How we deploy
We deploy AI where it makes the most sense for your company.
Every company has its own tools, data, security rules, and ways of working. We fit the system to your setup, whether that means using what you already have, building something dedicated, or combining both.
Where the system lives
It can run in your cloud, on your own servers, or across both.
In your cloud account
We set it up in the AWS, Azure, or Google Cloud account your company already controls. This is usually the quickest route.
On your own servers
When company rules require tighter control, the whole system stays inside your own infrastructure.
A mix of both
Sensitive work stays on your own systems while the rest runs in the cloud. We decide the split with you.
What we set up
Use the tools you have, build what is missing, or combine both.
Make the AI tools you already have useful
For companies that want results without a new platform.
We configure the AI tools you already pay for, connect them to real work, and help teams use them day to day.
- Choose tools around real work, not hype
- Connect them to the right data and process
- Measure use and results, then improve
Build a system your company owns
For companies that need something purpose-built.
We build a dedicated AI system around a complete business process, connected to your data and software. Your company owns and controls it.
- Handles the work and asks people to step in when needed
- Connects to the systems your company relies on
- Uses the model and setup that best fit the job
What makes it work
Three parts, chosen around the job.
We choose the right AI model, connect it to the information and tools it needs, and shape it around how the work actually happens.
- 01
The right AI model
We work with GLM, Qwen, Anthropic, Mistral, and other model families, from small models that can run locally to larger hosted models. We choose what best fits the job, budget, speed, and privacy needs.
- 02
Connections to your systems
We connect the AI to the business software and information it needs, with access limited to the job it has to do.
- 03
A clear understanding of the work
We map how the job really gets done, including handoffs and exceptions, so the system fits daily work and people actually use it.
Works with leading models
We choose the model that best fits the job, budget, speed, privacy, and where the system needs to run.
Runs in the environment you choose
We can use your existing cloud account, or keep the system on your own infrastructure when your rules require it.
In practice
Two common ways to get started.
Use the tools already bought
A services company had bought AI licenses, but few people used them in daily work.
We chose three useful workflows, configured and connected the tools, and helped the team make them part of everyday work.
Target: move from limited use to regular use by most of the team, measured weekly.
Build a system the company owns
A mid-sized insurer wanted its own system to speed up routine claims.
We built it inside the company’s cloud, connected it to policy and claims data, and kept every final decision with a person.
Target: cut time-to-decision on routine claims by 40 to 60%, with a human owning every final call.
Not sure which approach fits? The deployment review helps decide before any build begins.
Start with the work. We will recommend where and how the system should run.
We first agree on the result worth pursuing, then recommend an approach that fits your tools, rules, and team.