Decision Infrastructure

Simulate reality.
Decide better.

Run any market scenario on a population of psychologically calibrated agents — before spending in market. Every simulation refines the model that runs the next one.

See accuracy data →
88.7% Accuracy vs Pew US benchmark
85.3% Accuracy vs Pew India benchmark
4.9× More accurate than avg frontier LLM
2.3pp From the human accuracy ceiling
Live simulation
01 — Question
—
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02 — Population
Converts Uncertain Won't
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03 — Simulation
0 agents
Query agents
Weigh beliefs
Test hypotheses
Record outcomes
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04 — Finding
Recommendation
The calibration loop

Simulations compound.

Every decision routed through Simulatte generates calibrated behavioral data. The more you run, the sharper every future simulation becomes — for you and across the platform.

01 — Run
Simulate
A decision scenario runs against a population of psychologically calibrated agents. 200+ distinct behavioral profiles, each with persistent identity and working memory.
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02 — Learn
Capture
Every response — preference, reasoning, deviation from baseline — becomes a calibration data point. The system sees what it got right and what it missed.
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03 — Improve
Compound
The model recalibrates. Persona identity persists — core beliefs retained, working memory resets. Your second engagement costs a fraction of your first and delivers more.
Accuracy compounds over time
Applications

Before the real
world tests you.

01
Before you launch
You have a campaign concept. Run it through 800 target-market agents before buying a single impression. Know which segments convert, which ignore it, and exactly why.
02
Before you price
A price change affects different buyer profiles differently. Run the scenario on your population. See the elasticity curve — not a single A/B result weeks later.
03
Before you commit
A new market entry, a product reformulation, a channel shift. The decision looks clear from inside. Run it through the simulation first. The distribution of outcomes will tell you what you're missing.
Evidence

Numbers you can take into a board room.

88.7%
Accuracy vs Pew Research US benchmark across 15 attitudinal questions. 2.3pp from the human self-consistency ceiling.
Pew Research ATP · 15 questions · Sprint B-10
85.3%
Accuracy vs Pew + CSDS-Lokniti India benchmark. Religion × caste × region × language × political-lean — 5 intersecting axes. The only system to resolve Indian cultural complexity at benchmark.
Pew + CSDS-Lokniti India · Sarvam infrastructure
4.9×
More accurate than the average frontier LLM on the India benchmark. GPT-4o — the best LLM tested — reached 75.6%. More capable ≠ more calibrated.
10 LLMs tested · 5,878 SHA-256 verified API calls · See full comparison →
Client outcomes

What the simulation
found.

Real engagement results. The non-obvious insight is always the one worth paying for.

CS-011
Fintech · India · CS-011
Savings Habit Decay & AutoPay Churn
+41pts
Income predicted almost nothing about who churned. Identity around saving predicted everything.
Read case →
CS-012
E-Commerce · Amazon · CS-012
The Keyword That Blocked Its Own Market
83%
One diet keyword suppressed 83% of the addressable audience. Nobody at the company had noticed.
Read case →
CS-013
D2C · Creator Economy · CS-013
Creator Purchase Authority Audit
3×
The creator with 10% of the followers was converting 3× better. Reach is not authority.
Read case →
CS-014
FMCG · Brand · CS-014
The Brand Name Ceiling
0/3
Brand architecture options tested. The category-defining name had become a psychological ceiling on expansion.
Read case →
CS-015
Consumer Goods · India · CS-015
Tier 2/3 Market Entry — The Channel Nobody Budgets For
5 cities
Purchase ran through kirana networks and local trust — channels outside the brand's entire go-to-market plan.
Read case →
View all client studies →
Run the scenario

The model is ready.
Is the decision?

Tell us what you're deciding. We'll scope the simulation and send you a proposal within 48 hours.

Read case studies

Custom pricing · Scoped per engagement · No retainer required