Your AI is only as good as your data. We build modern data lakehouses, real-time pipelines, vector databases, and AI-powered analytics that transform messy enterprise data into a strategic AI advantage.
5x
Faster pipeline perf.
40%
Lower data infra cost
Real-time
Streaming insights
Data Platform Maturity Score
Design and build modern data lakehouses on Delta Lake, Apache Iceberg, or Apache Hudi — combining cheap storage with warehouse-grade performance.
✓ 40% lower data costEvent-driven data architectures using Kafka and Flink — enabling real-time AI inference, fraud detection, and live dashboards from streaming data.
✓ Sub-second latencySelf-service analytics platforms with natural language querying, AI-generated insights, automated anomaly detection, and executive-ready dashboards.
✓ 70% less reporting timeCentralized feature engineering, storage, and serving infrastructure that reduces feature duplication across ML teams and ensures training/serving consistency.
✓ 60% faster feature devVector storage and retrieval infrastructure for RAG pipelines, semantic search, recommendation engines, and multimodal AI applications.
✓ 10x faster semantic searchAutomated data quality testing, schema validation, freshness monitoring, and data lineage tracking to ensure your AI systems are built on trusted data.
✓ 95% data quality scoreDatabricks
Lakehouse
Snowflake
Data Cloud
BigQuery
Analytics DWH
Apache Spark
Processing
dbt
Transformation
Apache Kafka
Streaming
Airflow
Orchestration
Pinecone
Vector DB
Weaviate
Vector DB
Delta Lake
Storage Format
Great Expectations
Data Quality
dbt Cloud
Analytics Eng
A data lakehouse combines the cost-effective storage of a data lake with the performance and ACID guarantees of a data warehouse. It's the foundation architecture for enterprises running AI at scale. If you're spending >$100K/year on separate data warehouse and data lake infrastructure, or struggling to serve both ML and BI workloads from the same data, a lakehouse migration typically delivers 40-60% cost reduction and 3-5x query performance improvement.
A vector database stores high-dimensional embeddings and enables semantic similarity search — essential for RAG pipelines, semantic search, recommendation systems, and multimodal AI. If you're building any LLM application that needs to retrieve relevant context from a knowledge base, you need vector search. We work with Pinecone, Weaviate, Qdrant, pgvector, and Chroma depending on your scale and latency requirements.
We implement enterprise-grade data governance using Unity Catalog (Databricks), Collibra, or Apache Atlas — providing column-level security, row-level filters, data lineage tracking, and automated PII detection. For GDPR, HIPAA, and SOC 2 compliance, we implement right-to-erasure workflows, audit logging, and data residency controls.
Free data platform assessment — we'll evaluate your current architecture and identify the fastest path to AI-readiness.
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