Icebreaker
From AI tools nobody uses to a team that runs on them.
We embed with your experts, build your first agents around the work they actually do, and leave your team able to run and maintain them. Zero to fully running.
Start a conversation“Clearly some powerful alien tool was handed around, except it comes with no manual and everyone has to figure out how to hold it and operate it.”
Why It Stalls
Why AI adoption stalls
The gap between “we bought the AI tools” and “our people actually use them well” is where the value gets stuck. The causes are structural, not a matter of trying harder.
The skill gap.
People have the tools. Most were never shown how to use them well. Average prompting proficiency sits at 2.3 out of 10.
The imagination gap.
Teams can't see where AI fits the work in front of them, so it stays a novelty instead of a habit.
The mentor gap.
Most workplace skill is learned on the job, from someone who has done it before. With AI, there is no one to learn from yet. We become that person for your team.
The journey
Most companies are at zero: the licenses are bought, the tools sit unused. We take a team to running on its own: using agents built around real work, reviewing outputs with judgment, and extending the system without us. That path, zero to fully running, is the whole point.
Built in your environment
We work in the environment you already run.
You have already invested in a stack. We build inside it rather than asking you to adopt another one, and we stay agnostic on the model layer so the choice fits your business.
- Deep in Microsoft, at home anywhere.
- A team of Azure-certified AI engineers who build where your people already work, whether that is Microsoft, Google, or AWS. The approach carries to whatever your stack runs on.
- Provider-agnostic on models.
- Every business has different needs and preferences for its LLM stack. We build around yours, not around one model vendor we happen to prefer.
- No new platform to buy.
- We build with the tools you already pay for. Trusted advice on what fits your business, not a pitch for things you don't need.
How We Work
The embedded model: we build the agents, your team learns to own them.
Most firms deliver a black box and stay attached to it. We work beside the people who do the job today and build the first agents around their judgment. We set your team up to operate the agents with judgment: knowing when the output can be trusted, where it needs a closer look, and where a person signs off before it is used. And we leave the system implemented, documented, and maintainable, so your team can keep it running and scale it as the work grows. The aim is a system your team owns, not a dependency on us.
- 01
Embed.
We work with the people doing the task today and map where an agent helps in a real workflow, not in a slide deck.
- 02
Build.
Your experts provide the judgment. We bring the AI methods. We build the first agents alongside them, using the actual inputs and review steps the work requires.
- 03
Adopt and own.
We make your team effective at operating the agents: how to judge the output, how to catch failure modes, and where human sign-off belongs. Adoption is most of the work, and where many AI efforts stall.
- 04
Maintain and scale.
We leave the system documented and maintainable, and your team ready to keep it running and roll it out to more of the work over time. We stay available. You are not left dependent on us.
Most AI pilots don’t fail on the technology. They fail on people and process, the part that is most of the work and rarely gets funded. We put our time there.
What We Do
A focused engagement, not an open-ended retainer.
We don't start with company-wide autonomy or promises about transforming everything. We come in already understanding your business, so we don't burn your team's time getting up to speed, and we start with the repeatable, low-value work eating their day. We turn it into small agents your team can run, so their hours go to the high-value judgment work instead.
- Small agents for real low-hanging fruit, not a company-wide orchestration system.
- A decomposition approach that maps how your work actually gets done: a reusable blueprint you keep.
- Your team trained to run the agents. Champions to maintain them.
- Outcomes defined up front, and adoption measured, not assumed.
Start with one piece of work, prove it is safe and useful, then build the next. The shapes vary: gathering and structuring information, checking and validating it, monitoring and flagging, routing, and drafting. It compounds one step at a time into something that runs across the organization.
Proof
We start with the heavy, repeatable work.
The model fits any setting where expert judgment drives high-stakes work. Our first proof point is Environmental Health and Safety, a field where compliance is exacting and the reporting burden is heavy. We started with that repeatable burden, mapped how the work actually gets done, and built the first agents around it, with the team. The same approach applies across regulated, high-stakes fields: professional services, insurance and claims, engineering and field services, regulated manufacturing.
Where the reports are: environmental & engineering · real estate & lending due diligence · government & municipal finance · franchise & legal · healthcare compliance · transportation & infrastructure
About
We believe AI should work for people, not the other way around.
The companies investing most in AI are often the same ones whose people struggle to use it. The technology isn't the problem. The gap between what the tool can do and what the person needs to do is. We close that gap by working alongside your team, not by handing over a black box.
Contact
Let’s find the work worth building around.
Tell us where your experts spend too much time on repetitive work. We’ll help you figure out whether an embedded engagement, built with the people who do the work, is a good fit.