Customer onboarding
An agent combines CRM records and contract details into a draft onboarding plan. Your team approves changes before they reach the source systems.
Connect your software to AI agents.
Depending on the task, we build an API, an MCP server that AI platforms can use, or WebMCP tools for browser agents. Each one works with your existing applications, data, and business rules, within the limits you set.
Example integrations
These are the kinds of connections we build into existing products and internal tools.
Account upgrades
An agent helps a customer compare plans, check compatibility, and request an upgrade. Your application still enforces pricing, eligibility, and confirmation.
APIs, MCP, and WebMCP
An API connects one application to another. MCP (Model Context Protocol) makes selected data and actions available to compatible AI platforms. WebMCP offers actions to browser agents during a browser session. We can build one of these or combine them around the same application logic.
API development and integration
We add APIs to existing software or connect the APIs you already have. Records and workflows become available to external tools, partner applications, and internal systems, with authentication, validation, and clear documentation.
Explore application integrationMCP server development
We build an MCP server for your application or connect it to existing MCP tools. We define each tool’s inputs, responses, and permissions, then test compatibility with the AI clients you need to support.
MCP architecture referenceWebMCP implementation
We add structured tools to your existing web application so compatible browser agents can search records, complete forms, and request actions within the user’s session.
The regular interface stays as it is, and we test support in the browsers and agents your users rely on.
Explore WebMCP implementationChrome’s explanation of MCP and WebMCP. Technical references reviewed September 9, 2026.
Agent harnesses that control execution
The harness is the software around the model. It manages the model’s context, tool calls, and task progress. We build or adapt it so your limits are enforced in code, outside the model.
It saves progress and checks which actions already completed before it retries.
It enforces user permissions, protects credentials, and requires approval for sensitive changes.
It caps spending and retries, blocks duplicate changes, and escalates errors it cannot resolve.
We test real tasks and failure cases before releases and after model changes.
From technical review to implementation
Start with an assessment, a defined build, or a program spanning several systems.
Assess
We review an application or agent prototype, find what blocks agent use, and help you decide what to build, repair, or leave alone.
Build
We deliver one complete integration, tested against your team’s tasks and acceptance criteria.
Expand
We connect more systems in staged releases, with shared access rules and clear maintenance responsibilities.
Source code and documentation
Builds include source code, tests, compatibility notes, and operating instructions. Assessments deliver findings and recommendations.
- Review of the existing system and its interfaces
- Documented APIs and tool definitions
- Access controls and compatibility tests
- Source code, tests, and deployment handoff
When an API is enough
A scheduled API call can handle many recurring tasks. Agents are more useful when the next step depends on interpreting a document or request. We compare both approaches during the assessment.
Assess agent readinessHave a task in mind?
Tell us which software you use, what you want it to connect to, and the task that should become easier.