Developer Tools

Inputless Analytics SDK

Integrate advanced context-aware intelligence into existing systems—no schema changes, no rigid setups required.

Zero Schema Required

The Inputless Analytics SDK empowers developers and organizations to integrate advanced context-aware intelligence into existing systems. By working seamlessly across databases, logs, sensors, and unstructured data, the SDK turns raw inputs into a rich map of relationships and insights.

Work without defining rigid input structures. Makes adoption fast and flexible. The SDK builds relational context automatically to surface meaning—no carefully curated data required.

11 NPM PACKAGES
TypeScript SDK
4 PYTHON PACKAGES
Backend Engines
MULTI-SOURCE
Data Integration
Capabilities

Key Capabilities

Powerful features that amplify human intelligence and reduce time spent stitching data together.

Contextual Mapping

Automatically builds relational maps between data points—documents, sensor reads, logs—without manual modeling.

Automatic Linking & Discovery

Seamlessly cross-reference and connect information from disparate sources to reveal hidden associations and operational blind spots.

Predictive Modeling

Use statistical methods like regression models and historical pattern mining to forecast outcomes and anticipate issues.

Real-Time Monitoring

Track events continuously; trigger alerts, detect anomalies, or highlight critical shifts in behavior as they happen.

BI & Dashboard Integrations

Plug into your existing Business Intelligence tools and visualization pipelines. Surface insights where your team already works.

Native Insight via Context

Instead of waiting for carefully curated data, the SDK builds relational context automatically to surface meaning.

Why It's Different

Zero Schema Required

Work without defining rigid input structures. Makes adoption fast and flexible.

Context Over Input

Instead of waiting for carefully curated data, the SDK builds relational context automatically to surface meaning.

Amplifies Human Intelligence

Reduces time spent stitching data together, letting teams focus on decision-making and action.

Applications

Use Cases

Real-world applications across industries and operational domains.

Operational Intelligence

Gain visibility across sensor networks, logs, and reports to preempt failures, optimize workflows, or spot systemic inefficiencies.

Compliance & Traceability

For regulated industries—finance, healthcare, industrial—automatically link data to compliance rules, produce audit trails, and reduce manual governance overhead.

Strategic Forecasting

Predict trends, resource needs, or potential disruptions using historical behavior and real-time indicators.

Installation

SDK Packages

Modular packages for TypeScript and Python. Install only what you need.

@inputless/trackerTypeScript

Event collection and tracking

npm install @inputless/trackerDownload ZIP
Also available on:GitHubnpm
@inputless/sdk-cognitiveTypeScript

On-device intelligence processing

npm install @inputless/sdk-cognitiveDownload ZIP
Also available on:GitHubnpm
@inputless/contextTypeScript

Pattern detection and context awareness

npm install @inputless/contextDownload ZIP
Also available on:GitHubnpm
@inputless/dispatcherTypeScript

Signal routing and channel management

npm install @inputless/dispatcherDownload ZIP
Also available on:GitHubnpm
@inputless/storageTypeScript

Local storage and caching

npm install @inputless/storageDownload ZIP
Also available on:GitHubnpm
@inputless/visualizationTypeScript

Graph visualization components

npm install @inputless/visualizationDownload ZIP
Also available on:GitHubnpm
inputless-enginesPython

Mutation & reasoning engines

pip install inputless-enginesDownload ZIP
Also available on:GitHubPyPI
inputless-modelsPython

Pattern recognition models

pip install inputless-modelsDownload ZIP
Also available on:GitHubPyPI
inputless-graphPython

Inputless DB integration layer

pip install inputless-graphDownload ZIP
Also available on:GitHubPyPI
inputless-ingestionPython

Document processing pipeline

pip install inputless-ingestionDownload ZIP
Also available on:GitHubPyPI

Technical Specifications

Data Sources Supported

  • Documents and unstructured text
  • Sensor feeds and IoT devices
  • Log files and event streams
  • Structured databases (PostgreSQL, MySQL, etc.)
  • Semi-structured data (JSON, XML)

Output Types

  • Context maps and knowledge graphs
  • Predictive forecasts and trends
  • Real-time alerts and anomalies
  • BI dashboard integrations
  • Graph RAG query responses

Latency & Performance

Designed for near real-time performance to support operational monitoring and decision-making. Event processing with <100ms latency.

Privacy & Compliance

Tracks relationships without requiring full personally identifying schemas. Supports traceability for audits and compliance requirements.

Getting Started

01

Sign Up & Get Credentials

Sign up for access and obtain your API key and credentials. Contact us to get started with a demo deployment.

02

Install the SDK

Install the SDK in your environment. Choose from TypeScript packages (npm) or Python packages (pip) based on your stack.

03

Connect Data Sources

Point the SDK at your existing data sources—databases, logs, sensor pipelines, document stores. No schema changes required.

04

Start Querying

Begin querying for context, setting up monitoring, and exposing outputs to your dashboards. Use Graph RAG for natural language queries.

05

Scale Incrementally

Scale usage incrementally from pilot projects to organization-wide deployment. The system adapts as your needs grow.

Support

Frequently Asked Questions

Do I need to predefine data schemas or map relationships manually?

No—Inputless Analytics is designed to infer context and connections automatically. The SDK builds relational maps between data points without requiring manual modeling or schema definitions.

Is this solution applicable to both structured and unstructured data?

Yes—the SDK can process data in many formats, linking across structured records, documents, logs, and sensor feeds. It works seamlessly with databases, data lakes, and real-time API platforms.

How does predictive modeling work?

Using statistical tools like regressions and pattern-based forecasting to anticipate outcomes based on historical trends. The system mines patterns from historical behavior and real-time indicators to forecast trends, resource needs, and potential disruptions.

What about privacy and compliance?

The SDK supports traceability, audit-friendly data linking, and can operate without exposing personal data by inferring relationships without explicit personal identifiers. It tracks relationships without requiring full personally identifying schemas.

Can I integrate with existing BI tools?

Yes—the SDK plugs into your existing Business Intelligence tools and visualization pipelines. Surface insights where your team already works without requiring new infrastructure.

Ready to Get Started?

Integrate context-aware intelligence into your systems today. No schema changes required.