Helix Ai
Helix AI provides a regulated workflow platform that transforms raw fund data files into published, traceable documents like PRIIPs KIDs and CCI Product Summaries. The platform uses a deterministic core for all regulatory calculations, with LLMs strictly limited to extraction and classification tasks, ensuring outputs are reproducible and audit-ready. Every file, change, and operator action is logged with field-level audit trails, enabling replayable, regulator-ready reporting on demand.
- Financial Technology
- Regulatory & Compliance Technology
- Software Only
- Artificial Intelligence
Funding
Founders
Founder details are not available yet.
Product
Problem
Investment firms face significant operational burden and compliance risk when producing regulated fund documents like PRIIPs KIDs and CCI product summaries. Manual data handling, inconsistent validation, and opaque audit trails make it difficult to ensure accuracy, traceability, and regulatory compliance across every output.
Solution
Helix AI provides a regulated workflow platform that transforms raw fund data files into published, traceable documents. The platform ingests CSV or XLSX files, auto-detects column types against a canonical schema, and validates every record before it reaches the canonical store. A deterministic core executes all PRIIPs and CCI calculations, while LLMs are strictly limited to extraction and classification tasks, never writing to published fields. Every file, change, and operator action is logged with field-level audit trails, enabling replayable, regulator-ready reporting on demand. The platform supports multi-file ingestion linked by ISIN and as-of date, with a four-eyes generation workflow requiring human sign-off before publication.
Target Audience
Primary customers are asset managers, fund manufacturers, and financial institutions that need to produce PRIIPs KIDs, CCI product summaries, and related regulatory documents with high accuracy and full auditability.
Features
- Deterministic core for PRIIPs and CCI regulatory calculations, ensuring outputs reproduce identically on rerun
- Bounded LLM usage limited to data extraction and classification, with hallucinations unable to reach published fields
- Field-level audit trail tracking every operator, document, timestamp, and data field, with indefinite retention and replayable outputs
- Multi-file ingestion linked by ISIN and as-of date, preventing data assembly from conflicting sources
- Four-eyes generation workflow requiring human sign-off before documents are published
- Built against PRIIPs RTS 2017/653, CCI Template v1.0, EMT v4.3, openfunds CDS v2.13.0, and FundsXML 4.2.x standards
- SFTP, API, and feed delivery options for output distribution