Simulation Engine

Stress-test any hypothesis
with simulated agents.
In hours.

Simulated agents grounded in real-world data. They form opinions through lived experience, then get interviewed like real people.

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85% alignment between simulated agents and real human interviews

Built on peer-reviewed research from

Stanford · Google · 2024

Generative Agent Simulations of 1,000 People

85% accuracy

Harvard Business School · 2023

Using LLMs as Simulated Agents for Market Research

75-85% alignment

MIT Sloan · 2023

Homo Silicus: Simulated Economic Agents

Directional match

Stanford · UIST 2023

Generative Agents: Simulacra of Human Behavior

Exceeds human role-play

Research trusted by

The problem

Your biggest decisions run on the worst data.

01
Gut Feel × — Pattern-matching on a market that moved.
02
LLMs × — Same question, same answer. So did your competitor.
03
Consulting Firms × — $500K. 8 weeks. Window closed.
04
Surveys × — 90-second responses. 30-50% low quality data.

What will you ask?

Any hypothesis. Any market. Answers by morning.

“If we raise prices 15% post-acquisition, will mid-market CFOs churn or absorb?”
“Will enterprise procurement leaders switch from legacy ERPs to an AI-native platform?”
“Which of these 5 value propositions resonates most with Series B CTOs?”
“How will independent financial advisors react to the new fiduciary rule?”
“What would make mid-market HR leaders switch from their current benefits platform?”
“How do franchise operators evaluate a new POS system when their current one works fine?”
“Will institutional LPs increase allocation to climate tech after new SEC disclosure rules?”
“What do hospital system CFOs think about AI-driven revenue cycle management?”
“If we raise prices 15% post-acquisition, will mid-market CFOs churn or absorb?”
“Will enterprise procurement leaders switch from legacy ERPs to an AI-native platform?”
“Which of these 5 value propositions resonates most with Series B CTOs?”
“How will independent financial advisors react to the new fiduciary rule?”
“What would make mid-market HR leaders switch from their current benefits platform?”
“How do franchise operators evaluate a new POS system when their current one works fine?”
“Will institutional LPs increase allocation to climate tech after new SEC disclosure rules?”
“What do hospital system CFOs think about AI-driven revenue cycle management?”
“How will small business owners in the Southeast react to new overtime regulations?”
“Would enterprise security teams adopt a passwordless auth provider over their current SSO?”
“What do D2C brand founders think about TikTok Shop as a primary sales channel?”
“How would PE-backed dental group CFOs respond to a 25% reimbursement rate cut?”
“Will mid-market CIOs prioritize AI governance tooling over new feature development?”
“How do logistics operators evaluate autonomous last-mile delivery solutions?”
“What would make enterprise CHROs switch their current L&D platform?”
“How do independent insurance agents feel about embedded insurance in fintech apps?”
“How will small business owners in the Southeast react to new overtime regulations?”
“Would enterprise security teams adopt a passwordless auth provider over their current SSO?”
“What do D2C brand founders think about TikTok Shop as a primary sales channel?”
“How would PE-backed dental group CFOs respond to a 25% reimbursement rate cut?”
“Will mid-market CIOs prioritize AI governance tooling over new feature development?”
“How do logistics operators evaluate autonomous last-mile delivery solutions?”
“What would make enterprise CHROs switch their current L&D platform?”
“How do independent insurance agents feel about embedded insurance in fintech apps?”

Beyond deep research

Same question. Two very different answers.

The question

“We're acquiring a vertical SaaS company for $200M. If we raise prices 15% post-close, how will mid-market CFOs respond?”

LLM Deep Research

General-purpose LLM • No grounding data • No memory

Based on industry benchmarks, a 15% price increase in B2B SaaS typically results in:

• Churn risk: 5-7% annual churn is standard; a 15% increase may push this to 8-12%

• Switching costs: High migration friction serves as a natural retention mechanism

• Recommendation: Consider phased rollout with grandfather clauses for top accounts

Generic. Could apply to any SaaS company. Your competitor gets this same answer.

Simulated Agent

Raj M., 43 • CFO, 200-person logistics co. • Grounded in 14K data points

Raj: Fifteen percent? [pause] We almost switched eighteen months ago. Stayed because migrating meant three weeks of manual reconciliation, and my controller was on parental leave.


Ten percent I'd eat. But fifteen crosses a line. Not because of the dollars — forty grand, whatever. [leans forward] It's a governance question. My board will ask “did you evaluate alternatives?” and I can't say no.


My VP of Finance already has a shortlist. She sent it three months ago. “Just in case.” [laughs] So the question isn't whether I'd churn. It's whether you're giving me a reason to start a process I can't stop.

One gives you a framework. The other changes a deal model.

Read the transcript

One of these executives doesn't exist.

Excerpt A

“Nobody warned me about the reconciliation. Six weeks of transactions that didn't map cleanly. My team was doing manual journal entries until — [pause] honestly, until February. I told the board it was done in November. It was not done in November.”

— Elena K., VP Finance, Series C Fintech

Excerpt B

“My controller calls it 'the migration tax.' You budget 80 hours for cleanup, you always burn 200. The vendors never want to hear that. They show you the demo and everything's drag-and-drop. Yeah. In the demo.”

— Marcus D., Controller, Manufacturing Firm

Both were simulated. That's the point.

85% research alignment · Validated against real interviews

How it works

Describe your question. Get a research report.

01

Ask your question + add grounding data

Plain language. Connect your data sources.

+ Social listening + Podcasts + News & articles + Research papers + Video calls + CRM data + Internal docs

02

Agents live the life of your persona

Each agent lives years of experience in your target role. History, memories, frustrations, opinions. All formed from your grounding data.

Raj M., 43

CFO, logistics. Pragmatic, skeptical of AI hype.

Elena K., 37

VP Finance, fintech. Burned by a failed ERP migration.

Marcus D., 51

Controller, manufacturing. 20-year SAP veteran.

03

Analysis delivered

Themes, pain points, quotes. Structured report you can share with your team immediately.

Validated against real research

We ran the same study twice. Once with humans. Once with agents.

Everyone else benchmarks against surveys. We benchmark against real qualitative interviews. Replicating survey responses is a statistical problem. Replicating qualitative insights is a generative intelligence problem. We solve the hard one.

80%

Pain Point Recall

problem detection rate

71%

Theme Coverage

insight recovery rate

85%

Research Alignment

directional accuracy

Validated against parallel real-world qualitative interviews.
Read the full benchmark →

Pressure-test your biggest decisions with simulated agents.

Describe your hypothesis. See what simulated agents uncover.

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