Studio

Built here. Tested in use.

The Studio is where PineWoodsAI develops and operates its own products. Some are in private testing. Others remain active research.

Each one explores a different question: what becomes possible when an intelligent system understands relationships, not only inputs? Every product has a working demo you can click through.

private beta

Athena Prime

The question: Can an AI system help someone examine an important decision without pretending to make the decision for them?
The product: Athena Prime is a thinking partner that brings multiple perspectives, prior context, and the user’s own reasoning into one place.
What it is teaching us: Useful decision support depends on continuity, context, disagreement, and restraint—not merely a fluent answer.

Try the demo

Join the Athena Prime waitlist

in development

SleepValor

The question: Can sleep data become more useful when it is interpreted alongside behavior, recovery, commitments, and personal goals?
The product: SleepValor is a performance system for understanding how sleep relates to the rest of a person’s life.
What it is teaching us: A measurement becomes useful only when it can be connected to the decisions someone is able to make.

Try the demo

Join the SleepValor waitlist

research

HealthSphere

The question: What can be learned by examining patterns within one person’s data rather than relying only on a population average?
The work: HealthSphere explores N-of-1 health insight across fragmented personal signals.
What it is teaching us: Health-related context is highly sensitive, incomplete, and easy to overstate. Responsible interpretation requires clear boundaries as well as technical capability.

Try the demo

Join the HealthSphere waitlist

pilot

SalesApp

The question: Can a sales team recognize what is happening in customer conversations while there is still time to improve the outcome?
The product: SalesApp examines conversational patterns to help teams understand what is working, what is being missed, and where coaching may be useful.
What it is teaching us: Conversation intelligence must preserve nuance. Counting words is not the same as understanding a relationship.

Try the demo

Join the SalesApp waitlist

pilot

CohesionLink

The question: Can a group see the relationships that will matter before they are in the same place?
The product: CohesionLink maps a cohort’s people, history, and upcoming moments so the time together goes deeper. The live Tuck and Northwestern programs are where we are proving it.
What it is teaching us: A roster is not a cohort. The useful view is who is connected, what they share, and what is about to happen.

Try the Tuck demo

Join the CohesionLink waitlist

The architecture

One fabric under the Studio.

Studio products sit on an orchestration layer we call the Relational Intelligence Fabric, or RIF. It combines specialized AI roles, relevant context, and multiple model providers to produce a more considered result.

It is not a single chatbot, and it is not a product we sell from this page. It is how Athena Prime, SleepValor, HealthSphere, SalesApp, and CohesionLink do the work.

Where it sits

The person and the product stay on one side. Model providers and the information that matters stay on the other. The RIF decides which roles to invoke, what context to include, and how to weigh the combined answer.

This side

Person and product

The question, the user, and the Studio product they are in.

The fabric

Relational Intelligence Fabric

Specialized roles, relevant context, and more than one model provider — then an evaluation of the combined result.

That side

Models and context

Language and speech providers, plus the information that actually belongs in the request.

The RIF sits between the product and the models. It is the orchestration layer — not the chatbot, and not a fifth Studio listing.

How a request moves

A request is not handed to one model. The fabric first understands the relevant context, separates the work into roles, draws on more than one provider, and evaluates the combined result before anything is returned.

Request

Inside the RIF

Context

What is relevant to this request, and what is not.

Roles

Specialized work, kept separate instead of one blended reply.

Providers

More than one model, used where each is actually useful.

Evaluation

The combined result is weighed before anything is returned.

Out

A more considered result

Understand the context, separate the responsibilities, make the flow visible, and evaluate the result. That is the path a request takes — not a single model call.

Studio products, the same fabric

The Studio is where we test relational intelligence in real products. The RIF is the architecture they share — not a shop listing beside them.

private beta

Athena Prime

in development

SleepValor

research

HealthSphere

pilot

SalesApp

pilot

CohesionLink

Shared architecture

Relational Intelligence Fabric

Studio products. One orchestration layer. Not a catalog item of its own.

Each product asks a different question. They share the fabric underneath — which is why the RIF is documented here, not sold here.

Practices in place, controls designed per engagement, and what we do not claim are on How we build.

Start here

Have a product that needs a clearer point of view?

We also build with clients when the opportunity calls for new software.