50+
Data Connectors
100+
Process Layers
90%
Forecasting Accurancy
100%
GDPR Comliant
Gilion's AI Infrastructure
A finance-native AI stack engineered from the ground up for building tailored real-world company understanding for investment funds, capital institutions and entrepreneurs. Gilion’s infrastructure fuses event-driven data ingestion, ML and generative AL modeling, and context-aware reasoning orchestration, forming a complete pipeline from raw input to investment-grade intelligence.
50+ Realtime Connectors
Flexible connector infrastructure, with 50+ streaming realtime data connectors across ERP, CRM, payments, app analytics, and market APIs
Unified financial ontology
Unified financial ontology translating heterogeneous schemas into canonical business entities and individual fund’s metrics definitions, for example revenue, cohorts, churn, LTV, product lines, customer segments
10k-Layer DAG Engine
Individualized Deep Directed Acyclic Graph infrastructure across every single companies, orchestrating 10 000+ transformation layers with versioned lineage and advanced re-computation logic.
Self-serve ingestion layer
Self-serve ingestion layer with schema inference, validation, monitoring and reusable transformation templates.
Event-time alignment & backfill
Event time alignment and backfill logic for long-horizon growth analytics
Agentic AI Network
Gilion’s Agentic AI Network is a distributed reasoning architecture designed for investment-grade research automation. Each node in the network represents an autonomous analytical agent with a defined reasoning scope, operating within a codified workflow graph, orchestrate tools usage, designed to facilitate and amplify human research patterns.
Structured Reasoning Graph
Hierarchical reasoning graph infrastructure that allow decomposition of complex research questions into structured sub-problems, controlling the definitions of research activities and positive and negative signals with high precision
Agent-Level OODA Loop
OODA-loop orchestration — Observe, Orient, Decide, Act — embedded at the agent level, driving best practice conclusion-led research processes
Multi-agent synchronization
Multi-agent synchronization enabling concurrent hypothesis generation and validation
Context partitioning & scope control
Context partitioning & adaptive scope control for efficiency and truth consistency
Multimodal retrieval & synthesis
Multimodal retrieval and synthesis across structured data, text corpora, models, transcripts, MCPs and APIs
Agentic Frontend Layer
The frontend is not a static interface — it’s a self-assembling reasoning environment. It composes dynamically based on the query, context, and investment methodology, enabling human–AI collaboration with deep and efficient interpretability.
Adaptive Narrative Engine
Agentic UI compiler rendering adaptive research narratives in real time
Traceable Insight Graphs
Drillable visualization nodes that trace every data point and argument back to its origin
Transparent Reasoning Lineage
Explainability hooks exposing the reasoning lineage behind every output
Continuous Context Memory
Session persistence maintaining context across interactive exploration
Modular Analysis Interface
Composable interface framework integrating live forecasting, benchmarking, and portfolio analysis modules
Dynamic Accuracy Engine
A continuous evaluation and optimization layer that quantifies, improves, and self-corrects analytical performance.
Automated Model Orchestration
AutoML orchestration pipeline spanning XGBoost, Prophet, Transformers, LSTMs, and hybrid ensembles
Continuous Model Retraining
Dynamic retraining framework that continuously re-benchmarks models on live incoming data
Comprehensive Model Scoring
Multi-metric evaluation tracked across models and cases
Thesis-Driven Model Tuning
Thesis-aligned tuning integrating human judgment and fund-specific heuristics
Transparent Accuracy Scores
Explainable accuracy scoring propagated through every analytical layer
Security, Privacy & Data Integrity Layer
A security-first infrastructure. Every component of the stack is built on zero-trust principles and EU-native data governance.
Isolation
Tenant isolation by design — per-fund compute, storage, and agent sandbox
Zero PII architecture
Zero PII architecture with anonymization at ingestion
Connector-Level Compliance
GDPR compliance at the connector level, not just at output
End-to-End SOC2
SOC 2 Type II certification covering full operational lifecycle
Confidence-Aware Reasoning
Data triangulation and confidence propagation across the reasoning graph
Customizability & Extensibility Layer
An investment platform is a programmable research engine, built purposely to adapt. Every investor operates differently, and the platform’s architecture reflects that.
Fully Customizable Analysis
Every analysis tree, at every node, supports custom instructions and methodological overrides
Investor-Defined Reasoning
Investors can encode fund-specific logic, hypotheses, and quality criteria directly into the reasoning tree
Directive-Guided Workflows
Agents interpret these as structured behavioral directives, guiding tailored research workflows
Unified Custom Integrations
The platform supports custom integrations into proprietary datasets, internal code modules, or external APIs — all orchestrated within the same analytical context
Unlimited Data Ingestion
A universal data integration interface allows users to define new connectors, ingest any data type, and implement custom metric transformations
Deep System Extensibility
Data triangulation and confidence Composable code blocks and plugin interfaces enable deep extensibility — from new forecasting algorithms to alternative risk models across the reasoning graph
