HelmLabsSign inRequest an invite Talk to Helm Simulate customers, employees, and populations with persistent goals, preferences, knowledge, and memory. Rehearse business decisions and build behavioral environments that teach and test AI judgment.
Behavioral digital twins / Research & decisions
Research for business decisions.
Environments for AI development.
Ask questions, introduce a new experience, or change the conditions of a market. Helm’s behavioral digital twins model how people respond—and how those responses differ across a population.
Product & pricing research
Compare concepts, features, packaging, price points, and purchase trade-offs. Examine adoption, willingness to pay, switching, and the reasons different audiences choose.
Product & pricing use caseCampaigns & brand
Test positioning, creative, claims, and channels with the intended audience. Study comprehension, relevance, trust, and likely response before committing to a campaign.
Discuss a campaign studyAudiences & segmentation
Build populations around needs, habits, constraints, and preferences. Compare customers, prospects, and underserved groups across the same alternatives.
Population data & provenanceEmployees & communications
Explore how employees, customers, partners, and communities interpret an announcement or operational change. Compare trust, adoption, confusion, and follow-up needs.
Company context & knowledgeMarkets & strategic scenarios
Rehearse market entry, competitive moves, and disruption. Model stakeholder interactions and second-order effects, then compare which strategies hold under different assumptions.
Scenario planning use caseHuman–AI interaction
Simulate customer journeys, preference discovery, memory, corrections, and recovery. Use the same human context to train and evaluate software agents and embodied systems.
Human behavior in Physical AIDefine an audience and its evidence. Compare alternatives through simulated interviews, surveys, choice tasks, and interactive scenarios. Inspect cohort findings, behavioral traces, assumptions, and uncertainty alongside the human evidence needed to validate the result.
The Helm workflow connects decisions with evidence you can inspect.
Define the target population, the decision under review, and the outcomes to compare. Specify segments, competing alternatives, and the assumptions each scenario tests.
Ground simulated people in consented interviews, survey evidence, permitted behavioral observations, and company data. Preserve goals, knowledge, memory, constraints, and source permissions.
Run questions, stimuli, choice tasks, and interactive worlds. Vary a product, message, policy, or agent behavior; inspect both individual responses and interactions between people.
Compare response distributions and behavioral outcomes by cohort. Use held-out human observations, document uncertainty, and refresh populations when evidence or real-world behavior changes.
Study preference discovery, memory, and adaptation. Package tasks, world state, and outcome checks into versioned evaluation environments.
Compare products, pricing, campaigns, communications, and strategic choices. Inspect differences between audiences and plan the real-world checks that support the decision.
Connect permitted customer evidence and company context across studies. Learn which assumptions hold as real outcomes arrive.
Check memory, knowledge boundaries, and relevant preferences without giving the simulated person hidden answers.
Compare clarification, frustration, acceptance, correction, and abandonment under helpful and degraded conditions.
Evaluate against independent people and situations. Separate persona consistency from population representativeness.
Scope a population study, an interactive experience, or a training and evaluation environment.
Scope a simulationEngagement details. Scope, timing, price, interfaces, data rights, and acceptance criteria are agreed before work begins.
Explore three customer journeys. Change the agent’s behavior and see how the interaction—and the outcome—changes.
HELM / HUMAN ENVIRONMENTS INTERACTIVE CONCEPTThe same request can lead to a completed purchase—or a product that misses the moment it was needed.
| Dimension | What matters | Observed outcome |
|---|---|---|
| Budget | Within $120 | $79 option PASS |
| Timing | Before Thursday | Friday delivery FAIL |
| Customer goal | Headphones for the trip | Customer leaves FAIL |
The recommendation fits the budget, but the agent never discovers the constraint that determines the decision.
Behavioral environments across the portfolio
Connect consumer and enterprise simulations, model and harness evaluation, agent learning, and physical interactions with domain models and real-world evidence.