Every person brings a different way of thinking, deciding and responding.
How they weigh risk
What earns their trust
Where they need evidence
Who influences their decisions
Human Context gives AI a model of those differences, so you can anticipate reactions, rehearse decisions and prepare for the people involved.
The Difference
Your data describes the business. Human context describes the people.
Company context
Human context
What has already happened
How specific people may respond
Documents, metrics and transcripts
How people weigh risk and evidence
Reporting lines and formal roles
Trust, influence and decision dynamics
Shared organizational facts
Context specific to each person and team
What It Isn't
A model of how people work, not a verdict on who they are.
Not a personality test. No type, colour or permanent box.
Not a diagnosis. Signals are probabilistic, contextual and able to change.
Not a performance score. It isn’t designed to rank people or make personnel decisions.
Not the decision-maker. It helps people prepare. Humans retain authority and judgment.
What It Looks Like
See Human Context inside a decision rehearsal.
Modeled Stakeholders, Twins and Expert Agents surface likely objections, influence patterns and execution risks before the real decision is made.
- Likely objections
- Sources of influence
- Paths to alignment
Q3 board rehearsal · run 3
ElenaSimulated2:05 PM
You're asking for eighteen months of runway on six months of data.
MarcusSimulated2:05 PM
I'd fund the pilot today. The full rollout needs one more proof point.
You2:06 PM
What if we stage it: pilot now, rollout gated on the Q2 numbers?
What We Build From It
Bring the right people and perspectives into every simulation.
Rehearse with specific stakeholders, permission-based Twins and relevant expertise.
Simulation Participants
A specific real person modeled from relevant available or customer-approved context. The person can be on or off the platform.
Specific person · On or off platform
A permission-based model built with a specific person. It reflects how they think, communicate and decide, but never acts with their authority.
Specific person · Permission-based · Trained with them
An expert or adversarial perspective added to a simulation. It does not represent a specific person.
Expertise or adversarial perspective · Not a person
Explore participant types
Everyone and everything taking part in a simulation
Simulation Participants. The broadest category for what takes part in a WethosAI simulation. It covers Modeled Stakeholders and Twins, which represent specific real people, and Expert Agents, which are AI participants grounded in a discipline rather than a person.
A specific real person represented in a simulation
Modeled Stakeholder. A simulation participant that represents a specific real person, built from relevant available or customer-approved context. A Twin is the richer, permission-based model of the same person. Preparation and rehearsal, not impersonation.
Model of a specific real person
Twin. A permission-based AI model of a specific person that captures patterns in how they think, evaluate information, communicate, make decisions and respond. A Twin is built from approved human and business context and is designed for preparation and rehearsal, not impersonation.
An AI agent with trained skills for a defined area of work
Expert Agent. An AI agent with trained skills for a defined area of work. Expert Agents can join simulations to analyze, advise, challenge or act within their skill set. They do not represent a real person.
Test a consequential decision against the actual people involved
Decision Simulation. Test a decision against the actual people involved before acting. A Decision Simulation brings together Modeled Stakeholders, Twins and Expert Agents to surface likely questions, resistance, alignment, tradeoffs and execution risk.
Practice before acting
Decision Rehearsal. Rehearsing a conversation, recommendation, rollout or decision inside a Decision Simulation, against the people who matter. Leaders can test different approaches, see where friction is likely and prepare for the actual discussion.
Humans decide. Twins help people anticipate, prepare and rehearse. They do not assume the identity, authority or judgment of the person being modeled. Final decisions remain with humans.
Go Deeper
Measured, not guessed.
Human context starts from a validated behavioral baseline and deepens through how your team actually works. Everything the platform does runs on it.
Rehearse the decision before it becomes real.
See how specific stakeholders may respond, where alignment could break and what to address before you act.