AI & Machine Learning
Build and orchestrate AI agents
Kliv is an AI app builder. Describe the agent’s knowledge, permissions, actions, handoffs, and schedules, and it builds the agent inside the app it serves.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t just a chat box or an agent dashboard — it’s an AI that builds software for you.
You describe the app and the agent you need in plain words, and Kliv builds the real app: the data, the screens, the knowledge base, the permissions, the actions, and the transcripts. You own it, run it on your own accounts, and change it later by asking. It isn’t a template.
On this page, the app includes AI agent creation and orchestration. Kliv builds all kinds of web apps; this page focuses on agents that live inside those apps and follow their rules.
Why agents belong inside the app
Many agent tools give you a chat window and leave the hard parts to you: where the knowledge lives, what the agent may touch, who can see which data, and when a human should take over.
Kliv builds agents inside the app they serve. A distributor’s agent can read the same catalog rows the storefront uses, see the signed-in customer’s own orders and nobody else’s, and act only through capabilities you granted. Orchestration means permissions, schedules, transcripts, and escalation paths built into the app.
What an agent is made of
The records and rules an agent needs before you trust it with users.
The knowledge base
Spec sheets, torque tables, catalog PDFs, policies, and manuals are uploaded and indexed. The agent answers from your documents, without a vector database for you to run.
Capabilities
Each action is granted one by one: look up an order, draft a quote, file a return, book a slot. What you do not grant is not available.
The user’s access
The agent inherits the signed-in user’s row-level access. A customer asking about their order gets their own rows; a competitor asking cleverly gets nothing.
Transcripts
Every conversation is stored as rows in your database. You can review them, search them, and use them to improve the knowledge base.
Human handoff
When a question should stop being automated, the app opens a support thread with the transcript attached. A person starts with the context already present.
Scheduled work
Agents can also support scheduled jobs, such as flagging stale quotes each night or summarizing unanswered questions into Slack every Monday.
An example: Samira’s parts distributor
Here’s how one distributor might use an agent built with Kliv. It’s only an example — you would describe your own documents, users, actions, and escalation rules.
The documents become searchable
Samira uploads 40 catalog PDFs and the returns policy. Indexing runs automatically, and the agent can answer product questions from those files.
Order lookup is granted deliberately
The agent first answers policy questions only. Then Samira grants order lookup, so it can read a signed-in customer’s own order status under the same rules as the portal.
The access boundary is tested
A Scenario signs in as customer A and asks about customer B’s order. The test uses the real app on a disposable database copy and confirms the read is refused.
Escalation is part of the job
Unresolved conversations open support threads with the transcript attached, and Slack pings the right support channel. The agent knows when to stop.
Transcripts show what to improve
After launch, Samira reads the transcripts and finds missing catalog language. She updates the knowledge base where the agent stumbled.
Describe the agent you want
Kliv builds from your description, so include the documents it should answer from, the actions it may take, the users it serves, and when it should escalate. Here are three examples:
Order-desk agent
Manuals, order status, returns, and transcript logging.
“Build a customer portal for my industrial parts distributor. Add an AI agent that answers from uploaded product manuals and torque tables, checks the signed-in customer’s own order status, files return requests only for orders inside the return window, escalates uncertain cases to support, and stores every transcript.”
Booking agent
Service questions and live availability.
“Build a scheduling app for my repair business. Add an agent that answers from uploaded service descriptions, shows only available appointment slots, books a slot for a signed-in client, sends a confirmation email, and escalates pricing exceptions to a staff thread.”
Internal policy agent
Staff procedures with escalation.
“Build an internal tool where employees ask an agent questions answered from uploaded HR and operations procedures. Store every conversation, let unresolved questions open a support thread, and restrict policy categories by employee role.”
How orchestration works
Different agents can have different jobs. One app can have a customer-facing agent for order lookup and returns, and an internal agent that reads margin data. The boundary is the capability grant and the row rules.
Identity comes first. Agents serve signed-in users: email and password, Google or Facebook sign-in, passkeys, or two-factor. The app knows who is asking before the agent answers.
The feedback loop is built in. Transcripts show where the knowledge base is thin. You can add documents any time, and changes ship through the same tested publishing flow as the rest of the app.
What agents can be granted, how indexing works, and what transcripts look like in practice — the agents deep dive walks the whole surface.
Common questions
What exactly is Kliv?
Kliv is an AI app builder. You describe the web app and agent you need, and Kliv builds the app, the agent, the data, the permissions, and the screens around it.
Do I get a real app, or a template?
A real app. The agent runs inside an application built for your data, users, and workflows. It is not a generic chatbot template.
Can I change it after it’s built?
Yes. You can add documents, change capability grants, adjust escalation rules, or add another agent by asking for the change.
What keeps the agent from making things up?
It is grounded in the knowledge base you author and acts only through capabilities you grant. When the documents do not contain an answer, the right path is escalation with the transcript attached.
Can the agent take actions, not just answer?
Yes. It can take actions you explicitly grant, such as looking up an order, booking a slot, filing a request, or drafting a quote. Each action runs under the signed-in user’s permissions.
How do I see what it has been saying?
Every conversation is stored as a transcript in your own database. You can read, search, and audit those transcripts.
Do I need to run AI infrastructure?
No. Knowledge-base indexing, the agent runtime, and transcript storage are part of the app Kliv builds. You do not run a vector database, queue, or separate AI server.
Can two agents have different access?
Yes. A customer agent can have order lookup and returns while an internal agent can read staff-only data. Capabilities and row rules define the boundary.
Can customers see each other’s data through the agent?
No. The agent reads through the same row-level access rules as the app. A signed-in user only gets rows they are allowed to read.
Can I move off Kliv later?
Yes. The app’s code syncs to your own Git repository, so leaving is a git pull rather than a rebuild.
Can I use my own domain?
Yes. The app can run on your own domain, with the agent inside it.
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Give the agent a job, not just a chat window
Describe what it should know, what it may do, who it serves, and when a person should take over. Kliv builds the agent inside the app that enforces those rules.
