Data Engineering for theAI era

We design and operate cloud-native Lakehouse platforms, real-time streaming pipelines and AI-ready data products on Snowflake, Databricks, BigQuery, Kafka and Spark — so every team in your business runs on trusted, governed and fresh data.

INGEST · STREAM · INTEGRATETRANSFORM · MODEL · ENRICHGOVERN · SERVE · OBSERVEReal-TimeStreamingAI-ReadyPipelinesLakehouseStorageDataEngineering
100+
Data pipelines shipped to production
10TB+
Daily throughput across client platforms
99.9%
Average pipeline SLA delivered
60%
Average cost reduction on cloud spend

What we build

End-to-end data platforms — from ingestion to AI-ready serving — engineered for scale, reliability and developer joy.

Lakehouse Architecture

Unified analytics on Databricks, Snowflake, BigQuery and Redshift with open table formats — Delta Lake, Apache Iceberg and Hudi.

Real-Time Streaming

Sub-second event pipelines with Apache Kafka, Flink, Pulsar, Kinesis and Spark Structured Streaming for live decisioning.

ELT & Data Modeling

Production-grade transformations with dbt, SQLMesh and Spark — version-controlled, tested and documented as code.

Orchestration & Observability

Reliable pipelines with Airflow, Dagster and Prefect, monitored via Monte Carlo, OpenLineage and Datadog.

Data Governance & Catalog

Unity Catalog, Atlan, Collibra and OpenMetadata for lineage, access control, privacy and AI-ready data products.

Vector & Feature Stores

Embedding pipelines into Pinecone, Weaviate, pgvector and Feast — powering RAG, semantic search and ML in production.

The iMentus data pipeline

A reference architecture we've battle-tested with enterprise clients — from raw events to AI-grade datasets.

STEP 01
Ingest

CDC from OLTP with Debezium & Fivetran, event streams via Kafka, SaaS connectors via Airbyte.

STEP 02
Store

Cloud lakehouse on S3/ADLS/GCS with Delta, Iceberg or Hudi for ACID & time travel.

STEP 03
Transform

Medallion (bronze/silver/gold) modeling with dbt and Spark — tested & versioned in Git.

STEP 04
Serve

Reverse ETL with Hightouch & Census, semantic layer with Cube, BI on Looker, Tableau & Power BI.

STEP 05
Govern

Lineage, observability, masking, PII detection and FinOps alerts on every table and pipeline.

Our modern data stack

We pick the right tool for the job — open standards first, vendor lock-in last.

Lakehouse
DatabricksSnowflakeBigQueryRedshiftMicrosoft Fabric
Storage
Delta LakeApache IcebergApache HudiS3ADLS Gen2
Streaming
KafkaFlinkPulsarKinesisSpark Streaming
Orchestration
AirflowDagsterPrefectArgoAWS Step Functions
Transform
dbtSQLMeshSparkPolarsDuckDB
Governance
Unity CatalogOpenLineageAtlanCollibraMonte Carlo
Vector
PineconeWeaviatepgvectorMilvusQdrant
Cloud
AWSAzureGCPKubernetesTerraform

Use cases we power

Customer 360 & CDP

Unify product, marketing and revenue data into a single source of truth, activated to every downstream tool.

Real-time Fraud & Risk

Sub-second scoring on Kafka + Flink with feature stores feeding ML models in production.

AI / RAG Data Pipelines

Document ingestion, chunking, embedding and vector indexing for enterprise GenAI applications.

IoT & Telemetry

Time-series ingestion at scale into InfluxDB, TimescaleDB and Iceberg with edge-to-cloud orchestration.

Data Mesh & Self-Service

Domain-owned data products with contracts, SLAs and discoverability via a federated catalog.

Migration & Modernization

Legacy DWH → Lakehouse migrations with zero-downtime cutovers and automated parity testing.

Ready to modernize your data platform?

Whether you're migrating to a Lakehouse, building real-time pipelines, or preparing data for GenAI — our engineers can help.