Who builds the use case: Blits.ai or our developers?

The Blits.ai team builds it on the platform and connects it to your systems. Your developers give access and review; they can take over the build, or parts of it, at any point, because everything is configured on the platform rather than coded from scratch.

How does the agent connect to a core banking system?

Through a ready integration where one exists, through the REST and WebSocket API, or through a custom function we write for that system. Every tool carries an approval rule, so the agent acts only where you allow it. There are 158+ integrations to start from.

How is quality assured before go live?

Automated test suites run the typical and the difficult conversations before every release, guardrails check what the AI says and does, and your team tests with real scenarios during acceptance. The decision log records every agent step in production.

How long does a build take?

A Basic setup is live in days; enterprise deployments typically take four to eight weeks, depending on the integrations and the deployment option. The next use case reuses what the first one built.

How we build a use case and connect it to your systems and apps

The Blits.ai team builds your use case on the platform and connects it to your core systems, CRM and back office, and to your own mobile app and website, through 158+ integrations, the REST and WebSocket API and custom functions. Automated test suites run before every release.

Updated October 21, 2026

The build, step by step

Agent design
From the workshop's definition we design the agents: what each one handles, which tools it may use, where it asks a person, and the persona and tone your brand requires.
Knowledge base
Your approved content, policies and product information become the knowledge the agents answer from, with retrieval that cites what it used.
Integrations
The agents get tools that read and write in your systems, each with its own approval rule, so a transfer, a booking or a case update happens only where you allow it.
Channels and your app
The use case goes live on the channels from the workshop: your mobile app, your website, WhatsApp, telephony, a kiosk or a digital human.
Guardrails and tests
Guardrails on what the AI says and does, and automated test suites that run the typical and the difficult conversations before every release.
Acceptance and go live
Your team tests with real scenarios, we fix what it finds, and the use case moves to deployment with the decision log and monitoring in place.

How it connects to your systems

An agent is only useful when it can read and write in the systems that do the work. Four ways in, chosen per system.

158+ ready integrations

AI models, speech, cloud services, CRM, helpdesk, messaging and enterprise systems, connected with configuration rather than code.

See all integrations →

REST and WebSocket API

Connect any application or channel to the agents, with authentication guides, code examples and a full reference.

API documentation →

Custom functions

For a core system without a standard connector we write the function that reads or writes it, with the approval rule and the audit trail every tool carries.

MCP and A2A

Tools your organization already exposes over the Model Context Protocol, and other agents over A2A, plug into the same agent loop.

Integration standards →

In your own mobile app and website

The use case runs where your customers already are. In a mobile banking app it opens inside the app through the webview channel, uses the app's existing login, and acts on the customer's own data: a voice assistant inside a bank's mobile app is one of the deployments Blits.ai runs in production.

On the website the same agent runs as web chat or as a digital human, and through the API it reaches any other channel or application. One agent, every channel, with the same logic and guardrails.

Tested before every release

What goes live has been tested the way software is tested, and the way AI must be tested on top of that.

Automated test suites

Conversations as test cases, run before every release and graded automatically, so a change to a prompt, a model or a knowledge base cannot break what worked.

Platform features →

Guardrails

Checks on what the AI says and does: topic limits, tone, no financial advice where none is allowed, and grounding enforcement that sends an agent back to the knowledge base.

Security and governance →