service Cloud are all supported via their APIs. Custom helpdesks need a webhook layer we can build.
What about data residency?+
We default to UK or EEA cloud regions for both vector storage and LLM inference where the model provider supports it. Strict-residency clients can run on self-hosted vector stores and proxy LLM endpoints.
Typical stack
Results you can expect
50%+ reduction in first-contact ticket volume
< 2 min average first-response time (vs hours manually)
85%+ self-service resolution rate on common queries
Escalation accuracy > 92% — complex issues always reach a human
Example locations
Industries
Integrations
Typical timeline
Live in 21 days
From kickoff to a feature-flagged production rollout for a single channel. Multi-channel and regulated deployments take longer; we always agree the cut-off date in the SOW before any code is written.
“The unlock with ai customer support chatbot is not the model — it's the evaluation harness, the escalation path, and the audit trail. We build all three from day one so the system holds up under real workload, not just the demo.”
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