Inputless Analytics SDK
Drop context into the systems you already run. No schema rewrite. No rigid setup.
No schema pass first
The Inputless Analytics SDK adds context to the systems you already run. Point it at databases, logs, sensors, and unstructured files. It builds a map of how those pieces connect.
You do not define a rigid input schema first. The SDK infers links as data arrives. No curated warehouse required.
What ships in the packages
Less time joining tables. More time deciding.
Map the links
Documents, sensors, logs: the SDK builds the edges. You do not draw the schema first.
Find the gap
Cross-reference logs, docs, and sensors. Surface links people miss and holes between systems.
A forecast, with history
Regression and pattern mining on the stream you already have. A number you can check, not a vision.
Watch as it happens
Alerts and anomalies on the live event, not on last night’s batch.
BI & Dashboard Integrations
Plug into the BI tools you already use. Push findings to the screens people already open.
Meaning from the links
You do not wait for a clean warehouse. The SDK infers links and says what they are for.
Why It's Different
Zero Schema Required
Work without defining rigid input structures. Makes adoption fast and flexible.
Context Over Input
You do not wait for a clean dataset. The SDK builds the links and tells you what they mean.
Less glue work
Cuts the hours spent joining data so people can decide and act.
Use Cases
Real-world applications across industries and operational domains.
Ops picture
Sensors, logs, reports in one map. Failures and waste show up as a path, not as three tickets.
A trail for the auditor
Finance, health, industrial: link records to the rule, keep the trail, cut the manual pack.
Look-ahead
Demand, parts, and disruption from history plus the live feed. A planning input, not a crystal ball.
SDK Packages
Modular packages for TypeScript and Python. Install only what you need.
@inputless/trackerTypeScriptEvent collection and tracking
npm install @inputless/trackerDownload ZIP@inputless/sdk-cognitiveTypeScriptOn-device intelligence processing
npm install @inputless/sdk-cognitiveDownload ZIP@inputless/contextTypeScriptPattern detection and context awareness
npm install @inputless/contextDownload ZIP@inputless/dispatcherTypeScriptSignal routing and channel management
npm install @inputless/dispatcherDownload ZIP@inputless/storageTypeScriptLocal storage and caching
npm install @inputless/storageDownload ZIP@inputless/visualizationTypeScriptGraph visualization components
npm install @inputless/visualizationDownload ZIPinputless-enginesPythonMutation & reasoning engines
pip install inputless-enginesDownload ZIPinputless-modelsPythonPattern recognition models
pip install inputless-modelsDownload ZIPinputless-graphPythonInputless DB integration layer
pip install inputless-graphDownload ZIPinputless-ingestionPythonDocument processing pipeline
pip install inputless-ingestionDownload ZIPTechnical 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
- Proprietary 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
Sign Up & Get Credentials
Sign up for access and obtain your API key and credentials. Contact us to get started with a demo deployment.
Install the SDK
Install the SDK in your environment. Choose from TypeScript packages (npm) or Python packages (pip) based on your stack.
Connect Data Sources
Point the SDK at your existing data sources - databases, logs, sensor pipelines, document stores. No schema changes required.
Start Querying
Begin querying for context, setting up monitoring, and exposing outputs to your dashboards. Use Proprietary Graph RAG for natural language queries.
Scale Incrementally
Scale usage incrementally from pilot projects to organization-wide deployment. The system adapts as your needs grow.
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. Structured records, documents, logs, and sensor feeds. Databases, lakes, and live APIs all work.
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. Plug into the BI tools you already use. Findings go to the screens people already open. No new stack required.
Need an API key?
Wire the SDK into the stack you already run. No schema rewrite.