Website knowledge agent
A chat‑first version of a company website. An AI agent answers questions about the firm from approved content and broader Salesforce questions from its own knowledge, gauges whether the visitor is a good fit, and offers a booking calendar.
- Checked against 18 scripted test conversations, including attempts to pry out prices, client names, and its hidden instructions.
What it does
Visitors ask the website their questions and get answers about the firm from content the firm approved, instead of digging through pages. As they talk, the agent picks out their company size, industry, problem, and timeline, scores the fit, offers a booking calendar, and can alert the sales team in Slack. Its rules bar it from quoting prices or naming clients.
The situation
Maple DX, a Salesforce consulting firm that builds AI agents for its clients, described its services on a static website. It wanted visitors to talk to an agent instead of reading pages, so the site itself would show the kind of work the firm does. In front of buyers the agent had to follow strict rules: no prices, no client names, and no made-up facts. The firm chose to judge it mainly by how many conversations are with good-fit buyers.
What was built
A chat page where each visitor message goes to Claude, Anthropic’s AI model, along with a fixed set of rules and the firm’s full knowledge base. The answer streams to the screen as it is written, with cards for the firm’s team, industries, and sources, a short read on fit, and an embedded booking calendar. The server fills those cards only from content the site owns, so an invented person, credential, or link cannot appear on them. The one exception is the fit card, which shows the model’s own short summary.
The server also scores each visitor’s fit from a fixed point table, saves conversations to a database, and limits how many messages a visitor can send and how much the agent can spend in a day. A person gets involved through the booking calendar, a sales alert in Slack where one is set up, or an email address the visitor types in.
It was deployed to its own web address in August 2026.
How it works
Start a session
On the first visit the site sets one session cookie that page scripts cannot read. Nothing else is stored in the visitor’s browser.
Check the request
Before a message reaches the AI, the server checks where the request came from, the daily spending limit, and the per-visitor message limits. Where one is set up, it also runs an invisible bot check on the first message. A visitor who reaches a limit gets a short message with a link to book a call.
Load the conversation
The browser sends only the new message. Earlier turns come from the server’s own store, so nobody can slip fake earlier replies into the conversation.
Ask the model
Claude gets the firm’s rules (never quote prices, never name clients, never invent credentials, and ignore instructions hidden in a message) and the whole knowledge base. The rules tell it to answer questions about the firm only from that knowledge base, to give its own view on broader Salesforce questions, and to decline anything off topic briefly and politely.
Stream and check the reply
The answer text streams to the screen as it is written. When it finishes, the rest of the model’s output is checked against a strict format, and anything malformed falls back to safe defaults instead of an error.
Fill the cards
The server matches the model’s card choices to content the site owns: the team, industries, approved source links, and the booking calendar. An invented name or link has nothing to match, so it never reaches those cards.
Score, save, and alert
The server scores fit from a fixed point table, using the details the model picked out: company size, whether they run Salesforce, industry, the problem they named, and their timeline. It saves the conversation and, when set up, alerts sales in Slack once the score passes a set level or the visitor asks for a document.
How it is tested
Before launch, a battery of 18 scripted conversations runs against the live model with the production rules and knowledge base. Half test the guardrails: prices asked for several ways, pressure to name clients, someone posing as an AI company employee asking for the hidden instructions, instructions slipped into a message, and a visitor who is a clear poor fit. The other half test scope: good answers to real Salesforce questions, care on topics where it should hedge, and polite declines for off-topic questions or requests to write documents or code. A script grades each conversation and a person reads the full transcripts.
The battery runs before a release and tests the model and its rules directly. Unit tests check how the chat page reads the streamed reply, and a scan keeps secret keys out of the code sent to the browser.
Tools and who built it
- Tools
TypeScript, Next.js, React, Tailwind CSS, Zod, Anthropic Claude, Supabase (Postgres), Netlify, Vitest
- Who built it
Stratyx Ventures
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