Causal Discovery Engine
9 advanced causal discovery methods plus PC algorithm, Pearl's do-calculus, counterfactual engine, and transfer entropy. Benchmarked against CausalRivers (ICLR 2025 Spotlight).
Default: Calibrated Ensemble
The default method is a weighted vote across 4 scoring methods with an agreement bonus for cross-method consensus.
| Method | Weight | What It Does |
|---|---|---|
| Conditional Multivariate Granger | 3.0 | Tests X→Y while controlling for ALL other variables. Eliminates spurious edges from confounders. |
| Cascade-Aware Scoring | 1.5 | Detects indirect paths via lag decomposition: if lag(A→B) ≈ lag(A→C) + lag(C→B), penalizes A→B. |
| Pairwise Granger | 1.0 | Baseline: does X happening predict Y happening later? F-test on VAR models. |
| P-value Scoring | 0.8 | Statistical significance weighting. |
Use Cases
Software Engineering as a Service (SE-aaS)
AI Workers that understand your codebase, infrastructure, and delivery pipelines. 27 domains cover pod matching, early warning signals, scope creep alerts, and delivery intelligence. The worker remembers every past sprint, learns which interventions worked, and gets smarter every week. Brain IQ adapts depth of reasoning — simple queries use System-1 (<100ms), complex ones run full LATS + self-consistency.
Worker detects: PR velocity dropped 18% → pod-match recommends 2 senior engineers → early-warning flags flight risk on 3 accounts → delivery intelligence shows scope creep at 140% → all in one query, 2.1s response time
Accounting as a Service (AaaS)
AI Workers that handle financial operations end-to-end. 11 autonomous agent types cover reconciliation, anomaly detection, cash flow forecasting, compliance monitoring, and more. Each worker has persistent memory of your chart of accounts, historical patterns, and seasonal rhythms. Failures are remembered — the same mistake doesn't happen twice.
Worker flags: accounts receivable aging 42 days (above 30-day baseline) → traces to 3 enterprise clients with delayed approval cycles → proactive collection triggered → compliance status updated — zero human touch
Project Management as a Service (PM-aaS)
AI Workers that orchestrate cross-functional delivery. 7 domains cover sprint planning, risk detection, stakeholder communication, resource allocation, dependency mapping, milestone tracking, and retrospective analysis. Process Intelligence FSM handles structured workflows with HITL gates for EU AI Act Article 14 compliance.
Worker orchestrates: sprint capacity drops 20% (two engineers sick) → scope automatically deprioritized with stakeholder notification → dependency graph updated → delivery date revised → risk score recalculated — deterministic FSM, auditable decision trail
Research & Analysis
AI Workers that handle multi-step research tasks requiring real-world data. Full 36-layer pipeline: web search with LRU cache, Wikipedia infobox extraction, backward chaining to pre-structure answers, adversarial self-verification, and self-consistency voting (K=1–5). GAIA benchmark: 100% on Level 1 (30/30), targeting 90%+ overall.
Question: 'What is the GDP of the country that hosted the 2020 Olympics?' → System-1 classifies → System-2 engages → backward chaining structures search → web search + Wikipedia → adversarial verifier checks answer → K=3 consensus — 94% confidence, 3.2s
Enterprise AI Worker Fleet
Deploy multiple AI Workers, each with specialized capabilities and a shared workspace brain (L25–L29 federated knowledge). Workers collaborate via A2A protocols — one worker can call another. Brain IQ scales compute allocation per-worker based on task complexity. The fleet compounds intelligence: each worker's learning benefits all others through federated causal edges.
Workspace with 5 workers: SE-aaS worker discovers deploy-to-churn causal edge → federated to PM-aaS worker → PM-aaS incorporates into sprint risk model → AaaS worker sees revenue impact → all three coordinate response within same workspace brain
CausalRivers Benchmark
ICLR 2025 Spotlight — tested on real hydrological time series with known ground-truth causal structure.
| Dataset | AUROC | F1 | Accuracy |
|---|---|---|---|
| random_3 | 0.828 | 0.829 | 0.868 |
| close_3 | 0.818 | 0.822 | 0.865 |
| confounder_3 | 0.714 | 0.712 | 0.799 |