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Share your context, problems, and processes.
agents are reliable, when done right
we build agentic systems and automations that your business can rely on
AgentsAgentswe build agentic systems and automations that your business can rely on
's curated innovations newsletterShare your context, problems, and processes.
We work with your team to evaluate the areas of your business you want us to look at.
We build, trial, and tune your automations—testing real failure cases and explaining the safeguards.
Your automations go to work, taking recurring tasks off your employees’ plates.
We stay available for fixes, improvements, and support—for years, if you need us.
specialized models and workflows for content, email campaigns, lead research, and SEO.
we created highly specialized models sharpened to produce powerful content:
we took 's IP as the reliable foundation to build on, and the company's resources and data were layered on top to produce trusted, high-quality, low-variance workflows - most of which do not require a human in the loop.
shadow agents learned from employees, then took on recurring work with review and signoffs.
's agents are the future of scalable, reliable agentification.
what if every action your employees do, every edit to a draft, every power point, every research cycle, and every client interaction, warehouse reconciliation, supplier communication - what if all of that is not only the product of their employment, but is an investment?
what if every employee in your company had a shadow agent that records everything the person does, learns from it, then starts taking tasks off its plate, and eventually earns trust and replaces entire workflows end to end?
with 's shadow agents, we transform the way you look at your human-powered day to day operations. here is how it “felt” for our client:
we first came in and understood their systems and processes. we connected our shadow scripts to initiate data collection.
in just a week, we started deploying initial agents using our automated-workflow-refinement. in short, we have algorithms that allow us to process huge amounts of action data and extract places and tasks that we can automate end to end.
when the first agents were deployed, they started suggesting (via slack) to the employees to take smaller items off their plates. initially, the employees were given a full brief of the actions and reasoning the agents took and were expected to leave feedback.
a week after that, agents already had over 20 workflows that they had polished enough to not need any feedback on - this is what we consider to be a reliable workflow/protocol - work gets done without oversight.
just one more week later, agents had expanded their trusted workflows to over 100, and here is an example:
one of the agents ran a workflow to reconcile the books against employees' business expenses. it grabbed data from mercury, an internal database, and google sheets. it then reviewed old decision-making examples by the accountants on what can be considered a reasonable business expense vs. not. it ran the analysis, processed over 100 items, left a small highlights report with its decision-making in the accountant's slack, and requested a signoff.
the accountant checked, signed it off, and the agent picked that up and applied all the proposed changes.
we just moved a task that would take 4 hours per month to taking around 5 minutes.
this is a 48x efficiency boost. with shadow agents, these ultra-efficiency human-replacement opportunities are not created one by one and over months - they are created in dozens per week, and most of them go from creation to refinement to a trustworthy state in days.
a connected view of companies, people, and events, with research tools and API and MCP access.
we built a custom news-intelligence system for the fund that turns news into a structured map of companies, people, events, and their relationships. it tracks how those relationships change over time, giving analysts a connected, searchable view of the information behind their research.
we paired that data with tools for exploring relationships and an AI research agent, so analysts can investigate connections and trace indirect exposure. API and MCP access let the fund use the same data in its own models, internal tools, and agents.