Service · AI Chatbots

AI Customer Support Chat Agent

Your customers get real answers 24/7, pulled from your own docs and policies, so most queries get resolved without ever reaching your team. Built on LangGraph, the same framework leading AI teams use for controllable, production-grade agents, it knows when it doesn't have the answer and hands off to a real human instead of guessing.

Built on LangChain
Support assistant Grounded
What's your refund policy for online orders?
Online orders can be refunded within 30 days of delivery. Items must be unused and in original packaging. Refunds are issued to the original payment method within 5–7 business days.
↳ Refund Policy · §3 ↳ Shipping FAQ
Do you price-match competitors?
I don't see a price-match policy in your documents, so I won't guess. I've flagged this for the team. Want me to connect you with a human now?
no source found → human hand-off
Ask about orders, policies, hours…
Why businesses choose it

A support layer that pays for itself.

Book free consultation
80%

80% Customers served automatically

Most routine questions get resolved by the agent directly, so your team only sees the conversations that actually need a human.

24/7

24/7 Customer Support

Customers get accurate replies day or night, weekends and holidays included, without adding headcount.

Human in the loop

Never fully unsupervised

Anything sensitive or uncertain gets handed to a real person instead of guessed at, so nothing risky runs on autopilot.

LangGraph

Built on a controllable framework

The same technology leading AI teams use for production grade agents, not a drag and drop bot that breaks under real questions.

Connected to your tools

Not just a chat window

Linked to your CRM, order system, and other tools, so it can actually look things up and get things done, not just talk.

App + WhatsApp

Meets customers where they are

One assistant deployed across your website and WhatsApp, so customers get the same grounded answers wherever they reach out.

Agentic + human-in-the-loop

It routes, calls tools, and knows when to ask a human.

Every message is classified and routed to the right action. Simple questions get answered from your knowledge base. Order lookups call your systems. And anything sensitive, a large refund, an edge case, an unhappy customer, pauses for human approval or escalates automatically.

Classifies each message into a clear intent before doing anything
Routes down the right branch: knowledge, order lookup, refund, escalation
Pauses for human approval on high-value actions like large refunds
Resumes exactly where it left off once a human decides
incoming message
"Where's my order #4021?"
classify → order_issue
Knowledge question answer from knowledge base (RAG) auto
Order lookup call your systems via a tool auto
Refund / payment high value → human approval gated
Escalation hand off to a person human
Capabilities

More than a FAQ box. It takes action.

Answers from your content

Trained on your real docs and policies, so replies match what your team would actually say. No generic filler.

Cites every source

Each answer links back to the document it came from, so customers and staff can verify it in one click.

Won't make things up

If the answer isn't in your knowledge base, it says so plainly instead of guessing. That's the single most important safety behavior.

Calls your tools

Look up an order, check availability, create a ticket, trigger a workflow. The bot does the task, not just the talking.

Human hand-off

Unclear questions, upset customers, and sensitive actions route to a person automatically, with the full conversation attached.

Multilingual & always on

Answers in your customers' language, 24/7, across every channel, nights, weekends, and holidays included.

Under the hood for the technical buyer

How I actually build these systems.

Two cooperating layers: a retrieval service that grounds answers in your data, and an agent that classifies, routes, and calls tools, with a human in the loop for anything sensitive. Model-agnostic, so I use the right LLM for the job.

Retrieval layer
grounds answers in your data
ingest
Chunk & embed
Your content is split into passages and embedded with a sentence-transformer model into vectors that capture meaning.
store
Vector database
Embeddings live in PostgreSQL + pgvector (Supabase), so retrieval scales with your knowledge base.
query
Similarity search
Each question is embedded, and the closest passages are retrieved and ranked by relevance.
answer
Grounded generation
An LLM answers using only those passages and cites sources; if nothing matches, it declines instead of guessing. Served as a validated FastAPI endpoint.
Agent layer
classifies, routes & acts
classify
Intent detection
A structured-output LLM tags each message with a single intent before anything runs.
route
Conditional graph
A LangGraph state machine routes on that intent down the right branch with conditional edges.
tools
Tool calls
Branches call tools: the retrieval service, order lookups, ticket creation, workflow triggers.
human
Human-in-the-loop
Sensitive actions interrupt the graph, checkpoint state, and resume exactly where they paused once a person approves.
Models
OpenAIAnthropicGroq / Llamasentence-transformers
Retrieval
pgvectorPostgreSQLSupabase
Orchestration
LangGraphLangChaintool callingcheckpointer
Serving
FastAPIPydanticwebhooksREST
Use cases

The same engine, tuned to your industry.

E-commerce & retail
"Where's my order, and can I still return it?"

Order status, returns, sizing, and stock, answered from your policies and connected to your store.

Healthcare & clinics
"Do you take my insurance, and what should I bring?"

Intake questions, hours, and prep instructions handled from approved docs, with anything clinical routed to staff.

Real estate
"Is this listing still available for a viewing?"

Instant lead response and qualification, pulling details straight from your current listings and CRM.

Professional services
"What's included in your onboarding package?"

Scoping, pricing tiers, and process questions answered consistently, so every lead gets the right story.

Hospitality
"What time is check-in and is parking included?"

Booking, amenities, and policy questions across web and WhatsApp, freeing the front desk for guests.

SaaS & tech
"How do I connect my account to Slack?"

Product and troubleshooting answers grounded in your help docs, with tickets created for the hard cases.

FAQs

Chatbot questions, answered

The core questions businesses ask before deploying a chatbot grounded in their own knowledge.

Generic models answer from what they were trained on and will confidently guess about your business. Mine retrieves answers from your own documents first, cites the source, and refuses to answer when the information isn't there, so it can't invent policies or prices you never set.

No. It takes the repetitive questions off their plate, order status, FAQs, policy lookups, so your team only handles the conversations that actually need a person. Most businesses run it alongside their team, not instead of one.

Every model can technically get something wrong. The difference is what happens next. Mine checks its answer against your own documents before it replies, and if it isn't confident, it says so and hands off instead of guessing. That check is the whole point of building it this way, not an afterthought bolted on.

Whatever knowledge you already have, in almost any form: help docs, SOPs, policy PDFs, a product catalog, a spreadsheet of FAQs, even old support tickets. I handle turning that into a searchable knowledge base. You don't need anything technical on your end.

Send me the updated document, or edit it in the source I connect to. Re-indexing takes minutes, and the bot answers from the new content right away. No retraining, no code changes for you.

It says so instead of guessing, and hands the conversation to a human with the full context attached. You decide exactly when it escalates and to whom.

A website widget, WhatsApp, email triage, and inside the tools you already use. It can also take action: look up an order, create a ticket, trigger a workflow, not just answer a question.

Most chatbots go live in 1 to 3 weeks, depending on how much knowledge I'm ingesting and how many integrations you need. I test it against real questions before it ever reaches a customer.

Schedule

See what a grounded chatbot could do for your business

Book a free audit. I'll look at your docs and support volume and show you exactly what to automate first.

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