Sooth Labs

Name: Sooth Labs
Brand: Sooth Labs

AI Native

Strategy needs foresight. We're building the engine that delivers it.

Est 2026|Valuation $335M|Raised $50M

LinkedIn 688|Website|FTEs 10|HQ Pittsburgh, Pennsylvania, US

MarketsScenario Modeling & "What-if" Analysis Quantitative Analytics

01Competitive Landscape

| Aspect | Sooth Labs | Metaculus | Kalshi | |--------|------------|-----------|--------| | Starting Price | Not public | Public/community-access offering with optional paid/contract services; published pricing details are limited | Transactional pricing model with fees tied to trading activity; vendor describes a trade-based fee structure | | Typical fit | Enterprises seeking scenario-driven probabilistic forecasts across domains (vendor/investor materials describe this focus) | Teams seeking a transparent track record, community forecasting, and optional pro services | Participants needing regulated event contracts in a specific jurisdiction, where trading prices serve as signals | | Key Strength | Described in vendor/investor materials as offering interactive causal/scenario modeling; company states the system is not based on a traditional LLM approach | Large public question set, scoring and aggregation engine, visible track record | Market-based probabilities with liquidity when markets are active | | Key Weakness | Very limited public product detail, pricing, documentation, and third-party validation today | May not map perfectly to bespoke enterprise propositions without customization | Market availability/category constraints; not designed as an internal decision workflow tool | - Direct competitors and their key differentiators - Metaculus differentiates via transparency (resolved questions, scoring, public methodology) and an established forecasting community. - Prediction markets (Kalshi/Polymarket/Manifold) differentiate by producing price signals through trading, but are constrained by what markets exist, regulatory access, and the difference between tradeable contracts and internal decision propositions. - Feature comparison: where this product appears to compare relative to alternatives - Potential strengths (based on vendor/investor narrative, not validated): - Interactive scenario planning with editable causal chains, as described in investor materials. - Likely lags (based on lack of public material): - Transparency of performance track record - Documentation and self-serve evaluation - Public security/compliance posture - Pricing comparison: value proposition vs alternatives - Unknown for Sooth Labs; alternatives range from community/public-access platforms (Metaculus) to transaction-fee models (Kalshi) to bespoke enterprise contracts. - Customer testimonials comparing this product to competitors - Not found in accessible sources. - Market share and adoption trends - Not available for Sooth Labs.

01Competitive Landscape

Aspect	Sooth Labs	Metaculus	Kalshi
Starting Price	Not public	Public/community-access offering with optional paid/contract services; published pricing details are limited	Transactional pricing model with fees tied to trading activity; vendor describes a trade-based fee structure
Typical fit	Enterprises seeking scenario-driven probabilistic forecasts across domains (vendor/investor materials describe this focus)	Teams seeking a transparent track record, community forecasting, and optional pro services	Participants needing regulated event contracts in a specific jurisdiction, where trading prices serve as signals
Key Strength	Described in vendor/investor materials as offering interactive causal/scenario modeling; company states the system is not based on a traditional LLM approach	Large public question set, scoring and aggregation engine, visible track record	Market-based probabilities with liquidity when markets are active
Key Weakness	Very limited public product detail, pricing, documentation, and third-party validation today	May not map perfectly to bespoke enterprise propositions without customization	Market availability/category constraints; not designed as an internal decision workflow tool

Direct competitors and their key differentiators
- Metaculus differentiates via transparency (resolved questions, scoring, public methodology) and an established forecasting community.
- Prediction markets (Kalshi/Polymarket/Manifold) differentiate by producing price signals through trading, but are constrained by what markets exist, regulatory access, and the difference between tradeable contracts and internal decision propositions.
Feature comparison: where this product appears to compare relative to alternatives
- Potential strengths (based on vendor/investor narrative, not validated):
  - Interactive scenario planning with editable causal chains, as described in investor materials.
- Likely lags (based on lack of public material):
  - Transparency of performance track record
  - Documentation and self-serve evaluation
  - Public security/compliance posture
Pricing comparison: value proposition vs alternatives
- Unknown for Sooth Labs; alternatives range from community/public-access platforms (Metaculus) to transaction-fee models (Kalshi) to bespoke enterprise contracts.
Customer testimonials comparing this product to competitors
- Not found in accessible sources.
Market share and adoption trends
- Not available for Sooth Labs.

Aspect

Sooth Labs

Metaculus

Kalshi

Starting Price

Not public

Public/community-access offering with optional paid/contract services; published pricing details are limited

Transactional pricing model with fees tied to trading activity; vendor describes a trade-based fee structure

Typical fit

Enterprises seeking scenario-driven probabilistic forecasts across domains (vendor/investor materials describe this focus)

Teams seeking a transparent track record, community forecasting, and optional pro services

Participants needing regulated event contracts in a specific jurisdiction, where trading prices serve as signals

Key Strength

Described in vendor/investor materials as offering interactive causal/scenario modeling; company states the system is not based on a traditional LLM approach

Large public question set, scoring and aggregation engine, visible track record

Market-based probabilities with liquidity when markets are active

Key Weakness

Very limited public product detail, pricing, documentation, and third-party validation today

May not map perfectly to bespoke enterprise propositions without customization

Market availability/category constraints; not designed as an internal decision workflow tool