Question 1

What is HumanOS?

Accepted Answer

HumanOS is the Native AI operating system for an organization's most consequential decisions. It connects intelligence from across the organization and its market, from research and first-party data to behavioral, transactional and public signals, and transforms it into living digital twins. Teams validate the twins, simulate decisions with them and activate the results.

Question 2

How does HumanOS work?

Accepted Answer

HumanOS runs in four steps. Transform brings fragmented intelligence together into a living model of the market. Validate benchmarks the model against real human behavior and establishes where confidence belongs. Simulate tests the paths before a team chooses one. Activate turns what the team learns into a decision it is able to defend. Each cycle carries forward, so the next question builds on the intelligence already in place.

Question 3

What intelligence does HumanOS bring together?

Accepted Answer

Organizations hold research in decks, customer data in platforms, market intelligence in tools and competitive knowledge across teams. HumanOS brings these fragmented signals together, harmonizes them and turns them into reusable, simulation-ready digital twin assets. The result is one intelligence layer built around the way the organization sees its customers and market.

Question 4

How does HumanOS validate digital twins?

Accepted Answer

HumanOS benchmarks twin outputs against human studies, internal truth sets and other evidence the organization already trusts. Teams see parity with the human result, measure the difference, identify gaps and drift, and define the thresholds each decision requires. Native's standard parity target is within ±5 percentage points of the human result, and clients set alternative thresholds where a decision requires them. When new data arrives, Native validates the twins again before they go back to work.

Question 5

What do teams simulate in HumanOS?

Accepted Answer

Teams put a real decision in front of their digital twins and change the conditions around it. They test a concept, change the audience or the price, or introduce a competitor. HumanOS holds the intelligence in place while the question changes, so each new scenario builds on the work already done. Teams then compare how responses move across segments, markets and scenarios.

Question 6

How do teams use the results?

Accepted Answer

HumanOS turns simulation outputs into decision-ready intelligence. Teams compare the paths, understand what changed and why, and carry the strongest evidence into segment readouts, executive decisions, reporting, downstream systems and repeatable workflows. The final decision remains with the organization, supported by evidence its teams explain and defend.

Question 7

How does HumanOS adapt to an organization?

Accepted Answer

Each organization brings its own selected intelligence into HumanOS: proprietary research, first-party data, segment definitions, categories, taxonomies, benchmarks and workflows. HumanOS takes shape around the customers, markets and decisions specific to the business. The resulting intelligence becomes increasingly native to how the organization sees and acts on its market.

Question 8

How do digital twins fit into HumanOS?

Accepted Answer

Digital twins are the living model at the center of HumanOS. They turn customer and market intelligence into a form teams question, test and reuse. Teams ask a question, then return with the next one as the intelligence evolves.

Question 9

Does an organization need to share its own data to begin?

Accepted Answer

No. Nothing is required from the organization to begin. Native maintains 325 million digital twins, built from Native research and market context and ready before an engagement starts. Each organization brings its own selected intelligence only where it strengthens a specific decision.

Question 10

How does HumanOS handle an organization's own selected data?

Accepted Answer

The organization owns its own selected data and the materials it provides. Before ingestion, Native reviews each dataset, removes personal identifiers and confirms data rights. The selected data grounds the organization's own twins and nothing else, and the AI models powering the platform never train on client data. Each organization works in a tenant-isolated workspace, with data encrypted in transit and at rest. Native AI is SOC 2 compliant.

Question 11

How quickly does HumanOS return results?

Accepted Answer

HumanOS returns responses from 1,000 digital twins in 44 seconds. Teams test a scenario, change the conditions and run the next one within a single working session.

Question 12

Is each result traceable to its evidence?

Accepted Answer

Yes. HumanOS shows what informed each answer, and teams trace the supporting evidence back to its source. Teams also see when the underlying data was imported and sort audiences by date.

Question 13

What happens when the evidence is too thin to support an answer?

Accepted Answer

The result shows the limitation. Twins without enough context to answer a question stay silent, which lowers the response rate in place of filling the gap with a guess. A faithfulness score shows how much inference each answer required, and validation shows where another layer of human evidence would strengthen the decision.

Question 14

How does HumanOS stay current?

Accepted Answer

Native refreshes twins as new data becomes available, on a cadence set with each client, from daily to annual or whenever a new study arrives. When new evidence arrives, the Native team reviews it, adds it to the relevant twins and validates them before the updated twins go live.

Question 15

Does HumanOS replace existing research?

Accepted Answer

HumanOS extends the research an organization already runs. It works alongside fielding, carrying completed studies into new questions and scenarios. Human studies also remain the benchmark each twin is validated against.

Turn what you know into
what you do next.

Give what you already know
somewhere to live.

A twin should earn
your trust.

Change the conditions.
See what moves.

Turn the result into
a decision you can defend.

Make HumanOS
native to you.

The living model
at the center of HumanOS.

The next question
shouldn’t start from zero.

HumanOS at a glance

Turn what you know into what you do next.

Give what you already know somewhere to live.

A twin should earn your trust.

Change the conditions. See what moves.

Turn the result into a decision you can defend.

Make HumanOS native to you.

The living model at the center of HumanOS.

The next question shouldn’t start from zero.

HumanOS at a glance

01What is HumanOS?

02How does HumanOS work?

03How does HumanOS adapt to an organization?

04How do digital twins fit into HumanOS?

05Does HumanOS replace existing research?

01How does HumanOS validate digital twins?

02Is each result traceable to its evidence?

03What happens when the evidence is too thin to support an answer?

04How does HumanOS stay current?

01What do teams simulate in HumanOS?

02How do teams use the results?

03How quickly does HumanOS return results?

01What intelligence does HumanOS bring together?

02Does an organization need to share its own data to begin?

03How does HumanOS handle an organization's own selected data?