Product Roadmap

Explore our planned features and improvements as we continue to enhance GraphPolaris. Our roadmap reflects our commitment to delivering powerful graph analytics capabilities and an exceptional user experience.

Available Feature Set

🎯 Core Platform

No‑code Explorer with GPQL visual query language for live, code‑free graph querying and analysis. GraphPolaris provides a no-code, end-to-end visual (Knowledge) Graph analytics platform with structured capabilities across Core Platform, Visualizations, AI-powered Exploration, and Infrastructure, enabling domain experts to query, explore, and share insights without coding while scaling from cloud to on‑prem deployments. The feature set spans visual schema/query building, best‑of‑breed graph visualizations, AI guidance and recommendations, vendor‑agnostic connectors, and a cloud‑native microservice backbone.

Supported Data Sources: GraphPolaris connects to diverse data sources without requiring data engineering overhead:

  • Graph Databases - Connect to available Knowledge Graph sources (e.g., Neo4j, ArangoDB, Amazon Neptune, Memgraph) via connectors
  • File Formats: Excel (.xlsx), CSV, Parquet with automatic schema detection
  • Relational Databases: PostgreSQL, MySQL, SQL Server import into Knowledge Graphs

Automatic Schema Extraction - What This Means: When you upload a CSV/SQL DB connection/Parquet/Graph Database dump file, GraphPolaris automatically:

  • Detects column data types (dates, numbers, categories, text)
  • Identifies potential foreign keys and relationships (high-cardinality references)
  • Suggests which columns should be nodes vs. properties
  • Recommends composite indexes for multi-attribute matching

📊 Advanced Visualization Suite

Each visualization is optimized for specific analytical scenarios:

Graph Network Visualizations

1. Node-Link Diagram

  • Use Case: Overall network structure, community identification, centrality analysis
  • Features:
    • ForceAtlas2 layout: Physics-based simulation that automatically reveals hubs (cluster centrally), communities (separate visually), and bridges (nodes connecting groups)
    • State-of-the-art layout suite: All major cutting-edge layouts, such as hierarchical, layered, orthogonal layouts supported out of the box.
    • GPU rendering: Traditional SVG handles ~1,000 nodes before lag; Our layout engine renders 10,000+ nodes at 60 FPS (smooth interactivity). Example: Telecommunications company visualized 15,000 cell towers with 80,000 connections in one interactive view
    • Edge bundling for clarity in dense networks
    • Lasso selection and multi-select

Real-world impact: Bank visualized 50,000 accounts, 200,000 transactions using ForceAtlas2 - fraud rings appeared as isolated clusters, "money mule" accounts bridging legitimate/fraudulent clusters were instantly visible. Result: Identified $2.3M fraud network in 30 minutes vs. 2 weeks with SQL queries.

2. Matrix Visualization

  • Use Case: When node-link becomes an unreadable "hairball" (dense networks with high edge-to-node ratio)
  • Features:
    • Canvas-based matrix rendering with zoom/pan
    • Reordering algorithms reveal patterns:
      • Spectral ordering: Uses graph theory to cluster related nodes - communities appear as dense diagonal blocks
      • Degree-based: Sorts by connection count - hub nodes cluster at top-left corner
      • Barycentric: Minimizes edge crossings - bipartite patterns become organized rectangles
    • Weighted edge support with visual encoding
    • Interactive cell selection and filtering

Real-world impact: Insurance company analyzing 50,000 claims with 200,000 relationships - node-link view was unreadable hairball, matrix view with spectral ordering revealed 23 distinct fraud rings as dark diagonal blocks. Result: $18M in fraudulent claims identified (15% of annual fraud losses).

