Overview
KreateDataProduct is a platform that transforms raw, heterogeneous data into higher-level, consumable data products (“chocolate bars of data”).
These data products are structured, enriched, and insight-ready, designed to be directly useful for humans, AI models, and business processes. Instead of millions of raw signals, the platform creates interpretable, actionable indices and scores — bridging the gap between raw data and strategic decisions.
Core Capabilities
3.1 Dataset Creation
Go beyond raw data collection. Intelligently construct high-quality datasets for AI and analytics.
- Gold Dataset Construction: Curated, high-quality labeled data.
- Active Learning: Intelligent sampling for labeling efficiency.
- Weak Supervision: Programmatic labeling at scale.
- Optimal Transport: Adapt data distributions across domains.
- Synthetic Data: GANs & GenAI for augmentation and gap-filling.
3.2 Data Product Construction (“Chocolate Bars”)
Transforms raw signals into high-value, human/AI-ready outputs:
IoT Example (Data Center):
SwitchGear voltage & current → Health Score + Remaining Useful Life (RUL)
Finance Example:
Multi-quarter EPS & sentiment → Earnings Momentum Index
Customer Example:
Call center transcripts → De Complaint Clusters + Regulatory Risk Score
3.3 Lineage & Provenance (Key Differentiator)
Full lineage tracking across all data transformations ensures trust, auditability, and reproducibility.
- Source Tracking: Know if data was produced from raw signals, prompts & GenAI, optimal transport, or feature engineering.
- Graph-based Lineage: Visualize how higher-level data products derive from lower-level signals.
3.4 Monitoring & Quality
Continuous monitoring of data pipelines and data products.
- Data Quality Metrics: Track freshness, completeness, accuracy, and detect anomalies.
- Feedback Loops: Consumption metrics flow back to curation to refine products.
3.5 Vector DB & AI Integration
API-first design for easy use in ML pipelines.
- Native Integration: Connects with vector databases like ChromaDB, Pinecone, and Weaviate.
- AI-Ready: Supports semantic search, retrieval-augmented generation (RAG), and embedding-based enrichment.
3.6 Collaboration Features
A multi-user environment for teams of data scientists, engineers, and analysts.
- Shared Workspaces: Co-create data products in a shared environment.
- Governance: Includes versioning, access control, and role-based collaboration.
Our Differentiators
- Chocolate Bar Concept: Moves beyond raw data to interpretable, consumable products.
- Lineage-first: Graph-based tracking of how every data product is created.
- Enterprise-grade Monitoring: Continuous quality and anomaly detection.
- Cross-Domain Adaptability: Optimal transport to reuse data across industries.
- Seamless AI Integration: Native vector DB connectors for AI-first workflows.
- Team Collaboration: Built-in co-creation and governance features.
Example Use Cases
- Predictive Maintenance (IoT) → Health scores, RUL predictions.
- Financial Analytics → Earnings momentum index, sentiment-based insights.
- Customer Experience → Complaint detection, regulatory risk monitoring.
- Compliance & Risk → Early warning systems for regulatory mentions.
- AI Training Data → Gold datasets with full lineage.