Governed computation on ungoverned data is not defensible. Before the analytics workflow can produce audit-ready output, the data layer needs to be structured, versioned, and traceable. We build the investment data infrastructure that makes every governed run reproducible from source to output.
Analysts spend Monday morning downloading CSVs from Bloomberg, correcting column names, and manually updating a shared database before any analysis can run. That is not analytical work — and it means every downstream output inherits the fragility of the input process.
When portfolio data, benchmark series, NAV history, and position files live in a versioned, queryable data store with defined schemas and quality checks — every governed workflow run can reference a traceable input. The audit trail starts at the data layer, not at the computation.
Alpha Quant Agent's methods consume structured data inputs. A properly built data layer means runs are reproducible across time, inputs are auditable, and anomalies in source data are caught before they propagate into a client report.
Automated, scheduled pulls from Bloomberg, Refinitiv, custodian feeds, prime broker files, or internal systems. Data arrives in defined schemas — no manual CSV handling, no column-name corrections.
Metadata layer covering asset universe, benchmark definitions, data source lineage, update frequency, and field-level documentation. Every analyst knows what data exists, where it came from, and when it was last updated.
Structured tables for NAV history, position data, benchmark constituents, and corporate actions — designed for the query patterns that quant analytics actually require. Built on your Azure tenant, not a shared cloud database.
Automated checks that run on arrival: missing fields, stale timestamps, outlier values, schema violations. Failures are flagged before a workflow run is triggered. No silent bad data in a governed output.
Every data update is versioned. A run from six months ago can be reproduced against the exact data that existed at that point in time — a requirement for any serious regulatory audit or LP query.
Data lineage from source to output
Every field in every governed run traces back to a source record with a timestamp and ingestion log. No unattributed inputs.
Your data never leaves your Azure tenant
Ingestion pipelines, databases, and catalog layers are built and deployed inside your environment. Nexqion has no ongoing access after handover.
Point-in-time reproducibility
Data versioning ensures any past run can be reproduced exactly as it was executed — input state, method version, output — for regulatory audit or LP query response.
Quality gates before every run
No governed workflow run can start if the upstream data has not passed validation. Bad data is caught at ingestion, not discovered in a client report.
Single-fund investment team
Outcome
Analysts reclaimed Monday morning. Every governed run references a traceable, versioned input.
Multi-strategy fund
Outcome
Any historical run can be reproduced exactly — inputs, methods, and output — for regulatory or LP review.
Migration mandate
Outcome
Historical data is now queryable, auditable, and ready to serve the governed analytics workflow.
Audit
Inventory existing data sources, formats, quality, and gaps.
Design
Define target schema, ingestion patterns, and quality rules.
Build
Implement pipelines, database, catalog, and validation layer.
Validate
Confirm data integrity, reproduce historical runs, hand over with runbook.
Typical engagement: 6–12 weeks depending on source complexity and data history depth.
Start with an audit of your current data sources. We'll map what exists, what's missing, and what a structured investment data layer looks like for your specific workflow.