3. Arc Plot

  • Use Case: Temporal relationships, sequential patterns, ordered data
  • Features:
    • D3.js-powered arc rendering
    • Node ordering optimization
    • Edge attribute encoding (color, thickness)

4. Sankey Diagram

  • Use Case: Flow analysis, resource allocation, pathway visualization
  • Real-World Applications:
    • Supply chain: Manufacturing company tracked $500M material flow (raw materials → components → products), identified bottleneck: 60% production dependent on single supplier
    • Customer journey: SaaS company visualized Landing page → Trial → Usage → Conversion, found 40% drop-off at first use → implemented onboarding tutorial, increased conversion 25%
    • Budget allocation: Non-profit tracked $10M budget flow through programs, found admin consuming 18% (target: 12%) → restructured, reduced to 13%
  • Features:
    • Adjustable vertical spacing and label positioning
    • Multi-path flow tracking
    • Edge attribute visualization (width = flow volume, color = flow type)

5. Semantic Substrates

  • Use Case: Layered network analysis, multi-dimensional relationships
  • Features:
    • Configurable substrate layouts
    • Cross-layer edge rendering
    • Interactive filtering and selection

6. PAOH (Parallel Aggregated Ordered Hypergraph)

  • Use Case: Standard graphs show pairwise relationships (A-B), but real-world data often has multi-way relationships (Patient-Drug-Dosage-Outcome-Doctor-Date = 6 dimensions). For example, pharmaceutical research analyzing 10,000 patient clinical trial records
    • Traditional approach: Creates 50,000+ nodes (patients + drugs + doctors + intermediate nodes) → completely unreadable
    • PAOH approach: Each row = patient, columns = Drug | Dosage | Duration | Outcome | Side Effects. Color-coding reveals: "Patients on Drug X at high dosage with Doctor Y had 40% adverse events"
    • Result: Identified optimal dosage ranges 10x faster than SQL queries**
  • Features:
    • Attribute-based filtering with custom predicates
    • Transpose view for alternate perspectives (flip: each row = Drug, see which patient demographics respond best)
    • Hyperlink rendering with aggregation

Geospatial Visualizations

7. Map Visualization

  • Layers:
    • Node-Link Layer: Network overlay on geographic maps
    • Choropleth Layer: Regional data visualization with color encoding
    • Heatmap Layer: Density visualization for point data
  • Features:
    • Lasso selection on map
    • External selection coordination
    • Coordinate lookup and geocoding
    • Custom shape factories for node types

Traditional Analytics

8. 1D Visualizations

  • Histogram (Count/Degree): Distribution analysis
  • Line Chart: Temporal trends
  • Scatter Plot: Correlation analysis
  • Pie Chart: Proportion visualization

9. Table View

  • Use Case: Detailed data inspection, export preparation
  • Features:
    • Sortable columns
    • Multi-table joins
    • CSV/Excel export

10. Raw JSON View

  • Use Case: Technical debugging, API integration
  • Features:
    • Formatted JSON display
    • Selection data inspection

Cross-Visualization Features

  • Linked Interactions: Selections sync across multiple visualizations
  • Export Capabilities: High-resolution image generation for presentations
  • Theme Support: Light/dark mode with automatic contrast adjustment
  • Responsive Design: Adapts to different screen sizes and resolutions

🤖 AI-Powered Query Building

Mode 1: Natural Language Interface (Text-to-Query)

Users can build queries through whichever interface matches their expertise level:

  • Conversational Query Generation: Ask questions in plain English, get optimized graph queries
  • Context-Aware: Understands schema structure and suggests relevant patterns
  • Streaming Responses: Real-time query generation with visible AI reasoning
  • Multiple LLM Support: OpenAI GPT, Ollama, with model selection

Mode 2: Visual Query Builder

The intuitive Lego-like query builder allows visual query building with advanced GPQL-powered syntax checking:

  • Drag-and-Drop Pills: Entity, Relation, and Logic components for visual query composition
  • Related Nodes Panel: Interactive exploration of graph neighborhood
  • Real-Time Preview: Live query results update as you build
  • Query Logic Context Menu: Quick access to common graph patterns

Mode 3: Direct Cypher Input

For specific queries analysts can copy-paste or write directly in Cypher

  • Cypher-to-Text: AI explains existing queries in natural language
  • Query Optimization: Automatic performance tuning suggestions
  • Syntax Validation: Real-time error detection and correction

Machine Learning & Graph Algorithms

Advanced AI Assistant Features:

  • Chat History: Maintains conversation context across multiple queries
  • Query Context Selection: Include previous query results in new questions
  • Feedback Loop: Like/dislike system improves AI recommendations
  • Edit & Rerun: Modify AI-generated queries and see updated results
  • Computation Time Tracking: Transparency into query performance

