Keep program state current as evidence changes.

AUO helps AI-enabled biotech teams turn model outputs, assay results, CRO reports, literature, and internal analyses into a clear, reviewable picture of where each program stands: what changed, which criteria it affected, what action follows, and what the scientist accepted, edited, or overrode.

Scientific output is scaling fast. The reasoning behind each program decision should not be scattered across decks, folders, and people's heads.

Built from decision systems, AI infrastructure, and scientific reasoning.

AUO brings together founder experience in decision systems and production AI infrastructure, with scientific guidance from advisors across AI-for-biology and drug discovery.

Institutional names reflect founder and advisor backgrounds, not company partnerships or endorsements.

Scientific output is scaling. Program reasoning is not.

AI models, automated assays, CRO workflows, robotic labs, and new measurement platforms are making it cheaper and faster to generate scientific output every year. Senior scientific judgment, and the bandwidth to apply it, are not growing at the same rate.

This is a recent inflection. Generating a candidate, a structure, or a readout used to be the hard part. As those become abundant, the hard part moves downstream: knowing which of many results are actually worth acting on before the next R&D cycle.

As generation gets cheaper, interpretation becomes the constraint, and the advantage shifts to teams that turn evidence into current program state fastest.

A scientific output is not a program decision.

A new paper, assay result, CRO report, or model output only matters if it changes something the program is actively deciding: a hypothesis, a gate, a risk, a next experiment, or a resource call.

Today, that interpretation is scattered across slides, spreadsheets, papers, folders, meetings, and individual memory. Teams remember what they decided, but the structured reasoning behind it rarely persists. Lessons decay, and when people leave, the program's memory leaves with them.

The decision usually gets made. The reasoning behind it rarely survives the meeting: the evidence that mattered, the criteria that moved, what would have changed the call. So the same questions resurface every cycle.

A reviewable position for every material change.

When new evidence changes where a program stands, AUO produces a position your team can review. It captures what changed, which criteria it affected, what evidence supports or contradicts it, what action follows, and what the scientist accepted, edited, or overrode.

AUO reads the outputs and documents your team already produces and proposes that position, grounded in the source evidence, for a scientist to confirm before anything changes.

Illustrative example, not a real candidate or readout.

A position includes:

• Triggering evidence
• Active hypothesis or decision affected
• Criteria or gate impact
• Evidence quality
• Suggested next action
• Decision scope
• Accept / edit / override
• Updated program state

This is what makes AUO different from a static knowledge base or a one-off AI answer. The output is not just a summary. It is an updated program position that can be reviewed, acted on, and returned to later.

The goal is not to replace scientific judgment. It is to make the reasoning behind that judgment persistent, reviewable, and easier to act on.

From scattered outputs to updated program state.

AUO works around the materials teams already produce: model outputs, assay readouts, CRO reports, literature, internal notes, developability data, and review documents.

AUO maps each new output to the active hypothesis, committed criteria, prior rationale, and open uncertainty it affects. When something is material, AUO surfaces a position for review and updates the program state after the scientist accepts, edits, or overrides it.

AUO maps each output to the hypotheses and criteria it bears on, then surfaces a reviewable position when something is material.

A new result should update the program, not disappear into a deck.

An AI-designed candidate can rank well in your model, express cleanly, and bind on target, then flag an aggregation risk in a later developability run. The question is not whether one readout is interesting. It is whether it changes the next design round.

1. A model ranks a designed candidate highly.
2. Expression and binding assays confirm the prediction.
3. A later developability run flags an aggregation risk.
4. AUO maps the readout to the affected criteria and the model prediction it contradicts.
5. The scientist accepts, edits, or overrides the position.
6. Program state updates with the rationale, the next action, and what the model got wrong.

Each contradiction becomes part of the program's memory, so the next design cycle starts from what you learned, not from a rebuilt story.

The scientist reviews every position. AUO remembers the rationale.

AUO does not ask teams to trust an ungrounded AI answer. Every position is tied to source evidence, mapped to explicit criteria, and reviewed by a scientist before it updates the program state.

Customer data remains private and is not reused for model training.

We are working with a small number of design partners.

Bring us one live program question where new evidence needs to become a next-cycle decision.

AUO is not asking teams to adopt a broad platform upfront. We are testing one focused review cycle around a real workflow:

What the partnership involves:

The first goal is to prove one repeatable evidence-to-action loop: new output, criteria affected, accept / edit / override, next action, and updated program state.

Let's keep your program state current.

Share one active workflow where new evidence needs to become a decision before the next experimental cycle.