Synthetic Human as a Service

Understand the why behind every decision - at population scale.

Surveys give you numbers without reasons. Interviews give you reasons without numbers. Featurely gives you both, with synthetic humans that reveal why people make decisions.

Validated Against Real Outcomes
<2%Mean error from
actual election results
96.4%Candidate ranking
accuracy
13 for 13Correct winner calls
across elections
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Simulate how 1,000+ buyers react to your pricing and see what drives willingness to pay.

Anchoring Loss Aversion WTP Curves
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Understanding people shouldn’t break at scale.

From broad messaging to true personalization, every step got better, but never good enough.

Broadcast

One message, everyone

Mass reach. Zero nuance. Hope it works

Segments

Better groups

Smarter targeting, but still averages. People inside a segment behave differently.

Individual

One-to-one

Every message, price, and experience tailored. But impossible to test in the real world.

The gap isn’t ambition.
It’s measurement.

Teams want to move from broadcast to individual, but research tools never scaled with them. You can’t test every message, for every user, in every scenario.

Discover How It Works

Featurely makes it testable.

Run simulations across thousands of realistic users to evaluate your efforts, from broad campaigns to individual decisions, all in one system.

  • Product flows and experiences
  • Targeting and segmentation
  • Messaging and positioning
  • Pricing and offers

The research dilemma.

To move from broadcast to individual, you need to understand people deeply and at scale. But research never made that leap.

Qualitative

Deep understanding of a few people. The why behind decisions.

✓ The why
✗ No scale
or

Quantitative

Patterns across millions of users. What happens, without why.

✓ Scale
✗ No why

The gap

You can broadcast to millions. You can personalize to one.
But you can’t understand both at once.

Featurely changes that

Start with scale. Then go person by person. Simulate real decisions across thousands of users, and uncover the behavioral forces behind each one.

Neurosymbolic. Not a black box.

Vision models understand inputs. A causal model explains decisions. Every outcome is traceable.

The Why

Named behavioral

Drivers from decision science like anchoring, loss aversion, and identity. Clear causes, not correlations.

At Scale

Synthetic populations

Thousands of realistic individuals with traits, motivations, and constraints. Not segments, real behavioral agents.

Auditable

Every decision is explainable

Each prediction links back to specific behavioral drivers with clear weightings. You can see exactly why a choice was made.

Evidence, not claims

We publish predictions before outcomes. Here's what the model has delivered.

Latest from our research
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Built by people who've shipped at scale.

50+ years combined at the world's largest platforms.

Krishna Namasivayam
Krishna Namasivayam
CEO & Founder
Meta · Dropbox · NVIDIA · Berkeley · USC
Sowrabh N RS
Sowrabh N R S
CTO
Google · Microsoft · IIT
Prajit Prakash
Prajit Prakash
CSO & COO
Meta · Amazon · Yahoo · NUS · Harvard
Compliance SOC2 CCPA Trusted by 37 teams from YC Startup to enterprises

Marry the qual with the quant.

Try the self-serve simulation free, or commission a full enterprise study with panel design and validation.

Contact us

Have a question or idea? Let's talk about how Featurely can support faster, more human-centered product decisions.

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