🧠 Integrated ML Services

GraphPolaris includes microservice-based ML services for advanced analytics:

Graph Algorithm Suite:

  1. Centrality Analysis - Finding Influential Nodes
    • Degree centrality: Consumer brand identified top 100 Instagram influencers with 2M combined followers → $500K campaign reached 2M people, 4x better ROI than traditional ads
    • Betweenness centrality: Logistics company found Chicago distribution center routing 40% of all shipments ( bottleneck) → opened secondary hub → 2-day faster delivery, +15% customer satisfaction
    • Closeness centrality: City optimized ambulance station placement → average response time decreased from 8.5 to 6.2 minutes, estimated 15 lives saved annually
    • Eigenvector centrality: B2B SaaS targeting well-connected companies → 40% higher close rate vs. random outreach
  2. Community Detection - Finding Clusters
    • Louvain method: E-commerce analyzed 500K customers, detected 47 lifestyle communities (outdoor enthusiasts, tech adopters, home decor, fitness) → personalized marketing → email click-through +35%, conversion +22%
    • Telecommunications: Telco analyzed call patterns, communities geographically clustered → optimized cell tower placement → call quality improved 12%
  3. Link Prediction - Guessing Missing Connections
    • Common Neighbors: LinkedIn "People you may know" → 40% acceptance rate (industry benchmark: 20%)
    • Jaccard Coefficient: B2B partnership prediction, normalized for company size → 20% better accuracy than unnormalized approaches
    • Adamic-Adar: Banking fraud detection highlighting rare shared connections → 85% fraud detection rate, 10% false positive rate
  4. Shortest Path - Optimized Routing
    • Dijkstra/A algorithms*: Logistics routing with constraints (avoid tolls, prefer highways) → real-time route optimization instead of overnight batch processing
    • Multi-path discovery for redundancy planning

ML Integration Features:

  • Query-Embedded Algorithms: Run ML directly within query workflow
  • ML Dialog: Visual interface for algorithm parameter tuning
  • Result Augmentation: ML outputs seamlessly integrated into visualizations
  • Validation Framework: Comprehensive data format analysis and schema validation

Business Impact: Delivers sophisticated network science capabilities without requiring data science expertise, enabling discovery of patterns invisible to traditional analytics.

Collaboration & Workflow Features

Multi-User Collaboration

  • Save States: Persistent analytical sessions across users and time, share states with different analysts and users
  • Query Sharing: Share query definitions and results with colleagues
  • Version Control: Track changes to schemas and queries
  • Export Formats: PNG, SVG, CSV, Excel for presentations and reports

Enterprise Features

  • Authentication: Better Auth integration with enterprise SSO support
  • License Management: Usage tracking and access control
  • CORS Configuration: Secure cross-origin API access

Developer Integration

  • REST API: Programmatic access to all platform capabilities
  • WebSocket Support: Real-time updates and streaming responses
  • Call ID Tracking: Request tracing for debugging and auditing
  • Kubernetes Deployment: Enterprise-scale orchestration support

Business Impact: Scales from individual analysts to enterprise-wide deployment while maintaining security and governance requirements.

🔗 Infrastructure

Architecture Highlights

  • Microservice Architecture: Independent scaling of compute-intensive operations
  • Message Queue: RabbitMQ for reliable inter-service communication
  • Caching: Redis for session management and query result acceleration
  • Container Orchestration: Kubernetes with Istio service mesh

Deployment Options

  • Cloud-Native: AWS, Azure, GCP support
  • Air-Gapped: Secure deployment for sensitive environments
  • Docker Compose: Simplified local and staging deployments
  • From-Staging/From-Local: Flexible development workflows

Operational Features:

  • Health Checks: Automated service monitoring
  • Connection Testing: Database availability verification
  • Retry Logic: Resilient service communication
  • Drizzle Migrations: Database schema evolution without downtime

Performance Optimization Engine

GraphPolaris's competitive advantage lies in its query optimization technology:

Optimization Capabilities:

