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The platform is built for healthcare billing managers, revenue cycle directors, CFOs, and billing companies that manage medical claims data. Whether you run a solo practice, multi-provider group, or a third-party billing service, Vanaa Analytics automates the analytics workflows that previously required hours of manual Excel work.

We support Excel (.xlsx / .xls) and CSV files for claims data uploads. You can also connect to SQL Server databases via ODBC for direct, real-time data integration. The platform handles advanced parsing and edge-case scenarios (messy date formats, blank rows, mixed currency) automatically.

Sign up with your work email — you'll receive an OTP code to verify your address. Once logged in, you can connect your ODBC database or upload an Excel/CSV file immediately. The analytics pipeline runs automatically after data ingestion and the full dashboard populates within minutes.

Vanaa Analytics is optimized for Microsoft SQL Server connections. You will need a configured ODBC DSN on the server where Vanaa Analytics is deployed. The platform uses SQLAlchemy with pyodbc as the ODBC driver layer, which supports SQL Server 2012 and later. Other ODBC-compatible databases may work with custom DSN configuration.

Database credentials (host, port, database name, username, and password) are stored server-side in the application database, associated with your user account. In production, these should be encrypted at rest using your database encryption settings. Credentials are never stored in the browser or transmitted in URLs. We recommend using a dedicated read-only SQL Server user account for the ODBC connection.

When you initiate a data pull, Vanaa Analytics dispatches a background task using Celery (a distributed task queue backed by Redis). This means the data fetch runs independently from your browser session — you can navigate the platform freely while the pull runs. Progress and status updates are tracked in the database and visible on the ODBC connectivity page.

Yes. You can upload an Excel (.xlsx) or CSV file containing your claims data from any billing system export. The platform parses and processes the file through the same full analytics pipeline as a live ODBC pull. File uploads are ideal when you don't have direct database access or are using a cloud-hosted EHR with export capabilities.

The platform automatically generates:

• Monthly and yearly GCR (Gross Collection Rate) and NCR (Net Collection Rate) summaries
• Deposit-month payment analyses with date-of-service reconciliation
• Payer (insurance carrier) performance breakdowns with denial rates and AR aging
• Provider-level summaries with charges, payments, and collection rates
• Aging bucket reports: 0–30, 31–60, 61–90, and 90+ days
• Working days vs. calendar days payment lag analysis

All reports are exportable to formatted Excel workbooks.

GCR (Gross Collection Rate) measures the percentage of billed charges that are actually collected, before adjustments. NCR (Net Collection Rate) measures collections against the amount you are contractually allowed to collect — after contractual adjustments. NCR is the more meaningful KPI for billing performance benchmarking. Industry best practice is NCR ≥ 95%. Vanaa Analytics tracks both at monthly, yearly, and carrier levels.

Yes. Every analytics report, summary table, and AI-generated output can be exported to a formatted Excel workbook (.xlsx) with a single click. Reports include proper headers, formatted numbers, and tabular layouts ready for presentation.

The carrier analytics view ranks your insurance payers by total charges, payments, collection rate, denial rate, and AR aging. This lets you quickly identify which payers are slow-paying, denying at higher rates, or dragging down your overall NCR. You can compare carrier performance across months and export the analysis to present to your team or payer representatives.

The AI Analyzer Agent uses Google's Gemini AI model with RAG (Retrieval-Augmented Generation). After your analytics data is processed, the agent is provided with full context — collection rate summaries, payer breakdowns, deposit analyses — and can answer natural language questions about your data, generate custom Python Pandas code for deeper analysis, create visualizations, and produce downloadable reports.

You can ask questions in plain English, such as:

• "What was the NCR for Medicare in Q3?"
• "Which provider had the highest GCR last month?"
• "Show me the trend in total payments over the last 6 months"
• "What payers have more than 15% denial rate?"
• "Generate a Python script to compare Q1 vs Q2 deposit totals"
• "Create a bar chart of top 10 payers by collected amount"

The agent maintains context across a conversation, so you can ask follow-up questions naturally.

The AI agent's context is limited to the aggregated analytics outputs (collection rates, deposit summaries, payer rankings, provider totals) rather than individual patient records. Raw claim-level data stays in your database. For HIPAA-compliant deployments using PHI, you must configure your Gemini API with a BAA from Google and operate within a HIPAA-compliant cloud environment.

Yes. The AI Analyzer Agent requires a Google Gemini API key (available from Google AI Studio). In self-hosted deployments, the key is configured in the server's environment variables. Administrators manage the API key — end users don't need to configure anything. For cloud-managed Vanaa deployments, the key is included in your plan.

The platform can be configured for HIPAA compliance. For processing PHI, you must deploy on a HIPAA-eligible cloud environment (e.g., AWS GovCloud, Azure Healthcare APIs), enable encryption at rest and in transit, and sign a BAA with your AI provider (Google). We provide a HIPAA compliance checklist and deployment guidance. Out-of-the-box, the platform is designed with security best practices but is not pre-certified.

The platform uses OTP (One-Time Password) email authentication. When you log in, a time-limited OTP code is sent to your registered work email. You enter this code to gain access — no password to remember or leak. This approach eliminates password-based attacks and ensures only those with access to the registered email can log in.

All sensitive configuration (API keys, database credentials, secret keys) should be stored in environment variables and a secrets manager in production. The platform supports Azure Key Vault, AWS Secrets Manager, and HashiCorp Vault. Never commit secrets to source control. Configuration uses Django's SECRET_KEY pattern with environment variable injection. The .env file should be added to .gitignore.

Yes. All analytics features require authenticated sessions (login required). Only registered users can access the analytics dashboard, ODBC settings, and AI agent. New user registration is controlled via email OTP verification. For enterprise deployments, the platform can be configured to allow only emails from specific domains or a pre-approved whitelist.

Yes. Vanaa Analytics is a Django-based application that can be deployed on your own server, on-premises, or on any cloud provider. You'll need Python 3.10+, PostgreSQL, Redis, and a Celery worker. Full deployment documentation is available on request. Self-hosted deployment gives you complete control over your data environment.

The platform requires:

• Python 3.10+ (runtime)
• Django 5+ (web framework)
• PostgreSQL (application database)
• Redis (Celery task broker)
• Celery (background workers)
• A Google Gemini API key (for AI features)
• ODBC Driver 17+ for SQL Server (for ODBC connectivity)

The platform runs on Linux, Windows Server, or macOS and can be containerized with Docker.

Use the Demo Request form to schedule a personalized walkthrough. For technical support or deployment questions, reach out through the contact options below. We typically respond within 1 business day.