Connect Investment Data Without Months of ETL

Unify custodians, fund admins, and market data into a governed data layer. Pre-built connectors and domain models for positions, NAVs, transactions, and securities.

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Connect any asset class or data source

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Built for the complexity of investment data

Every custodian, fund admin, and market data vendor delivers differently. Aquata already knows the formats, the models, and the mappings

Unified, governed, and trusted data.

Every source maps into 350+ pre-built domain models—covering positions, NAVs, transactions, securities, performance, and attribution. 10,000+ attributes validated on ingestion. Role-based access and full lineage built in.

Explore the data model

Any source. Any format. Without the integration tax.

Pre-built connectors to custodians, fund admins, market data, portfolio systems, and cloud warehouses. Extract data from any PDF or file automatically. No custom ETL. No parsers. Just configuration.

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Platform Capabilities

Everything you need to ship data infrastructure fast

Pre-built connectors, data models, and quality checks so your team spends time on insight, not plumbing.

API Connectors

State Street, Bloomberg, Alter Domus, and more. Configure and deploy without writing custom ETL.

Data Access Controls

350+ models covering positions, NAVs, securities, and performance. 10,000+ attributes ready to use.

Built-in Data Quality

Exception-based validation across six dimensions on ingestion. Issues surface before they reach your warehouse.

Bulletproof Data pipelines

Visual pipeline builder with event-driven triggers, parallel execution, and AI Copilot. Low-code for common transforms, full SQL when you need it.

Unstructured Data Extraction

Upload capital call or loan notice PDFs and get structured, validated data automatically.

MCP for AI Connectivity

Connect Claude or ChatGPT to your investment data through one endpoint. Deploy connectors, run queries, and build pipelines — with full audit trails.

How long does your team take to add a new data source?

The Old Way

How teams have always done it

The Aquata Way

How it works now

Connecting to a new data source

Custom code per source. APIs, file feeds, PDFs. 3-6 months per integration.

100+ pre-built connectors plus LLM-powered document extraction. Configure and deploy in weeks.

Normalizing data across sources

Every source has its own format. Your team writes and maintains mapping logic for each one.

350+ domain models. Positions, NAVs, transactions, securities normalized on ingestion.

Data quality and access controls

Manual spot checks and shared logins. Issues surface in reports, not at the source. No control over who sees what.

Six-dimension validation on ingestion. Column-level access controls. Role-based permissions down to individual attributes.

AI and agents

Copy-paste between your data tools and ChatGPT. Build custom integrations to give AI access to your data. No governance, no audit trail.

Query your data in plain English. AI agents build pipelines, author validation rules, and deploy connectors. Claude and ChatGPT connect directly via MCP.

Observability and data catalog

Tribal knowledge and spreadsheets. No way to trace where a figure came from or where it went.

Full data catalog. Source-to-report lineage. Know where every figure has been and where it's going.