Your AI is only as good as your data pipeline. We build production data pipelines that ingest, transform, embed, and deliver data to your AI systems, with ETL automation, embedding generation, vector store loading, real-time streaming, and ML feature engineering.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
Automated data pipelines with Apache Airflow, Prefect, or Dagster. Extract from databases, APIs, files, and SaaS platforms. Transform with dbt, Pandas, or Spark. Load into warehouses, lakes, or AI systems.
Generate embeddings from documents, images, and audio. Chunk strategies optimized for retrieval. Incremental updates to Pinecone, Weaviate, Chroma, pgvector, or Qdrant. The backbone of every RAG system.
Kafka, Redis Streams, and event-driven architectures for real-time data processing. Live RAG updates, streaming analytics, and sub-second data delivery for time-critical AI applications.
Feature stores, feature computation pipelines, and online/offline feature serving. Time-series features, aggregations, and derived features that feed ML models with fresh, consistent data.
Schema validation, anomaly detection, completeness checks, and drift monitoring at every pipeline stage. Great Expectations, custom validators, and alerting for data quality incidents.
PDF extraction, image OCR, audio transcription, video processing, and web scraping pipelines. Convert unstructured sources into structured, AI-ready data with metadata and lineage tracking.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
Automated data pipelines with Apache Airflow, Prefect, or Dagster. Extract from databases, APIs, files, and SaaS platforms. Transform with dbt, Pandas, or Spark. Load into warehouses, lakes, or AI systems.
Generate embeddings from documents, images, and audio. Chunk strategies optimized for retrieval. Incremental updates to Pinecone, Weaviate, Chroma, pgvector, or Qdrant. The backbone of every RAG system.
Kafka, Redis Streams, and event-driven architectures for real-time data processing. Live RAG updates, streaming analytics, and sub-second data delivery for time-critical AI applications.
Feature stores, feature computation pipelines, and online/offline feature serving. Time-series features, aggregations, and derived features that feed ML models with fresh, consistent data.
FAQ
Tell us about your data sources and AI requirements — we'll design a pipeline architecture that delivers clean, fresh data to your AI systems reliably.