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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), CSV, and ZIP archive uploads for claims data. The platform auto-detects the report type inside your upload — claims, carrier/denial, or appointment reports — and 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 upload an Excel, CSV, or ZIP claims export immediately. The analytics pipeline runs automatically after upload and the full dashboard populates within minutes.

Any system that can export claims data to Excel or CSV works with Vanaa Analytics. The platform includes dedicated processing pipelines for DrChrono and Osmind exports, and its smart file detector recognizes standard claims, carrier/denial, and appointment report layouts from most practice management systems automatically.

The platform first detects the report type from the file's columns, then dispatches a background processing task. The pipeline validates rows, computes charges, payments, and adjustments, calculates GCR/NCR, deposit summaries, denial and aging breakdowns, and provider/payer rollups, and stores the results against your account. Your dashboard populates automatically once processing finishes — typically within minutes.

Uploads are processed by Celery (a distributed task queue backed by Redis), so heavy calculations run independently from your browser session — you can navigate the platform freely while processing runs. Progress and status updates are tracked per upload and visible on the analytics dashboard.

Yes. Beyond standard claims files, the platform processes carrier reports with denial detail (producing denial code summaries and denied vs. paid aging buckets) and appointment reports (producing visit and scheduling summaries). Multiple related files can be bundled in a single ZIP upload and each is routed to the correct pipeline automatically.

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
• Denial analytics: denial code summaries with denied vs. paid aging and monthly trends
• AR days and monthly days-in-AR tracking
• 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, data uploads, 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)

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.