Want to see how the controls work?
We'll show you how the agent works with rules and validation in practice.
We use AI where it's strong: understanding human messages and natural communication.
But the critical parts of the process are controlled by the system: data, rules, states, allowed actions, validation, and message delivery.
That's why SnipCat feels natural in conversation, but inside it works as a controlled agent with clear boundaries.
Converts an unclear human message into a structured query.
The agent answers from specific data, not from its own memory.
Conversations run in clear states and the agent knows what's currently allowed.
A separate control layer verifies the message before sending.
AI doesn't decide the recipient or channel. The system controls that.
Every step is traceable and explainable.
AI safety for us isn't just text on a website. It's the product architecture.
At SnipCat, we combine multiple layers that together limit errors, hallucinations, and uncontrolled agent behavior.
Standard RAG often works by finding similar text and sending it to the AI model as context. That may be enough for simple queries, but business communication requires greater precision.
At SnipCat, we first convert data into a structured index. It's not just vector search. Indexes are stored so they can be queried by a database and reviewed by a human.
The agent first understands what the prospect is looking for. Then it uses rules from your settings to determine which indexes the database should search. The database returns only the relevant context the agent has access to for that use case.
For example, when a prospect writes: "I'm looking for an apartment for a couple without kids, in a good area for walks," the agent knows from the rules to primarily search for 2-bedroom units. It also converts "good area for walks" into indexes like proximity to parks, rivers, green spaces, or quiet neighborhoods.
The agent doesn't make up the result. It passes the interpretation to the database as a structured query. The database finds matching properties and returns specific data the agent can safely work with.
The result: you can see what indexes each property has, what rules the agent used, how it interpreted the query, and what context it received.
The agent doesn't run conversations as free chat, where it reinvents what to do next with every response. Each conversation runs in states that determine what phase the prospect is in and what next step is allowed.
When scheduling viewings, a conversation might go through states like: new prospect, qualification, waiting for response, time selection, viewing scheduled, handoff to human, or conversation ended.
This means the agent knows what to handle at any given moment. During qualification, it asks questions according to the rules. When it has enough information, it can offer a time slot. When the situation is unclear, it can hand the conversation off to a human.
AI doesn't take control of the entire process. It helps with understanding and crafting responses, but the conversation flow itself is managed by the state system.
The agent's response isn't sent automatically right after generation. It first goes through a separate validation layer.
This validation is another AI request that operates in a different mode than the agent itself. It receives only the source of truth from your settings and the response the agent wants to send. Its job isn't to continue the conversation, but to verify whether the proposed response is safe and follows the rules.
The validation checks, for example, whether the response:
AI also doesn't decide who receives the message. The recipient, channel, and allowed next step are determined by the system based on conversation state.
If the response doesn't pass validation, the agent must revise it. If it can't, the conversation stops or is handed off to a human according to the configured rules.
Together, this creates an agent that isn't just a "chatbot connected to data." It's a controlled system for business communication where AI helps with understanding and crafting responses, but rules, data, next steps, and message delivery remain under system control.
Less room for hallucinations.
Lower risk of manipulation.
Better traceability of every step.
We don't sell "magical AI." We build agents with clear rules, control, and auditable decision-making.
We'll show you how the agent works with rules and validation in practice.