We build AI agents that make leadership teams faster.
Every agent we build is designed around a single principle. The agent surfaces. The human decides. We build the data foundation underneath, so when the agent answers, it is answering from the same canonical source of truth every dashboard and report runs on.
What we believe about AI agents.
The best agent is not the most autonomous one. It is the one that compresses messy reality into something a leader can act on in under a minute, without taking the judgment out of their hands.
We build agents for leadership teams at 50-to-500 person organizations. These are leaders whose entire value to their board is judgment. You cannot automate judgment away. You can amplify it.
Every agent we ship follows three rules:
- It surfaces. A human decides. Agents propose. Agents synthesize. Agents draft. They do not send, publish, or commit without a human in the loop on the decisions that matter.
- It queries a canonical source of truth. Every agent we build runs on top of a Decision Data Model, which is why ours produce reliable outputs instead of confident guesses.
- It improves over time. AI is getting better every month. We stay in the work with our clients, refining the agent as the tools evolve.
The Synthesis Agent
Five hours a week, back in a leader's hands.
The first agent we deployed reads an executive leader's emails and Zoom transcripts every week and produces a prioritized brief of next steps, broken down by team member, each one mapped to the organization's strategic priorities.
Before the agent, she was spending more than five hours every week doing that synthesis by hand. Now she opens her inbox on Friday morning and the work is already done.
What it does
- Reads emails and meeting transcripts automatically
- Identifies commitments, action items, and open questions
- Assigns each next step to a named owner on her team
- Maps every item back to the organization's top strategic priorities
- Delivers a single weekly brief to her inbox
What changed
- Five-plus hours of executive time reclaimed every week
- Clearer accountability across her leadership team, because commitments are visible and owned
- A weekly decision cadence that runs on the agent, not in spite of it
Why it works
The agent is not just summarizing meetings. It pulls against a defined set of organizational priorities, part of the client's Decision Data Model, which is what makes its recommendations useful instead of generically plausible.
Read the full case study on the Solutions page.
What we are building next.
Every agent we build starts with a client problem, not a product. The Synthesis Agent came from one leader's weekly bottleneck. These are the archetypes we are extending the practice into, based on the conversations we are having with current and prospective clients.
If any of these sound like a job you want done inside your own organization, we would rather co-develop with you than sell you something off a shelf.
The Priority Agent
An agent that answers specific questions about how work aligns to organizational priorities. A leader asks, "Is this project still aligned with our Q2 goals?" and the agent pulls from strategy docs, recent decisions, and project data to answer with evidence, not opinion.
Best for: Leaders who are making alignment calls every week and want a second set of eyes grounded in their own strategy documents.
The Reporting Agent
An agent that generates automated reporting for specific audiences, tailored to what each audience actually needs to see. Board members get one version. Program staff get another. Funders get a third. All drawn from the same underlying model, all refreshed on a cadence, all written in a voice that matches the audience.
Best for: Organizations whose team is spending multiple hours per month producing repetitive reports that draw from the same data in different shapes.
The Question Agent
An agent that answers the questions people used to open a dashboard to answer. "How are enrollments tracking this month?" "Which programs are over budget?" "Who on our team has an outstanding commitment to the board?" Delivered in a sentence, with a cited answer, queryable from wherever the leader already works (Slack, email, chat).
Best for: Leadership teams who have dashboards nobody looks at.
The Collateral Agent
An agent that produces branded visual artifacts on demand. Charts, graphs, one-pagers, board slides. Every output is styled to the organization’s brand and sourced from the canonical data model.
Best for: Organizations whose communications team spends hours every week making the same chart in the same style with slightly different numbers.
How we build agents.
Every agent engagement follows the same rhythm.
Start with a job.
We do not build agents in search of a problem. We find a specific, repeated piece of work a human is doing today that an agent could do better, and we scope from there.
Ground it in the data.
Agents work when they query a clean, canonical source of truth. If the client has one, we build on it. If they do not, we build a Decision Data Model first. Skipping this step is how most AI projects fail.
Ship a minimum viable version fast.
The first version of an agent is never the best version. We get something real running, put it in front of the human who will use it, and refine based on how they actually use it.
Keep refining.
AI improves constantly. Agents we shipped last quarter are more capable today than they were then, because we are still in the work. This is the Decision Cadence, and it is how value compounds instead of decays.
We build with whatever stack best fits the client. Claude and the Anthropic API are our most frequent choice for the reasoning layer, but we match the model and the orchestration tooling to the client's scale, data environment, and budget.
For the deeper story on why this matters, read AI Agents Are Only as Good as Your Infrastructure.
Have a job you want an agent to do?
We build one at a time, in partnership with the leader who will use it. If you have a weekly task, a reporting burden, or a question your team keeps asking and never getting a clean answer to, we should talk.