Stop Guessing.
Start Knowing.
AI platforms that normalize bias, verify against ground truth, and report with honest uncertainty. From causal discovery to regulatory compliance to investment intelligence. Swiss-engineered rigor.
Your ML model tells you what.
It can't tell you why.
Predictive models find patterns. Causal models find mechanisms. When the question is "would the outcome have changed?" — you need causation, not correlation.
Prediction (ML)
“Claims correlate with age”
Black box. No explanation.
Causal Libraries
“Age increases claims by 12%”
Code-heavy. Fragmented. PhD required.
InnoVertex
“If we raise premiums by 5%, reserves drop CHF 2.3M”
AI copilot. Validated. Any analyst can use it.
Four Products. One Mission.
State-of-the-art decision making — from Python library to enterprise platform.
CausalEdge Platform
71 methods. 19 benchmarks. Zero guesswork.
Causal inference platform with AI copilot. Consensus discovery across multiple algorithms, doubly robust estimation, refutation tests that try to break your own findings, sensitivity analysis that quantifies what you might have missed. Every result validated against published ground truth.
AAL
Auditable intelligence for regulated industries.
Enterprise analytics for IFRS17, Solvency II, Basel III — domains where the cost of being wrong is catastrophic. Multi-agent orchestration with quality validation, human-in-the-loop approvals, and auditable paths from data to every conclusion.
CausalEdge Core
The engine underneath.
Python library unifying DoWhy, EconML, and CausalML into one API. Consensus discovery across multiple algorithms, conformal prediction intervals with guaranteed coverage, LLM-powered priors verified by statistical tests. Every method calibrated against published benchmarks with known outcomes.
InsightOut
Due diligence that survives scrutiny.
Investment intelligence platform for VCs, accelerators, and startup stakeholders. Normalizes the inherently biased due diligence process into structured, validated decisions. Multiple layers of verification, evidence scoring, and bias correction — so pre-investment conclusions hold up under scrutiny.
Why Not Just Use Open Source?
DoWhy, EconML, and CausalML are excellent libraries. But they cover one layer. CausalEdge covers the full stack — from discovery to counterfactuals to enterprise deployment.
| Capability | CausalEdge | DoWhy | EconML | CausalML |
|---|---|---|---|---|
| Causal Discovery (L1) | 12 algorithms + consensus | Limited | — | — |
| Treatment Estimation (L2) | 45+ methods | Yes | Yes | Yes |
| Counterfactuals (L3) | Full SCM engine | Limited | — | — |
| AI Copilot | Natural language | — | — | — |
| Web Platform | Full dashboard | — | — | — |
| Interactive Notebook | Built-in kernel | — | — | — |
| Benchmark Validation | 19 datasets | 3 | 2 | 5 |
| Transfer Intelligence | Auto-match | — | — | — |
| Report Generation | PDF/PPTX/HTML | — | — | — |
| RBAC & Audit | 18 roles | — | — | — |
The Only Platform Covering All Three Levels
Pearl's Ladder of Causation. DoWhy covers L2. EconML covers L2. CausalML covers L2. InnoVertex covers L1 + L2 + L3.
See
Association
Discover causal structure from data. 12 algorithms. Consensus ensemble.
Do
Intervention
Estimate treatment effects. 45+ methods. Doubly robust, neural, ML.
Imagine
Counterfactual
What would have happened? SCM engine. do-calculus. Twin networks.
Any Domain. Any Question.
Our platforms are domain-agnostic. The causal question is universal.
Insurance
“What drives claims frequency? What would reserves look like under alternative scenarios?”
Finance & Banking
“Does this intervention reduce credit default? What's the causal effect of a rate change?”
Pharma & Healthcare
“What's the real treatment effect? Would this patient have responded to the alternative?”
VC & Startups
“Which startups will succeed — and WHY? Not correlations. Causes.”
Manufacturing & Retail
“What caused the quality drop? What happens if we change the supplier?”
Public Policy
“Did the program work? What would have happened without the intervention?”
Normalize. Verify. Report honestly.
Between raw data and accurate conclusions, there is bias — confounders, noise, selection effects. InnoVertex builds AI platforms that systematically eliminate that gap. Every method is validated against published benchmarks with known outcomes. Every claim survives refutation testing. Every result reports honest uncertainty. We don't optimize for answers. We optimize for getting closer to what's actually true.
For Data Scientists
CausalEdge Core — the same 71+ methods, as a Python library. Domain-agnostic. Infrastructure-agnostic. One install.
Available through our private registry. Contact us for access.
from causaledge import CausalEngine
engine = CausalEngine()
result = engine.estimate_effect(
data=df,
treatment="intervention",
outcome="outcome",
method="aipw",
)
print(f"ATE: {result.ate:.3f}")
print(f"95% CI: [{result.ci_lower:.3f}, {result.ci_upper:.3f}]")
# ATE: 0.342
# 95% CI: [0.198, 0.486]Ready to move from correlation to causation?
Join the teams making decisions based on why, not just what.