AI AGENT INTEGRATION

Let customers and partners use your service through AI agents

We integrate MCP and APIs so your service can securely accept requests from AI agents and perform only approved actions.

Discuss the integration

MCP compatibility

A single connection standard for Claude, Codex, ChatGPT, Hermes, and other MCP systems.

Real actions

Search, quotes, orders, bookings, requests, and status checks.

Controlled access

Role-based permissions, approvals, limits, and full action auditing.

USE CASES

Where can AI agents work with your service?

For services where customers, partners, or employees regularly interact with data and perform recurring operations.

B2B procurement

A partner's AI agent checks the catalogue, pricing, and availability, places an order, and retrieves its status directly from the system.

Booking and scheduling

A customer asks an AI agent to find a suitable time, make or reschedule a booking, or check its status. The action is completed directly in the system.

SaaS and business platforms

Users interact with the service through a familiar AI agent, without switching between interfaces.

Catalogues and marketplaces

An AI agent searches, filters, and compares products, properties, or offers, checks availability, and submits a request to the system.

INTEGRATION SCOPE

What we implement

We deliver a complete, end-to-end integration: MCP, APIs, access control, reliability, testing, and ongoing development.

MCP server

We expose approved service operations as MCP tools with clear parameters, outputs, and usage rules.

API for agent operations

We create or restructure the API layer with clear schemas, input validation, and predictable responses.

Authentication and access control

We configure OAuth, tokens, roles, and approval for critical actions. Each agent receives only the permissions it needs.

Reliability and auditability

We add request limits, duplicate-operation protection, error handling, and a complete action log.

Testing with AI agents

We test data access, operations, errors, and permission boundaries across multiple compatible agent systems.

Documentation and development

We prepare tool descriptions, schemas, and examples. After launch, we maintain versions and add new operations.

Examples of systems we integrate with

Integration is configured around the client's workflows, permissions and data model. We use available APIs, webhooks, MCP tools and custom adapters where required.

CRM & sales

SalesforceHubSpotZoho CRMBitrix24amoCRMPipedriveMonday CRMFreshsales

ERP & operations

OdooSAPMicrosoft Dynamics 365Oracle NetSuiteSageInforAcumatica

Communication

WhatsApp BusinessTelegramSlackDiscordMatrixMicrosoft TeamsGmailOutlook

Productivity & documents

Google WorkspaceMicrosoft 365Google DriveOneDriveSharePointNotionAirtable

Automation & data

n8nMakeZapierPostgreSQLSupabaseREST / GraphQL APIsWebhooksMCP

Other systems

Booking systemsCall tracking & telephonyAd accountsPayment providers

HOW WE WORK

From business scenarios to integration launch

Analysis

We define AI-agent tasks, user roles, and the required constraints.

API and access model

We design scenarios, data schemas, authentication, permissions, and approval for critical actions.

MCP integration

We build the MCP server, connect the API, and define tools for agent systems.

Testing and launch

We test scenarios, failures, and security, and deploy the production version.

Ongoing development

We add new operations, maintain versions, and monitor integration stability.

Where can AI-agent integration be useful for your service?

We will review customer, partner, and internal scenarios, assess the current API, and recommend an MCP integration architecture.

After the conversation, you will receive a list of priority operations, a recommended access model, and the appropriate integration format.