Conversational AI & Chatbots
An assistant your customers prefer to the human alternative.
Most chatbots are terrible. They hallucinate, they dead-end users into tickets, they repeat answers nobody asked for. We build the other kind: customer-facing AI assistants grounded in your actual data, wired to your actual systems, constrained by your actual policies, and continuously evaluated against real conversations. Use cases range from product concierge (answer feature questions, route demos, qualify leads) to support deflection (solve tier-1 issues before they become tickets) to sales qualification (screen inbound inquiries, book calls, update the CRM). Each one is a custom build - your data, your voice, your guardrails.
Trusted by teams backed by
You're here because something isn't working, right?
Common challenges we solve for teams like yours.
Hallucinations come from models answering outside their grounded context. We use retrieval-augmented generation against your docs, a strict tool-use pattern for structured queries, and a hard fallback to "I don't know - let me connect you with a human" when confidence drops below threshold. Trust is earned by refusing to guess.
A proven approach that delivers results
Use Case Scoping
Which conversations do we want the assistant to handle? What is explicitly out of scope?
Data Ingestion & Grounding
Docs, tickets, CRM, product DB, and knowledge base wired to a retrieval layer.
Agent Build
Prompts, tool-use patterns, guardrails, escalation logic, and voice alignment with your brand.
Evaluation Harness
Automated evals against historical conversations + golden-path test suite before any customer sees it.
Deployment
Widget, in-app, WhatsApp, iMessage, Slack, or voice - whichever surface your customers actually use.
Continuous Improvement
Weekly conversation review, eval updates, and prompt/retrieval tuning based on real usage.
Questions from Professionals Like You
- Off-the-shelf vendor bots are fine for generic support. Custom builds win when your product is specialised, your brand voice matters, your data policy is strict, or you need the assistant to take real actions (create tickets, book demos, update records) rather than just answer. The ROI case is usually: which path gets to 70% deflection without angering your customers?
- Both, via tool use. Common actions: look up order status, create or update support tickets, book demos via Calendly, fetch account-specific data from your CRM, trigger workflows in your ops tools. Every action goes through an authorization layer - the assistant cannot do anything its user role cannot do.
- Model inference, vector DB hosting, and monitoring are usually $0.5K–$5K/mo depending on volume and model choice. The build itself is one-time (6–10 weeks). The business case is typically measured in tickets deflected × fully-loaded agent cost, which usually makes payback obvious inside 90 days.
Your customers are one interaction away from giving up.
Book a scoping call and we'll show you which 2–3 conversations in your funnel are the highest-ROI candidates for AI automation - and what the custom build would look like.