  • Cypher-to-Visual Conversion: Analyzes query structure for visual representation
  • Query-to-Graph Logic: Optimizes cross-logic operations
  • Synthetic Attribute Handling: Efficient edge counting and aggregation
  • Parallel Execution: Multi-threaded processing for complex traversals

Performance Metrics - Concrete Examples:

100x Improvement - Multi-Hop Product Recommendation:

  • Scenario: E-commerce query "Customers who bought A and B, plus their friends who haven't bought either"
  • Naive Cypher: 47 seconds (separate MATCH clauses, huge intermediate results)
  • GraphPolaris optimized: 0.4 seconds (combined MATCH, early filtering, WITH clause aggregation)
  • Result: 117x faster → enabled real-time product recommendations on website, +18% cross-sell revenue

127x Improvement - Shortest Path with Constraints:

  • Scenario: Logistics routing "City A to City B, avoid tolls, prefer highways"
  • Naive shortestPath(): 23 seconds (explores all paths, then filters = wasted work)
  • GraphPolaris A* with heuristics: 0.18 seconds (filters during search, geographic heuristic guides toward goal)
  • Result: 127x faster → delivery planners try multiple scenarios during customer calls instead of overnight batch

1000x Improvement - Graph Algorithms:

  • Scenario: PageRank on 100,000 nodes
  • Pure Cypher iterative approach: 40+ minutes (often doesn't complete - graph DBs optimized for traversal, not iterative computation)
  • GraphPolaris ML Service (NetworkX): 2.4 seconds (optimized C/C++ libraries, adjacency matrices, parallel processing)
  • Result: 1000x faster → influence analysis that ran overnight now runs in real-time, marketing team tests scenarios interactively
  • Sub-second response for interactive operations
  • Real-time query abort capability for long-running operations

Monitoring & Control:

  • Progress Tracking: Skeleton loading states and progress bars
  • Query Abort: Stop long-running queries mid-execution
  • Computation Time Display: Transparency into performance characteristics
  • Auto-Run Configuration: Smart query execution based on complexity

Business Impact: Enables real-time analytics during meetings, replacing the traditional wait-for-results workflow that takes days or weeks.

Current Development Cycle

🚧 Visual Schema Design (GraphPolaris Designer)

GraphMaker Capabilities: The Designer enables users to visually map tabular data into graph structures without writing code

Visual Node/Relationship Mapping - Drag-and-drop interface for defining entities and connections

Composite Index Creation - Automatic optimization for multi-attribute matching (e.g., composite index on country + zip_code speeds up geographic queries 50x)

One Click SQL to Knowledge Graphs - Convert your SQL graph automatically with one click.

Knowledge Graphs on-the-fly sources editing - Add arbitrary file sources (csv, excel, parquet) to boost your knowledge graph source iteratively.

Self-Relation Support - Real-World Applications:

  • Organizational hierarchies: Employee nodes with REPORTS_TO relationships enable unlimited-depth org chart queries and management layer analysis
  • Supply Chain networks: Component nodes with CONTAINS relationships power Bill of Materials (BOM) explosion queries and impact analysis ("If microchip X fails, which products are affected?")
  • Knowledge graphs: Document nodes with REFERENCES relationships enable citation network analysis and conceptual clustering

ML-Powered Recommendations - What AI can suggest: Based on analyzing 10,000+ data modeling projects:

  • Relationship suggestions: "Your Customer table has order_id field. Should this connect to an Order node?"
  • Index recommendations: "Customer lookups by email are frequent. Create unique index on email property? (50x speedup)"
  • Normalization hints: "Address field appears in 3 tables. Extract to separate Address nodes to avoid duplication?"

Importer Features:

  • ETL Pipeline Builder: Visual workflow for data transformation and loading
  • Multi-Format Support: Unified handling of CSV, Excel, Parquet with encoding detection
  • Progress Tracking: Real-time visibility into data import status
  • Error Recovery: Intelligent handling of malformed data and file URIs

Business Impact: Reduces graph schema design from weeks to hours, enabling rapid experimentation with different data models.

