Five ways we put AI to work for you
Same team, same approach as everything else we build: solve your specific problem, not a generic one.
AI-Assisted Development
We build with AI coding tools every day, so your project ships faster without cutting corners on quality or review.
Automation
We find the manual, repetitive work slowing your team down and hand it off to something that runs it for you, reliably, in the background.
Agentic Systems
We design and deploy agents that carry out multi-step work on your behalf, not just answer questions.
Integrations & MCPs
We connect AI tools to the systems you already run, using the Model Context Protocol and custom integrations alike.
Purpose-Built Models
We're building models trained specifically for your business and run in an environment segmented from the public internet, so your data stays yours.
Your own model, trained on your business, walled off from the rest of the world
Public AI tools share infrastructure across every customer using them. We build and run models purpose-built for a single company, in an environment segmented from outside access, so what your business feeds into the model never leaves it.
- Trained on your data and your workflows, not a generic public dataset
- Run in an environment isolated from the public internet
- No shared infrastructure with other companies' models or data
What clients ask before bringing AI into their systems
We're wary of AI touching our data. How do you handle that?
That concern is exactly why we're building purpose-built models that run segmented from the outside world. Your data trains and informs a model that answers to you, not one shared across every customer of a public AI vendor.
Do we need to already have an AI strategy before talking to you?
No. Most clients start with one specific bottleneck — a manual process, a slow workflow, a system that should talk to another system and doesn't. We start there and build out from what actually helps.
Is this a bolt-on chatbot, or something deeper?
Deeper. Agentic systems and MCP integrations mean the AI can take real, multi-step action inside your existing tools, not just respond in a chat window bolted onto the side of them.