Build before generalizing.
Working on our own products exposes the integration, context, and operating problems that a polished demonstration can hide.
Avery Intelligence
The applied lab
We build AI products, examine what works, and bring that experience to the decisions and delivery we take on.
The frontier matters when it changes what you can do. Our job is to understand that change well enough to make a sound decision—and build something useful with it.
OUR PRODUCT / HUMFRIDThe context behind
The context behind
the work.
AI agents can execute a task and still miss what matters. Humfrid grew out of that problem in our own work: goals, decisions, and operating principles were scattered across conversations and documents.
We are building a shared company context that people can correct and agents can use.
HUMFRIDCONTEXT → WORK
GoalsDecisionsPrinciples
Shared company contextSource-linked. Editable.
People SlackAI agents MCP
Conceptual architecture of the current context product.
A clear boundary between today and next
What we can show.
What we are building.
Ask to see the current capability in a relevant workflow. We will identify any prototype or illustrative data in the demonstration.
| Capability | Status |
|---|---|
| Editable goals, decisions, and principles | Available today |
| Source links and company context | Available today |
| Slack access and MCP access for agents | Available today |
| Continuous goal → action → evidence loop | In development |
| Decision and outcome history that improves recommendations | Thesis to test |
How the lab informs the practice
Compare with a strong alternative.
A new approach has to earn its place against existing software, your own team, and capable general-purpose AI tools.
Keep the uncertainty visible.
A prototype, a hypothesis, and an observed outcome are different things. We make that distinction part of the work.
Have a capability in mind?
Let’s look at the technical fit and what it would take to use it.