A support chatbot that actually knows your policies, orders, and cases — not just your FAQ page.
A chatbot that answers from general knowledge instead of your actual policies, orders, or case records is a liability, not automation — it confidently gives wrong answers and loses trust the first time it's tested. We build support assistants grounded in your real data, scoped to what they're allowed to say and do, with a clean handoff to a human for anything outside that scope.
Where this fits
Healthcare intake & patient support
Insurance, appointment prep, and clinic-policy questions answered from your actual intake documents, not general medical advice.
Insurance policy & claims support
Coverage, claims status, and quote questions answered from your real policy documents and claims system instead of a canned script.
Property management & tenant support
Maintenance requests, lease questions, and prospect FAQs routed and answered from your actual property and lease data.
E-commerce order & returns support
Order status, return eligibility, and product questions answered from your real order and inventory data instead of a static FAQ.
Internal knowledge support
The same grounded-assistant approach turned inward, for staff who need a policy or procedure answer without digging through a wiki.
What we deliver
Grounded answers
Retrieval against your real documents, policies, and records, so answers reflect what's actually true instead of general training data.
Ticket & case lookup
The assistant looks up a real order, claim, or maintenance ticket instead of asking the customer to repeat details a human would already have.
Human handoff
Anything ambiguous, sensitive, or outside its scope routes to a person, with the full conversation history attached.
Multi-channel deployment
The same assistant on your website, SMS, or an existing support tool, instead of a separate bot built per channel.
Guardrails & scoped access
The assistant only sees and says what a given role or use case allows, so it can't leak data it shouldn't have.
Analytics & deflection tracking
See what's actually being asked and how much volume is deflected, not just an anecdotal sense that the bot is helping.
Continuous retraining
As your policies or catalog change, the knowledge base updates instead of quietly drifting out of date.
How we run this engagement
Discovery
Map your ticket volume and the systems — orders, policies, records — the assistant needs to be grounded in.
Design
Structure retrieval and handoff rules around your actual data and escalation paths.
Build
Prompt and agent design with systematic evaluation against real questions before rollout.
Launch & iterate
Ship with guardrails and logging, then refine based on what's actually being asked.
Built on a proven, modern stack
Claude API
Careful, instruction-following responses for an assistant that represents your brand.
OpenAI
An alternate model option evaluated case by case for cost and quality.
Vector DBs
Answers grounded in your documents and records, not the model's general training.
Node.js
A fast, realtime-friendly backend for chat interfaces that feel instant.
PostgreSQL
Stores conversation history, tickets, and escalation records.
Support tool integrations
Connects into the helpdesk or CRM you already use — Zendesk, Intercom, or your own ticketing system — instead of replacing it.
Things people ask before starting
Can't find what you're looking for? Reach out and we'll answer directly.
Off-the-shelf widgets often answer from the model's general knowledge instead of your actual policies or records, which is exactly how they end up confidently wrong. We ground every answer in retrieval against your real data and evaluate accuracy before launch.