Solutions

Less distance between intent and execution.

Practical approaches to putting AI to work across development, administration, and engineering.

01

AI-assisted development

From intent to reviewable code.

Design tools that help agents understand your environment, draft extensions, and prepare tests.

Illustrative use case · Example: draft an application script and a test plan for developer review.

Discuss your workflow
02

AI-driven software configuration

Make configuration a deliberate workflow.

Connect approved configuration operations with validation and explicit review.

Illustrative use case · Example: propose a workflow change, inspect the diff, and validate in a sandbox.

Discuss your workflow
03

Engineering & CAD automation

More time for engineering judgment.

Scope repeatable drafting, modeling, and geospatial tasks against supported desktop APIs and SDKs.

Illustrative use case · Example: generate a parameter-update script and validate it on a test model.

Discuss your workflow
04

Custom MCP servers

A precise toolset for your agents.

Expose purposeful operations with clear schemas, permissions, and understandable results.

Illustrative use case · Example: provide a scoped tool for checking deployment prerequisites.

Discuss your workflow
05

Enterprise workflow automation

Connect the steps between systems.

Design cross-system workflows with clear error handling and human approval where needed.

Illustrative use case · Example: prepare a change request from a validated development task.

Discuss your workflow

Built around your business

Your stack. Your workflow.
Your next possibility.

Let’s connect your AI to the work that matters.

Let’s talk about your project