LLM-Powered Customer Assistants
Customer Assistants That Resolve Tickets, Not Just Chat
Intelligent support agents that resolve tickets, escalate edge cases, and stay current with your product knowledge, deployed via web chat, Slack, or API.
74%
Of tickets resolved without a human agent
91%
CSAT held post-deployment
4,000+
Tickets/month handled, one deployment
6 weeks
Typical MVP deployment
Beyond a scripted chatbot
An assistant that actually resolves the issue
A scripted chatbot follows a decision tree and hands off the moment a customer says something it wasn't programmed for. An LLM-powered assistant reasons about the actual request, retrieves the relevant order data or documentation, and takes the resolution action itself, whether that's issuing a return label or answering a product question with current, accurate detail.
The engineering that makes this safe is retrieval grounding, confidence thresholds, and clear escalation logic, not a bigger decision tree. We build all three in from day one.
Grounded
Answers pulled from your real data
Escalates
Hands off complex cases, doesn't guess
Multi-channel
Web chat, Slack, or API
Always current
Synced with your latest product knowledge
What's included
A support assistant that ships, not a proof of concept
Ticket Triage & Auto-Resolution
Incoming queries classified and resolved directly for the common cases: order status, returns, product questions.
RAG-Grounded Product Knowledge
Every answer retrieved from your live documentation and order data, not the model's general training knowledge.
Escalation & Human Handoff
Confidence thresholds route complex or sensitive cases to your human team with full conversation context.
Multi-Channel Deployment
Live on web chat, Slack, or via API, so the assistant meets customers where they already are.
CSAT & Quality Monitoring
Every resolved conversation tracked against customer satisfaction, not just resolution volume.
Continuous Knowledge Sync
As your product docs and policies change, the assistant's knowledge base updates automatically.
Ecommerce: Support Automation
74%
of tickets resolved without a human agent
We built an LLM-powered customer assistant for a D2C brand handling 4,000+ tickets a month. It resolves order tracking, returns, and product queries autonomously, escalating only complex cases to the human team. CSAT held at 91% post-deployment, no drop in customer satisfaction as automation scaled up.
FAQ
Common questions about customer assistants
We ground every response in retrieval-augmented generation against your actual product documentation, order data, and policies, rather than letting the model answer from general training knowledge. We also set confidence thresholds: below a certain confidence level, the assistant escalates to a human instead of guessing. This is why our deployments hold customer satisfaction scores steady even as automation rate increases.
No. In our most mature deployments, the assistant resolves around 74% of incoming tickets autonomously, escalating the remaining complex or sensitive cases to your human team. That frees your support team to focus on the tickets that actually need human judgement, rather than answering the same order-tracking question for the hundredth time that week.
We deploy customer assistants via web chat widgets on your site, Slack (for internal or partner-facing support), and API (for embedding into your existing support platform, mobile app, or product). Most clients start with web chat and add channels as the assistant proves out.
We build with data privacy by design: customer data is processed within your cloud environment (AWS, Azure, or GCP), and we use enterprise API agreements (such as Azure OpenAI, which processes data in your Azure tenant) so nothing passes through third-party servers unnecessarily. We sign NDAs and data processing agreements before any scoping work begins.
Ready for an assistant that actually resolves tickets?
Tell us your support volume and product knowledge base. We'll scope an assistant for it in one call.
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