Compatible with every major AI agent and IDE
Increase create account on Increase
Spin up a new Bank Account programmatically
Increase create ach on Increase
Push an outbound ACH transfer to any US Bank
Increase create card on Increase
Issue a physical/virtual debit card attached to an account
Increase create routing number on Increase
Generate new ABA routing & account number data
Increase create wire on Increase
Send a same-day US Wire transfer
Increase get balance on Increase
Fetch realtime ledger balance for a specific account
Increase list accounts on Increase
List all sub-accounts under your charter
Increase list cards on Increase
Sweep the active array of issued Cards
Increase list transactions on Increase
Financial history extraction (Booked)
Increase list transfers on Increase
Audit outbound transfers
Increase simulate inbound ach on Increase
Simulate receiving an ACH inbound (SANDBOX ONLY)
Increase simulate inbound wire on Increase
Simulate receiving a Wire inbound (SANDBOX ONLY)
How Vinkius protects your data
Is there a risk of the AI "going crazy" and deleting important company data?
No. With Vinkius, the AI operates on "rails". It can only make the exact moves you authorized in the tool's settings. It cannot invent routes, access other networks in your company, or decide to delete random files. If the action isn't in the approved catalog, the attempt is blocked instantly.
Can I test money flows safely without losing real funds?
Absolutely. Increase has a top-tier sandbox environment. In your MCP settings, pass a Sandbox API Key (starting with sk_test_) and optionally set the environment to 'sandbox'. We even implemented literal tool overrides like increase_simulate_inbound_ach which are intentionally restricted exclusively to simulating inbound events on your test environment.
How does the AI access my passwords and credentials?
It simply doesn't. On Vinkius, your passwords, API keys, and login details are kept in a secure vault. The AI (like ChatGPT or Claude) merely "asks" Vinkius to perform the task. Vinkius opens the door, does the work, and hands the result back to the AI. Your credentials are never seen, read, or learned by the artificial intelligence.
What if the AI ends up reading customer data or confidential information?
We have a built-in digital "bodyguard" called DLP (Data Loss Prevention). If a tool fetches data and the response contains social security numbers, credit cards, or personal customer info, Vinkius magically blocks and erases that information before it is delivered to the AI. The AI works only with what is strictly necessary, and your sensitive data never leaks.
What can AI Agents do with Increase?
We map standard API endpoints to agent-compatible instructions. Connect Increase to execute these core functional operations.
Optimizing commercial banking with Claude
The Increase MCP integration translates natural language prompts into structured commercial banking queries. This allows agents to fetch and update money moves records securely.
Next-Gen payment rails Automation
The Increase toolkit provides structured tools for payment rails. It enables conversational interfaces like Claude Code to query and modify data within your money moves infrastructure.
Increase. Runs on everything.
From IDE to framework. Every connection governed by Vinkius.
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
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