🎯 Core Platform Enhancements

  • Graph Database Designer: Intuitive visual interface for complex graph queries
  • Performance Optimization: Enhanced processing speed for large datasets

📊 Visualization Improvements

  • Interactive Graph Layouts: New force-directed and better hierarchical layouts
  • Custom Color Schemes: User-defined color palettes for better data representation
  • Export Capabilities: High-resolution exports for presentations and reports

🔧 Developer Experience

  • API Documentation: Comprehensive REST API documentation,
  • SDK Development: Python and JavaScript SDKs for easier integration

Next Development Cycles

🤖 AI-Powered Features

  • Smart Pattern Recognition: Automatic detection of interesting graph patterns to get to results even faster and more reliably
  • Visualization Story Telling: Automatic describe interesting graph patterns within the visualization in context.
  • Intelligent Visualization Recommendations: AI-suggested Visualizations (Show Me button)

🔗 Enhanced Integrations

  • Database Connectors: Direct connections to popular databases; SQL-enhanced platforms such as DataBricks
  • Cloud Platform Integration: Seamless AWS, Azure, and GCP integration
  • Collaboration Tools: Integration with Slack, Teams, and project management tools

🛡️ Enhanced Security & Compliance

  • Audit Logging: Comprehensive activity tracking
  • GDPR Compliance Tools: Data privacy and compliance features

Future Vision

🌐 Exploration Capabilities

  • Keep Track of Knowledge Graph Schema Changes: Changes to the underlying data source (e.g., due to policy changes; e.g., new tariffs) will change the outcome of existing analysis and or render them obsolete and to-be-reviewed.
  • Better Schema Visualization with automated Analytics: Better Schema Visualizations will allow to track dead spots, missing data, and contradictory data.
  • Full support for Bring-your-own-Algorithm: Enterprises are designing sophisticated analytics ML. We will support these algorithms natively in GP. Examples: Advanced path analytics for supply chains, Temporal Graph analytics for evolving social networks in intelligence

🌐 Enterprise Features

  • Multi-tenant Architecture: Support for large enterprise deployments
  • Advanced Role Management: Granular permissions and access controls
  • Custom Branding: White-label solutions for enterprise clients
  • Full Data and Analytics Provenance: Companies will be able to trace every data insight down to the database/file entry that produced the result, visualized in an intuitive and audit ready fashion.
  • Full Write-back support: Store your data transformations or analytics results (e.g., classifications) directly in a (graph) database or file where they belong for future use.

🔬 Research & Innovation

  • Graph Machine Learning: Built-in ML algorithms for graph data running in existing graph DB architectures (e.g., Neo4j's Graph Analytics )
  • Graph Embeddings: Compute, visualize and explore graph node/relation embeddings even for multivariate knowledge graphs.
  • Real-time Analytics: Live data streaming and analysis to multiple users in real-time (Google Docs-like UX)
  • Improved Data Engineer and Analyst Collaboration: Notify Data Engineers for missing data (suggest data engineer follow-up tasks) and notify Data Analysis for newly available data sparking new exploration paths.
  • Autosuggest Meaningful Pattern Analytics: Uses AI to screen for next-best-action query. Allows improving time-to-first-insight with minimal user input .

🎨 User Experience

  • Redesigned Interface: Modern, intuitive user experience
  • Accessibility Improvements: Enhanced support for users with disabilities
  • Customizable Dashboards: Personalized workspaces for different use cases
  • Human-in-the-Loop Entity resolution: Steer entity resolution algorithms with human feedback
  • Support for different screen sizes: Run GraphPolaris on Large and Small-screen displays
  • Central Query and Insight Repositories: Share and track the most successful and queries and analysis in your entire company.
  • Bias Detection and Mitigation: Automatically track and suggest unexplored analysis paths to mitigate confirmation and availability biases.

Community & Feedback

We value community input in shaping our roadmap. Features are prioritized based on:

  • User Feedback: Direct input from our user community
  • Market Research: Industry trends and competitive analysis
  • Technical Feasibility: Development complexity and resource requirements
  • Business Impact: Value delivered to our users and customers
Want to influence our roadmap? Join our community discussions, submit feature requests, or contact our product team. Your feedback drives our development priorities.

Last updated: October 2025

This roadmap is subject to change based on market conditions, technical constraints, and user feedback. Features may be moved between quarters or modified during development.