AI Data Engineering & Knowledge Systems

We apply automated techniques to:

• Standardize attributes and formats
• Resolve conflicts across multiple data sources
• Extract hidden signals from free-text fields
• Derive missing attributes using pattern-based and AI-driven methods
• Improve data completeness and consistency

This significantly increases downstream AI accuracy.

To unlock meaning from unstructured text, we use hybrid extraction techniques, including:

• Rule-based pattern recognition
• Transformer-based language models
• Context-aware semantic parsing

This enables accurate extraction of functional, descriptive, and contextual attributes from raw text.

We design systems that understand semantic similarity, not just exact matches.

Our capabilities include:

• Embedding generation for text and attributes
• Approximate nearest neighbor search
• Similarity detection using cosine distance
• Persist similarity results to avoid repeated computation
• Large-scale deduplication and clustering

These systems power recommendations, matching, and discovery use cases.

We build graph-based knowledge systems to model relationships across data entities.

Using graph databases and modeling techniques, we enable:

• Relationship discovery
• Dependency analysis
• Contextual traversal
• Explainable AI insights

Graphs make complex data navigable and understandable.

We design storage architectures optimized for:

• High-volume analytical queries
• Real-time similarity search
• Large-scale graph traversal
• AI-driven workloads

 This includes columnar stores, graph databases, and hybrid architectures optimized for performance and scale.

For production-grade AI systems, we build:

• Asynchronous, high-throughput inference pipelines
• Model optimization using ONNX
• Scalable batch and real-time processing
• Cost-efficient AI execution at scale

This ensures AI systems remain fast, reliable, and affordable in production.